NEMS Forecast Evaluation Methodology (Draft)

 

 

This is a working document prepared as a job of work (DE-AP01-06EI38129.A000) on behalf of the Energy Information Administration (EIA) in order to solicit advice and comment on methods for evaluating EIA forecasts.

 

 

 

Summary

 

The purpose of the methodology proposed here is to present a methodology for assessing the accuracy of National Energy Modeling System (NEMS) projections.  NEMS is configured to project future energy product production and consumption in a fashion that accounts for the wide range of detailed circumstances that result in multi-market economic equilibria for energy products. In addition to market forces NEMS accounts for technological change and the impact of government actions and policies. The evaluation methodology presented here calls for constructing statistical approximations of important energy market relationships implicit to NEMS, e.g., residential and commercial sector energy consumption. The approximations will be derived by regression analyses of the NEMS solutions prepared in support of the Annual Energy Outlook (AEO); or, other special studies. The approximations are to be specified to account for important, explanatory relationships, e.g., the elasticity of sectoral energy consumption to the sectoral price of energy. Based on this, “differences” between NEMS projections and the actual values of the variables projected can be partitioned among general uncertainty, “errors” in projecting explanatory variables, structural changes in market behavior, and transitory influences such as the weather. An illustration of the approach is provided for residential and commercial sector demand for delivered energy, utilizing the versions of NEMS used in support of the 1998 – 2000 AEO’s.

 

 

 

 

November 2006

 

 

NEMS Forecast Evaluation Methodology

(DRAFT)

 

I. Background. In principle, an evaluation of NEMS forecast accuracy that accounts for the various sources of differences between forecast and actual values can be readily accomplished. The form of the model, in brief, involves projecting a variety of important influences on energy markets, e.g., technology and the parameters of consumer behavior. Based on the projections of these “conditional variables” the model then determines the corresponding multi-energy-market equilibria with the associated projections of energy product prices and quantities. When a forecast period actually occurs, and the actual values of the conditional variables are known, the model can be re-run using the actual values for model assumptions and the resulting projections compared to the actual prices and quantities. The impact of individual conditional variables can be determined by differentially using the original projections and the actual values, ceteris paribus. Routines within the model might also be amended to account for changes in government policies, technologies, or consumer behavior. Extra-model influences such as weather could also be accounted for in the historical period, independent of NEMS,  or using NEMS’ own routines for processing the impact of weather variables. In general, the size of the model and need to maintain, and then run,  non-current versions of the model over many years appear to make this approach impracticable. In any event, this approach has not been attempted. Do do so, among other requirements, would entail archiving and re-implementing versions of NEMS years and even decades after their current use. A multitude of problems suggest that this approach is too expensive, and perhaps not possible as a practical matter.

An alternative is to isolate the  explanatory variables at issue for each model component to those that correspond to the basic economic forces associated with the energy markets represented by NEMS. Given this, the underlying interrelationships, e.g., price elasticities of supply and demand, can be determined for a current version of the model and then retained. In general, the method would be to selectively change important assumptions, ceteris paribus, and catalogue the corresponding sensitivities. Later, these sensitivities could be applied to actual data to determine the basis for forecast differences without having to archive and re-run the corresponding version of NEMS (This was done for RSTEM in Costello (2006) Reduced Form Energy Model Elasticities from EIA’s Regional Energy Model (RSTEM)  , released 5/9/2006 as a one time report, for price and weather elasticities)  A differential accounting for the impact of differences in projected versus actual  “conditional variables” could still be approximated from the sensitivities. As before, extra-model, transitory influences on the historical values could also be accounted for. The basis for the approach is to detect the important market sensitivities implicit to NEMS’s representation of energy markets for each version of the model. This could be done for NEMS, although it would be, perhaps unreasonably, expensive to implement. Although the method proposed below is presented as an alternative, the actual conduct of a comparative statics analysis with NEMS components to verify model sensitivities should periodically be conducted.

 

II. Proposed Method. There is an alternative method for extracting the underlying sensitivities implicit to NEMS. The approach noted above would be to solve NEMS components, relative to a base or reference case, with each important assumption, e.g.,  the residential sector price of energy, changed, individually (with all other assumptions held constant), and compare results. The method proposed here as an alternative is to estimate the market sensitivities implicit to NEMS based upon the solutions prepared each year for the AEO. Among other ways, NEMS solutions are saved in a binary format and can be processed by the PC-based graphic interface Graf2000. Solution data have been saved in this format starting with those prepared for the 1998 AEO. This utility includes a regression component that enables regression analysis to be conducted using resident data; and, a data extraction routine that enables any collection of solution series to be extracted and input to other statistical procedures. Initially, the proposed method entails no additional resource requirements in terms of running NEMS or archiving versions of NEMS for use at a future time.1 Instead, the solution sets for the AEO versions of NEMS can be pooled for the projections to be evaluated at a future time.

The basic approach is to specify the underlying energy market supply and demand relationships in terms of their important, explanatory variables; and, given this, to estimate the relationships based upon NEMS solution data. The results of the estimates provide a description of the NEMS model version in terms of how energy markets are represented. The actual specifications utilized would be guided by the expertise of the EIA staff responsible for developing and maintaining individual model components. Since the solution sets can be readily archived, the actual regression analyses need not be conducted until the time that a model version is to be evaluated, although the outcomes of the regressions can have immediate diagnostic use in NEMS development.2 A demonstration of the general success of representing NEMS components via regression analysis is given in: Buck and Lady, “Approximation of Large, Computer-Based Economic Models,” presented at annual meetings of International Atlantic Economic Association on October 9, 2005 in New York City, New York. A copy of the paper can be downloaded from the link:

http://optima-com.com/buck_lady/AES_Paper.htm

 

It is proposed to configure the means of performing the regression analyses in a fashion that can be routinely conducted and maintained by EIA staff. The goal of the statistical analysis is to enable the errors in EIA forecasts to be explicitly decomposed with respect to influences such as the following:

            Transitory Influences, e.g., weather, strikes, accidents, embargoes not accounted for in the projections.

 

            Institutional Influences, e.g., changes in laws and regulations and changes in data series definitions compared to model assumptions.

 

            Structural Influences, e.g., changes in resource availability or energy use technology compared to model assumptions.*

 

            Errors in Projecting Conditional Variables, e.g., differences in the eventual values of activity drivers and other exogenous factors such as GDP and population.*

 

            Errors in Behavioral Parameters, e.g., changes in consumer price sensitivities compared to those assumed by the forecasting methodology.*

 

            Uncertainty, e.g., the residual error of the projection method.*

 

The methodology for partitioning forecast differences among (such as) the influences outlined above is as follows for the items indicated by “*”, given the availability of actual data for previously forecast values.

 

The equations derived to represent the important relationships of supply and demand are  re-run using the actual values for the explanatory variables. The actual values are substituted for the values used (or solved for) in the original projections, one explanatory variable at a time. This enables the identification of the influence of each explanatory variable separately. The equation is then re-run with all explanatory variables assigned their actual values. For this case, the residual error is due to general  forecasting "uncertainty" or other, structural changes. Structural change is the issue of whether or not the values of the coefficients in the forecasting equation have changed for the forecast period compared to the model version to which the equation had been "fit." The equation is re-estimated and the results compared to the outcome of the estimation used to approximate the characteristics of the model. One method to assess if there were significant "differences" between the original, and revised, estimate is the Chow test (Chow, Gregory, "Tests of Equality Between Sets of Coefficients in Two Linear Regressions," Econometrica, 28, (July 1960), pp. 591-605.). Events influencing the actual data not accounted for by the forecasting equation will be identified and evaluated by EIA staff as appropriate.

 

NEMS design is intended to enable the differential assessment of changes in many influences on energy consumption and production. The present methodology for evaluating NEMS projections, e.g., as presented in:

 

http://www.eia.doe.gov/oiaf/analysispaper/forecasteval/index.html),

 

assesses the accuracy of projections of seventeen aggregate variables, e.g., total energy consumption. A review of this method was presented in: Winebrake, James and Denya Sakva (2006), "An evaluation of errors in U.S. energy forecasts: 1982-2003," Energy Policy, 34, pp. 3475-3483. This article assessed the AEO forecast evaluation and found that the apparent accuracy of aggregate forecast series was somewhat misleading when the series were broken down into their components. Their examples were EIA projections of energy consumed in the U.S. These showed errors of between 1.7% to 4.8% depending upon the time horizons considered. They then showed that the components of the total really had much larger errors for the individual residential, commercial, industrial, and transportation sector consumption forecasts, with these errors off-setting, e.g., half of the forecast values could be 12% high and the other half 9% low, with the aggregate projections being just 3% high. Their conclusion was that an analysis of the components of the aggregate total was necessary to get a feeling for how well the forecast method was doing. The proposed method here takes that point one step further: When assessing the accuracy of a forecast, an assessment of the sources of error is also necessary to evaluating accuracy. Here too errors can be off-setting with the resulting forecast accuracy being somewhat misleading, e.g., a demand forecast might seem accurate; but in fact, there was a large positive error due to the projected price being significantly lower that the historical value; and, a large negative error due to underestimating the associated activity driver. The point of the method being presented here is to find a practical way to parse these individual sources of error out of a comparison of the interrelationships within NEMS with historical data. It is advocated that an assessment of forecast error in these terms is important to evaluating NEMS’ accuracy in projecting the differential impacts of individual influences on energy markets.

 

 

 

 

 

III. An Example for Residential and Commercial Sector Consumption of Delivered Energy. The example presented here is intended to be illustrative and indicative of the methodology proposed, rather than a definitive evaluation of NEMS projections of residential and commercial sector energy demand.

 

To illustrate the proposed method, solution data from the 1998-2000 AEO versions of NEMS were assembled. A simple relationship was used to represent each sector’s demand:

 

Qt = A + B(Sector Price)t + C(Driver)t + DQt-1,

 

where Qt = sectoral consumption of delivered energy in year t, (Sector Price)t = the average price of energy delivered to the sector in year t ($ per million Btu), and (Driver)t = a sectoral “activity” variable. The driver for the residential sector was millions of households and the driver for the commercial sector was billions of square feet. The lagged endogenous variable picks up secular trends in such as energy intensity, the average size of a household, and other infrastructure or behavioral characteristics.  Since the AEO hi/lo GDP cases represent shifts in energy demand, to accommodate issues of identification, the regressions were performed on data pooled from the AEO Base Case and the hi/lo WOP cases. The regressions were run on the solution data for the years 2000-2020. Generally, the fit of the regressions was exceptionally good (regression results are in Appendix C below). Below are the results for the residential sector. The regression equations were then used with “actual” data (the solution values in the AEO 2006 NEMS solutions) for projections for the years 2000-2005 and the differences compared to the original forecast values. An example is provided below for the projections of residential consumption of (delivered) energy in the year 2005 as projected by the 2000 AEO version of NEMS.

 

Model = N_ResAll_00 for Year = 20054

Residential Energy Consumption: NEMS - Actual =-.28533 (as % =-2.44)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.02821       17.24          1.0614         9.08          -.277041       -.093418      

Driver          111.3056       115.3573      -.2825         -2.42           .685312        .57847       

Lag             11.33194       11.43521      -.016          -.14            .154078        .240553      

HDD             4524           4228           .3802          3.25           .48155          n/a         

CDD             1215           1444          -.2265         -1.94           .10487          n/a         

Total                                         .9166          7.83         

Uncertainty                                  -1.2019        -10.28        

 

The NEMS projection for 2005 was .28533 quads (or 2.44%) low as made for the 2000 AEO base case. The first five rows of the table evaluate the difference in the NEMS projection associated with actual, rather than assumed, values for each of the explanatory variables. The first three are from the regression equation while the assessment of the weather using heating degree days (HDD) and cooling degree days (CDD) was made independently (see Appendix A). In each row the value assumed for the 2000 AEO is compared with the actual value in 2005. The impact, in quads, is the difference in the projected versus actual value associated with the explanatory variable. For example the average price of energy assumed for the 2000 AEO was over 24% lower than the actual price in 2005.3 As a result, given the estimated residential sector price elasticity of  -.277, the forecast value figured to be 1.0614 (9.08%) high. In contrast, the NEMS assumption for residential households was 3.5% low with an estimated impact (from the regression approximation of residential sector demand) of 2.42%. When the lag and weather effects were added in, the NEMS projection figured to be 7.83% high. But in fact it was 2.44% low. The corresponding uncertainty, or “unexplained” variation is, therefore, -10.28%.5

 

This differential analysis of forecast differences was performed for each of the years 2000-2005. A table summarizing the outcome of the analysis for each year is provided below.

 

Residential Sector Projection % Differences: Model = N_ResAll_00

Year            2000           2001           2002           2003           2004           2005         

NEMS%          -2.04           .91           -.84           -2.61          -.9            -2.44         

Ajustments%     1.88           5.1            .84            .88            4.73           7.83         

Uncertainty%   -3.92          -4.19          -1.67          -3.49          -5.64          -10.28        

 

Inspection of the table reveals that NEMS projections were generally low for each year (with 2001 the exception) while the backcast adjustments suggested that NEMS projections should have been high. A more detailed analysis would accompany a regression equation with more explanatory variables. One hint for the example used is that the “updated” price elasticity, derived by running the same regression equation for the 2006 AEO version of NEMS shows a significantly lower price elasticity. As given above, a formal analysis of structural change across NEMS versions will be conducted using the Chow Test, or related statistical procedure. A plot summarizing the comparison of the NEMS projection compared to the backcast projection is provided below.

 

In the above plot “NEMS SIM” is the projection provided by the statistical approximation of the 2000 AEO NEMS version residential sector demand function. The “Backcast” is that provided by the regression equation using actual values for the explanatory variables. As noted, the projected values tended to be low, but the impacts of errors in projecting the corresponding explanatory variables led to the estimate that the NEMS projection should have been high, i.e., the backcast projections should be low. These results imply the significance of features of the NEMS residential sector demand relationship not accounted for in the regression equation utilized for this example; and/or, other, extra-NEMS impacts upon consumption not accounted for in the illustration developed here.

 

Appendix A presents a summary of the manner of accounting for the impact of weather on actual consumption. Appendix B presents the year by year workup and summary plot as above for each of the approximations of NEMS residential and commercial sector demand for the 1998-2000 versions of NEMS. Appendix C provides the regression results for the sectoral demand approximations.

 

Notes

 

1. A fair number of NEMS scenarios are run in support of each AEO, e.g., around forty were run for the AEO2006, in order to reveal important sensitivities and uncertainties. As the use of regression analysis of solution data is developed for the purposes proposed here, some number of additional runs might be formulated to facilitate the isolation of important influences for the ultimate evaluation of NEMS projections. As noted below, the formulation of weather scenarios might facilitate an accounting for weather effects as they  cause actual variable values to deviate from forecast values.

 

2. Large changes in year to year implicit sensitivities could detect anomalies in model development.

 

3. The monetary units used for prices in the AEO lag the current period by two years. Accordingly, prices for the 2000 AEO are in $1998 while those in the 2006 AEO are in $2004. Actual prices for each AEO version are converted to the monetary units used in the contemporary AEO. The price index used to adjust monetary units to those used in each NEMS version was the “GDP Chain-Type Price Index,” as  taken from Table B-3 (Appendix B) of the  2006 Economic Report of the President.

 

4. In the automated simulations of NEMS solutions supporting the examples presented here, “N_ResAll_##” stands for the regression-based approximation of NEMS residential sector energy demand with ## ranging from 98-00 for the 1998-2000 AEO version of NEMS. “N_ComAll_##” similarly stands for the approximation of NEMS commercial sector energy demand.

 

5. (Total Impact) + Uncertainty = NEMS – Actual.

 

 

 

 

 

 

Appendix A: Derivation of the Weather Adjustment Impact Multiplier

 

Note: NEMS includes HDD and CDD explanatory variables in its representation of sectoral demand. These variables are accounted for in a somewhat different manner than used in RSTEM, the source of HDD and CDD elasticities used here. The differences in the effects as estimated by both methods has not been derived at the present time. Significantly, the petroleum elasticity for the sectoral demand, as estimated from RSTEM for total demand, appears to be low. As a result, the weather sensitivities used here may be underestimates as applied to residential and commercial sector demand.

 

Background. The derivations below present a way to apply an exogenous weather elasticity to actual data for weather and energy consumption to compute the difference, in energy, between actual consumption and that level of consumption that would have been the case for “normal” weather.

 

Let E = dQ/dW where E is the elasticity of energy demand (Q) to the  condition of the weather as measured by heating degree days (HDD) or cooling degree days (CDD). In computing E let dQ and dW be changes in Q and the weather, i.e., HDD or CDD, as decimal proportions of the base quantities being used as the percentage bases for the elasticity, e.g., for the percentage change in Q = +10%, dQ = + .1. The analysis assumes that dW, i.e., dHDD or dCDD, and E are known. The problem, given this, is to compute dQ; and, given this, apply it to the data to derive the difference in consumption due to the weather in energy (quads).

 

Computing dW and dQ. Let,

 

dW = (Actual Weather)/(Normal Weather) -1.

 

For example (AER Table 1.7) normal HDD = 4524 (based on 1971-2000 data) and actual HDD in the year 2000 were 4460. Given this,

 

dW = (4460/4524) – 1 = 98.585 – 1 = -.01415,

 

i.e., it was 1.415% warmer in 2000 as measured by HDD using “normal” HDD as the percentage base.

 

An estimate of the HDD elasticity of residential sector energy demand is .48155. Applying this estimate,

 

dQ/dW = E, dQ = E(dW) = .48155(-.01415) = -.006814 = Adjust.

 

This value is the estimate that residential sector demand in the year 2000 is .6814% lower due to a warmer fall/winter. Using normal weather demand as the percentage base,

 

Normal Q = (Actual Q)/(Adjust  + 1),

 

where (Normal Q) is an estimate of consumption had normal weather been the case.

 

The Weather Impact Multiplier. Given the above, the difference between actual and “normal weather” demand is given by,

 

Impact = (Actual Q) – (Normal Q) = (Actual Q) – ((Actual Q)/(Adjust +1)).

 

Factoring out (Actual Q) gives,

 

Impact Multiplier = (1 – (1/(Adjust +1)).

 

For the example of residential energy demand in the year 2000,

 

Impact Multiplier = (1 – (1/(Adjust +1)) = (1 – (1/.99319)) = -.00686.

 

Actual residential sector demand in 2000 was 11.176 quads. From the above analysis, this value is .00686(11.176) = .0767quads low due to the warmer weather. Accordingly, a NEMS forecast that assumed normal weather would be .0767quads high. Below are the weather impact multipliers for residential and commercial sector energy demand for the years 2000-2005 as computed by the above method. The estimates of sectoral HDD and CDD elasticities are the weighted averages (using 2005 quantities) of the weather elasticities given in: http://www.eia.doe.gov/emeu/steo/pub/pdf/elasticities.pdf.

 

 

 

 

 

Weather Data (AER Tables 1.7 and 1.8)

 

Year            HDD           %Normal         CDD           %Normal       

 2000           4460           98.585         1229           101.152      

 2001           4223           93.347         1245           102.469      

 2002           4294           94.916         1393           114.65       

 2003           4460           98.585         1290           106.173      

 2004           4290           94.828         1232           101.399      

 2005           4228           93.457         1444           118.848      

Normal          4524                          1215         

 

 

 

Residential in 2005

Fuel           Q(quads)       Weight         HDD Elas.      CDD Elas.     

Petroleum       1.585          .14094         .076           .016         

Gas             5.08           .45172         .88           -.01          

Electricity     4.581          .40734         .18            .263         

Wt. Avg. Elas.                                .48155         .10487       

 

Commercial in 2005

Fuel           Q(quads)       Weight         HDD Elas.      CDD Elas.     

Petroleum       .7899          .09564         .076           .016         

Gas             3.103          .37572         .526          -.017         

Electricity     4.366          .52864         .015           .11           

Wt. Avg. Elas.                                .21283         .05329       

 

Weather Impact Multipliers (Impact = Mult*(Actual Q))

               Residential                   Commercial    

Year           HDD            CDD            HDD            CDD           

 2000          -.00686         .00121        -.00302         .00061       

 2001          -.0331          .00258        -.01436         .00131       

 2002          -.0251          .01513        -.01094         .00775       

 2003          -.00686         .00643        -.00302         .00328       

 2004          -.02554         .00147        -.01113         .00075       

 2005          -.03253         .01938        -.01412         .00994       

 

 

 

 

 

Appendix B: Backcast Results And Workup

 

 

Residential Sector Projection % Differences: Model = N_ResAll_98

Year            2000           2001           2002           2003           2004           2005         

NEMS%           1.83           5.06           3.05           1.08           2.67           .92          

Ajustments%     4.11           6.35           3.15           2.51           5.82           8.27         

Uncertainty%   -2.28          -1.3           -.1            -1.42          -3.15          -7.35

 

 

 

Model = N_ResAll_98 for Year = 2000

Residential Energy Consumption: NEMS - Actual = .20424 (as % = 1.83)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.45476       13.44          .2043          1.83          -.20489        -.093418      

Driver          105.3386       105.189        .0075          .07            .475539        .57847       

Lag             11.2109        10.66378       .1837          1.64           .333017        .240553      

HDD             4524           4460           .0767          .69            .48155          n/a         

CDD             1215           1229          -.0135         -.12            .10487          n/a         

Total                                         .4586          4.11         

Uncertainty                                  -.2544         -2.28         

 

 

Model = N_ResAll_98 for Year = 2001

Residential Energy Consumption: NEMS - Actual = .55232 (as % = 5.06)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.46056       14.48          .4188          3.83          -.20489        -.093418      

Driver          106.4929       109.0325      -.127          -1.16           .475539        .57847       

Lag             11.38          11.17576       .0686          .63            .333017        .240553      

HDD             4524           4223           .3616          3.31           .48155          n/a         

CDD             1215           1245          -.0282         -.26            .10487          n/a         

Total                                         .6938          6.35         

Uncertainty                                  -.1415         -1.3          

 

 

Model = N_ResAll_98 for Year = 2002

Residential Energy Consumption: NEMS - Actual = .34203 (as % = 3.05)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.37254       13.33          .1986          1.77          -.20489        -.093418      

Driver          107.6285       110.472       -.1422         -1.27           .475539        .57847       

Lag             11.47643       10.92411       .1854          1.65           .333017        .240553      

HDD             4524           4294           .2814          2.51           .48155          n/a         

CDD             1215           1393          -.1696         -1.51           .10487          n/a         

Total                                         .3536          3.15         

Uncertainty                                  -.0116         -.1           

 

 

 

 

 

 

 

 

Model = N_ResAll_98 for Year = 2003

Residential Energy Consumption: NEMS - Actual = .12474 (as % = 1.08)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.38588       13.98          .3306          2.87          -.20489        -.093418      

Driver          108.7612       112.0135      -.1626         -1.41           .475539        .57847       

Lag             11.55409       11.21206       .1148          1              .333017        .240553      

HDD             4524           4460           .0789          .69            .48155          n/a         

CDD             1215           1290          -.074          -.64            .10487          n/a         

Total                                         .2877          2.51         

Uncertainty                                  -.163          -1.42         

 

 

 

Model = N_ResAll_98 for Year = 2004

Residential Energy Consumption: NEMS - Actual = .30526 (as % = 2.67)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.30915       14.89          .5352          4.68          -.20489        -.093418      

Driver          109.9131       113.6467      -.1867         -1.63           .475539        .57847       

Lag             11.63279       11.50805       .0419          .37            .333017        .240553       

HDD             4524           4290           .2921          2.55           .48155          n/a         

CDD             1215           1232          -.0168         -.15            .10487          n/a         

Total                                         .6656          5.82         

Uncertainty                                  -.3603         -3.15         

 

 

 

Model = N_ResAll_98 for Year = 2005

Residential Energy Consumption: NEMS - Actual = .10744 (as % = .92)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.31398       16.77          .9241          7.91          -.20489        -.093418      

Driver          111.0799       115.3573      -.2139         -1.83           .475539        .57847       

Lag             11.74047       11.43521       .1025          .88            .333017        .240553      

HDD             4524           4228           .3802          3.25           .48155          n/a         

CDD             1215           1444          -.2265         -1.94           .10487          n/a         

Total                                         .9664          8.27         

Uncertainty                                  -.859          -7.35         

 

 

 

 

 

Residential Sector Projection % Differences: Model = N_ResAll_99

Year            2000           2001           2002           2003           2004           2005         

NEMS%           .77            3.88           1.82          -.42            .97           -.98           

Ajustments%     3.82           6.25           3.17           2.36           5.35           8.41         

Uncertainty%   -3.05          -2.37          -1.35          -2.77          -4.37          -9.39         

 

 

 

 

 

 

 

Model = N_ResAll_99 for Year = 2000

Residential Energy Consumption: NEMS - Actual = .08649 (as % = .77)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.97353       13.66          .1601          1.43          -.24661        -.093418      

Driver          105.0142       105.189       -.0062         -.06            .344404        .57847       

Lag             11.12915       10.66378       .2101          1.88           .447881        .240553      

HDD             4524           4460           .0767          .69            .48155          n/a         

CDD             1215           1229          -.0135         -.12            .10487          n/a         

Total                                         .4271          3.82         

Uncertainty                                  -.3406         -3.05         

 

Model = N_ResAll_99 for Year = 2001

Residential Energy Consumption: NEMS - Actual = .4236 (as % = 3.88)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.957         14.72          .4113          3.77          -.24661        -.093418      

Driver          106.1805       109.0325      -.1011         -.93            .344404        .57847        

Lag             11.26225       11.17576       .039           .36            .447881        .240553      

HDD             4524           4223           .3616          3.31           .48155          n/a         

CDD             1215           1245          -.0282         -.26            .10487          n/a         

Total                                         .6826          6.25         

Uncertainty                                  -.259          -2.37         

 

 

Model = N_ResAll_99 for Year = 2002

Residential Energy Consumption: NEMS - Actual = .20367 (as % = 1.82)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.85189       13.55          .1629          1.45          -.24661        -.093418      

Driver          107.345        110.472       -.1109         -.99            .344404        .57847       

Lag             11.34771       10.92411       .1912          1.71           .447881        .240553      

HDD             4524           4294           .2814          2.51           .48155          n/a         

CDD             1215           1393          -.1696         -1.51           .10487          n/a         

Total                                         .355           3.17         

Uncertainty                                  -.1513         -1.35         

 

 

 

 

 

 

 

 

 

Model = N_ResAll_99 for Year = 2003

Residential Energy Consumption: NEMS - Actual =-.04873 (as % =-.42)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.94609       14.22          .2972          2.58          -.24661        -.093418      

Driver          108.525        112.0135      -.1237         -1.07           .344404        .57847       

Lag             11.41573       11.21206       .0919          .8             .447881        .240553      

HDD             4524           4460           .0789          .69            .48155          n/a         

CDD             1215           1290          -.074          -.64            .10487          n/a         

Total                                         .2703          2.36         

Uncertainty                                  -.319          -2.77         

 

 

Model = N_ResAll_99 for Year = 2004

Residential Energy Consumption: NEMS - Actual = .11147 (as % = .97)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.00933       15.14          .4971          4.35          -.24661        -.093418      

Driver          109.7295       113.6467      -.1389         -1.21           .344404        .57847       

Lag             11.45932       11.50805      -.022          -.19            .447881        .240553      

HDD             4524           4290           .2921          2.55           .48155          n/a         

CDD             1215           1232          -.0168         -.15            .10487          n/a         

Total                                         .6114          5.35         

Uncertainty                                  -.4999         -4.37         

 

 

Model = N_ResAll_99 for Year = 2005

Residential Energy Consumption: NEMS - Actual =-.11463 (as % =-.98)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.04349       17.05          .9347          8             -.24661        -.093418      

Driver          110.9643       115.3573      -.1558         -1.33           .344404        .57847        

Lag             11.54668       11.43521       .0503          .43            .447881        .240553      

HDD             4524           4228           .3802          3.25           .48155          n/a         

CDD             1215           1444          -.2265         -1.94           .10487          n/a         

Total                                         .9829          8.41         

Uncertainty                                  -1.0975        -9.39         

 

 

 

 

 

 

 

 

 

Residential Sector Projection % Differences: Model = N_ResAll_00

Year            2000           2001           2002           2003           2004           2005         

NEMS%          -2.04           .91           -.84           -2.61          -.9            -2.44         

Ajustments%     1.88           5.1            .84            .88            4.73           7.83         

Uncertainty%   -3.92          -4.19          -1.67          -3.49          -5.64          -10.28        

 

 

 

 

 

 

 

 

Model = N_ResAll_00 for Year = 2000

Residential Energy Consumption: NEMS - Actual =-.22746 (as % =-2.04)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.30314       13.81          .1277          1.14          -.277041       -.093418      

Driver          105.3671       105.189        .0124          .11            .685312        .57847       

Lag             10.70899       10.66378       .007           .06            .154078        .240553      

HDD             4524           4460           .0767          .69            .48155          n/a         

CDD             1215           1229          -.0135         -.12            .10487          n/a         

Total                                         .2102          1.88          

Uncertainty                                  -.4377         -3.92         

 

 

Model = N_ResAll_00 for Year = 2001

Residential Energy Consumption: NEMS - Actual = .099 (as % = .91)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.17413       14.89          .4324          3.96          -.277041       -.093418      

Driver          106.539        109.0325      -.1738         -1.59           .685312        .57847       

Lag             10.9483        11.17576      -.0353         -.32            .154078        .240553      

HDD             4524           4223           .3616          3.31           .48155          n/a         

CDD             1215           1245          -.0282         -.26            .10487          n/a         

Total                                         .5567          5.1          

Uncertainty                                  -.4577         -4.19         

 

Model = N_ResAll_00 for Year = 2002

Residential Energy Consumption: NEMS - Actual =-.09372 (as % =-.84)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.06647       13.7           .1597          1.42          -.277041       -.093418      

Driver          107.7027       110.472       -.1931         -1.72           .685312        .57847       

Lag             11.02311       10.92411       .0154          .14            .154078        .240553      

HDD             4524           4294           .2814          2.51           .48155          n/a         

CDD             1215           1393          -.1696         -1.51           .10487          n/a         

Total                                         .0938          .84           

Uncertainty                                  -.1875         -1.67         

 

 

 

 

 

 

 

 

Model = N_ResAll_00 for Year = 2003

Residential Energy Consumption: NEMS - Actual =-.30046 (as % =-2.61)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.06551       14.37          .3287          2.86          -.277041       -.093418      

Driver          108.8805       112.0135      -.2184         -1.9            .685312        .57847       

Lag             11.11834       11.21206      -.0146         -.13            .154078        .240553      

HDD             4524           4460           .0789          .69            .48155          n/a         

CDD             1215           1290          -.074          -.64            .10487          n/a         

Total                                         .1006          .88          

Uncertainty                                  -.4011         -3.49         

 

 

Model = N_ResAll_00 for Year = 2004

Residential Energy Consumption: NEMS - Actual =-.10327 (as % =-.9)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.08315       15.31          .5612          4.91          -.277041       -.093418      

Driver          110.0828       113.6467      -.2485         -2.17           .685312        .57847       

Lag             11.20759       11.50805      -.0467         -.41            .154078        .240553      

HDD             4524           4290           .2921          2.55           .48155          n/a         

CDD             1215           1232          -.0168         -.15            .10487          n/a         

Total                                         .5412          4.73          

Uncertainty                                  -.6445         -5.64         

 

Model = N_ResAll_00 for Year = 2005

Residential Energy Consumption: NEMS - Actual =-.28533 (as % =-2.44)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.02821       17.24          1.0614         9.08          -.277041       -.093418      

Driver          111.3056       115.3573      -.2825         -2.42           .685312        .57847       

Lag             11.33194       11.43521      -.016          -.14            .154078        .240553      

HDD             4524           4228           .3802          3.25           .48155          n/a         

CDD             1215           1444          -.2265         -1.94           .10487          n/a         

Total                                         .9166          7.83         

Uncertainty                                  -1.2019        -10.28        

 

 

 

 

 

 

 

 

 

Commercial Sector Projection % Differences: Model = N_ComAll_98

Year            2000           2001           2002           2003           2004           2005         

NEMS%          -4.28          -2.97          -3.49          -3.59          -1.48          -2.7          

Adjustments%    4.98           4.77           3.02           2.19           3.21           4.05         

Uncertainty%   -9.26          -7.74          -6.51          -5.78          -4.7           -6.74         

 

 

 

 

 

 

 

Model = N_ComAll_98 for year = 2000

Commercial Energy Consumption: NEMS - Actual =-.348716 (as % =-4.28)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.3309        13.18          .0395          .49           -.063328       -.132454       

Driver          74.89623       68.70575       .364           4.46           .565757        .318224      

Lag             7.722946       7.76739       -.0168         -.21            .374241        .649403      

HDD             4524           4460           .0246          .3             .21283          n/a         

CDD             1215           1229          -.005          -.06            .05329          n/a         

Total                                         .4063          4.98         

Uncertainty                                  -.755          -9.26         

 

 

Model = N_ComAll_98 for year = 2001

Commercial Energy Consumption: NEMS - Actual =-.241118 (as % =-2.97)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.20863       14.43          .1035          1.27          -.063328       -.132454      

Driver          75.71932       70.45488       .3095          3.81           .565757        .318224      

Lag             7.803784       8.1525        -.1315         -1.62           .374241        .649403      

HDD             4524           4223           .1166          1.44           .21283          n/a         

CDD             1215           1245          -.0106         -.13            .05329          n/a         

Total                                         .3875          4.77         

Uncertainty                                  -.6286         -7.74         

 

 

Model = N_ComAll_98 for year = 2002

Commercial Energy Consumption: NEMS - Actual =-.287438 (as % =-3.49)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.1045        13.38          .0594          .72           -.063328       -.132454       

Driver          76.51044       72.17918       .2547          3.09           .565757        .318224      

Lag             7.880362       8.12148       -.0909         -1.1            .374241        .649403      

HDD             4524           4294           .0902          1.09           .21283          n/a         

CDD             1215           1393          -.0639         -.78            .05329          n/a         

Total                                         .2495          3.02         

Uncertainty                                  -.5369         -6.51         

 

 

 

 

 

 

 

 

Model = N_ComAll_98 for year = 2003

Commercial Energy Consumption: NEMS - Actual =-.299084 (as % =-3.59)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.05031       13.72          .0778          .93           -.063328       -.132454      

Driver          77.33833       73.67056       .2157          2.59           .565757        .318224      

Lag             7.957394       8.244832      -.1084         -1.3            .374241        .649403      

HDD             4524           4460           .0252          .3             .21283          n/a         

CDD             1215           1290          -.0273         -.33            .05329          n/a         

Total                                         .1829          2.19         

Uncertainty                                  -.482          -5.78         

 

 

Model = N_ComAll_98 for year = 2004

Commercial Energy Consumption: NEMS - Actual =-.122073 (as % =-1.48)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           11.94473       14.24          .1069          1.3           -.063328       -.132454      

Driver          78.19029       75.03583       .1855          2.25           .565757        .318224      

Lag             8.03783        8.336914      -.1128         -1.37           .374241        .649403      

HDD             4524           4290           .0918          1.11           .21283          n/a         

CDD             1215           1232          -.0062         -.08            .05329          n/a         

Total                                         .2652          3.21         

Uncertainty                                  -.3873         -4.7          

 

Model = N_ComAll_98 for year = 2005

Commercial Energy Consumption: NEMS - Actual =-.2273 (as % =-2.7)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           11.83853       15.85          .1868          2.22          -.063328       -.132454      

Driver          79.01656       76.20448       .1654          1.96           .565757        .318224      

Lag             8.122841       8.244914      -.046          -.55            .374241        .649403      

HDD             4524           4228           .1191          1.41           .21283          n/a         

CDD             1215           1444          -.0838         -.99            .05329          n/a         

Total                                         .3415          4.05         

Uncertainty                                  -.5688         -6.74         

 

 

 

 

 

 

 

 

 

 

Commercial Sector Projection % Differences: Model = N_ComAll_99

Year            2000           2001           2002           2003           2004           2005         

NEMS%          -2.84          -1.41          -1.82          -2.02           0             -1.31         

Adjustments%   -1.72          -1.04          -3.27          -3.84          -2.57           .14          

Uncertainty%   -1.11          -.36            1.45           1.81           2.57          -1.45         

 

 

 

 

 

 

 

Model = N_ComAll_99 for year = 2000

Commercial Energy Consumption: NEMS - Actual =-.231366 (as % =-2.84)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.57405       13.4           .104           1.28          -.172156       -.132454      

Driver          62.89052       68.70575      -.2929         -3.59           .397353        .318224      

Lag             7.820432       7.76739        .0287          .35            .536537        .649403      

HDD             4524           4460           .0246          .3             .21283          n/a         

CDD             1215           1229          -.005          -.06            .05329          n/a         

Total                                        -.1406         -1.72         

Uncertainty                                  -.0908         -1.11         

 

 

Model = N_ComAll_99 for year = 2001

Commercial Energy Consumption: NEMS - Actual =-.114683 (as % =-1.41)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.45059       14.67          .2794          3.44          -.172156       -.132454      

Driver          63.5958        70.45488      -.3455         -4.25           .397353        .318224      

Lag             7.921134       8.1525        -.1252         -1.54           .536537        .649403      

HDD             4524           4223           .1166          1.44           .21283          n/a         

CDD             1215           1245          -.0106         -.13            .05329          n/a         

Total                                        -.0853         -1.04         

Uncertainty                                  -.0294         -.36          

 

 

Model = N_ComAll_99 for year = 2002

Commercial Energy Consumption: NEMS - Actual =-.149878 (as % =-1.82)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.28535       13.6           .1655          2.01          -.172156       -.132454      

Driver          64.25379       72.17918      -.3992         -4.84           .397353        .318224      

Lag             8.006797       8.12148       -.0621         -.75            .536537        .649403      

HDD             4524           4294           .0902          1.09           .21283          n/a         

CDD             1215           1393          -.0639         -.78            .05329          n/a         

Total                                        -.2695         -3.27         

Uncertainty                                   .1196          1.45         

 

 

 

 

 

 

 

 

Model = N_ComAll_99 for year = 2003

Commercial Energy Consumption: NEMS - Actual =-.168493 (as % =-2.02)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.33296       13.95          .2036          2.44          -.172156       -.132454      

Driver          64.93594       73.67056      -.4399         -5.28           .397353        .318224      

Lag             8.094954       8.244832      -.0811         -.97            .536537        .649403      

HDD             4524           4460           .0252          .3             .21283          n/a         

CDD             1215           1290          -.0273         -.33            .05329          n/a         

Total                                        -.3196         -3.84         

Uncertainty                                   .1511          1.81         

 

 

Model = N_ComAll_99 for year = 2004

Commercial Energy Consumption: NEMS - Actual = .000088 (as % = 0)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.34807       14.48          .2684          3.26          -.172156       -.132454      

Driver          65.61822       75.03583      -.4743         -5.75           .397353        .318224      

Lag             8.168421       8.336914      -.0912         -1.11           .536537        .649403      

HDD             4524           4290           .0918          1.11           .21283          n/a         

CDD             1215           1232          -.0062         -.08            .05329          n/a         

Total                                        -.2115         -2.57         

Uncertainty                                   .2116          2.57         

 

 

Model = N_ComAll_99 for year = 2005

Commercial Energy Consumption: NEMS - Actual =-.110758 (as % =-1.31)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.33938       16.12          .476           5.64          -.172156       -.132454      

Driver          66.28476       76.20448      -.4996         -5.92           .397353        .318224      

Lag             8.245002       8.244914       0              0              .536537        .649403      

HDD             4524           4228           .1191          1.41           .21283          n/a         

CDD             1215           1444          -.0838         -.99            .05329          n/a         

Total                                         .0117          .14          

Uncertainty                                  -.1225         -1.45         

 

 

 

 

 

 

 

 

Commercial Sector Projection % Differences: Model = N_ComAll_00

Year            2000           2001           2002           2003           2004           2005         

NEMS%          -4.16          -2.41          -2.67          -2.8           -.72           -1.8          

Adjustments%   -1.94          -2.49          -3.21          -3.71          -2.59          -.57          

Uncertainty%   -2.22           .09            .54            .9             1.87          -1.24         

 

 

 

 

 

 

 

 

Model = N_ComAll_00 for year = 2000

Commercial Energy Consumption: NEMS - Actual =-.33897 (as % =-4.16)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           13.08415       13.55          .0281          .35           -.085136       -.132454      

Driver          63.34077       68.70575      -.166          -2.04           .249884        .318224      

Lag             7.710082       7.76739       -.0397         -.49            .686408        .649403      

HDD             4524           4460           .0246          .3             .21283          n/a         

CDD             1215           1229          -.005          -.06            .05329          n/a         

Total                                        -.158          -1.94         

Uncertainty                                  -.181          -2.22         

 

 

Model = N_ComAll_00 for year = 2001

Commercial Energy Consumption: NEMS - Actual =-.195572 (as % =-2.41)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.85606       14.83          .1192          1.47          -.085136       -.132454      

Driver          64.20343       70.45488      -.1935         -2.38           .249884        .318224      

Lag             7.81353        8.1525        -.2346         -2.89           .686408        .649403      

HDD             4524           4223           .1166          1.44           .21283          n/a         

CDD             1215           1245          -.0106         -.13            .05329          n/a         

Total                                        -.2029         -2.49         

Uncertainty                                   .0073          .09          

 

Model = N_ComAll_00 for year = 2002

Commercial Energy Consumption: NEMS - Actual =-.219833 (as % =-2.67)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.60261       13.75          .0693          .84           -.085136       -.132454      

Driver          64.93169       72.17918      -.2243         -2.72           .249884        .318224      

Lag             7.925908       8.12148       -.1354         -1.64           .686408        .649403      

HDD             4524           4294           .0902          1.09           .21283          n/a         

CDD             1215           1393          -.0639         -.78            .05329          n/a         

Total                                        -.2641         -3.21         

Uncertainty                                   .0443          .54          

 

 

 

 

 

 

 

 

 

Model = N_ComAll_00 for year = 2003

Commercial Energy Consumption: NEMS - Actual =-.233694 (as % =-2.8)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.52698       14.1           .095           1.14          -.085136       -.132454      

Driver          65.62161       73.67056      -.2491         -2.99           .249884        .318224      

Lag             8.024999       8.244832      -.1522         -1.83           .686408        .649403      

HDD             4524           4460           .0252          .3             .21283          n/a         

CDD             1215           1290          -.0273         -.33            .05329          n/a         

Total                                        -.3085         -3.71         

Uncertainty                                   .0748          .9           

 

 

 

Model = N_ComAll_00 for year = 2004

Commercial Energy Consumption: NEMS - Actual =-.05913 (as % =-.72)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.46017       14.64          .1316          1.6           -.085136       -.132454      

Driver          66.34257       75.03583      -.269          -3.26           .249884        .318224      

Lag             8.10322        8.336914      -.1618         -1.96           .686408        .649403      

HDD             4524           4290           .0918          1.11           .21283          n/a         

CDD             1215           1232          -.0062         -.08            .05329          n/a         

Total                                        -.2136         -2.59         

Uncertainty                                   .1545          1.87         

 

 

Model = N_ComAll_00 for year = 2005

Commercial Energy Consumption: NEMS - Actual =-.151769 (as % =-1.8)

Source          NEMS           Actual         Impact         Percent        Elasticity     Updated Elas 

Price           12.3208        16.29          .2396          2.84          -.085136       -.132454      

Driver          67.10787       76.20448      -.2815         -3.34           .249884        .318224      

Lag             8.185784       8.244914      -.0409         -.49            .686408        .649403      

HDD             4524           4228           .1191          1.41           .21283          n/a         

CDD             1215           1444          -.0838         -.99            .05329          n/a         

Total                                        -.0475         -.57          

Uncertainty                                  -.1043         -1.24         

 

 

 

 

 

 

 

Appendix C: Regression Results

 

Endogenous Variable:

Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

   Sector and Source:  Residential:      Delivered Energy...........

 

Exogenous Variables:

# 1) Table #3 Energy Prices by Sector and Source (1996 Dollars per Million Btu)

     Sector and Source:  Residential....................:

 

# 2) Table #4 Residential Sector Key Indicators and Consumption (Quadrillion Btu per year, Unless otherwise noted)

     Key Indicators and Consumption:  Households (millions):      Total......................

 

# 3) Lagged Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

     Sector and Source:  Residential:      Delivered Energy...........

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     12.16087      -.20739        -.20489        -5.242288     

Variable# 2     117.0589       .050005        .475539        5.686485     

Variable# 3     12.20893       .335754        .333017        2.896522     

Constant                       4.878567     

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        12.30925       .030533        .997571        1.505466348310

 

Data pooled for the years  2000 to  2020 for the solutions given below:

 

aeo98b.ran     hwop98.ran     lwop98.ran    

 

 

 

 

 

 

 

 

 

 

 

Endogenous Variable:

Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

   Sector and Source:  Residential:      Delivered Energy...........

 

Exogenous Variables:

# 1) Table #3 Energy Prices by Sector and Source (1997 Dollars per Million Btu)

     Sector and Source:  Residential....................:

 

# 2) Table #4 Residential Sector Key Indicators and Consumption (Quadrillion Btu per year, Unless otherwise noted)

     Key Indicators and Consumption:  Households (millions):      Total......................

 

# 3) Lagged Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

     Sector and Source:  Residential:      Delivered Energy...........

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     12.78631      -.233292       -.24661        -6.478882     

Variable# 2     117.4799       .03546         .344404        6.662139     

Variable# 3     12.0023        .45137         .447881        5.39698      

Constant                       5.49542      

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        12.09579       .024701        .998236        1.822722053114

 

Data pooled for the years  2000 to  2020 for the solutions given below:

 

aeo99b.ran     hwop99.ran     lwop99.ran    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Endogenous Variable:

Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

   Sector and Source:  Residential:      Delivered Energy...........

 

Exogenous Variables:

# 1) Table #3 Energy Prices by Sector and Source (1998 Dollars per Million Btu)

     Sector and Source:  Residential....................:

 

# 2) Table #4 Residential Sector Key Indicators and Consumption (Quadrillion Btu per year, Unless otherwise noted)

     Key Indicators and Consumption:  Households (millions):      Total......................

 

# 3) Lagged Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

     Sector and Source:  Residential:      Delivered Energy...........

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     13.06701      -.252014       -.277041       -4.669263     

Variable# 2     116.8374       .069721        .685312        5.080185     

Variable# 3     11.78577       .155396        .154078        .95813       

Constant                       5.202166     

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        11.88658       .031538        .997149        1.183986815122

 

Data pooled for the years  2000 to  2020 for the solutions given below:

 

aeo2k.ran      hwop2k.ran     lwop2k.ran    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Endogenous Variable:

Table #2 Energy Consumption by Sector and Source (quadrillion Btu, unless otherwise noted)

  Sector and Source:  Residential:      Delivered Energy

 

Exogenous Variables:

# 1) Table #3 Energy Prices by Sector and Source (2004 dollars per million Btu)

    Sector and Source:  Residential:

 

# 2) Table #4 Residential Sector Key Indicators and Consumption (quadrillion Btu)

    Key Indicators and Consumption:  Households (millions):      Total 

 

# 3) Lagged Table #2 Energy Consumption by Sector and Source (quadrillion Btu, unless otherwise noted)

    Sector and Source:  Residential:      Delivered Energy

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     17.20498      -.066236       -.093418       -5.26068      

Variable# 2     122.5325       .05759         .57847         5.694203     

Variable# 3     12.07313       .243057        .240553        1.942199     

Constant                       3.347288     

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        12.19881       .110425        .977698        1.321103438435

 

Data pooled for the years  2000 to  2020 for the solutions given below:

 

aeo2006.1119a.rlp2006.1201a.rahp2006.1130a.ra

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Endogenous Variable:

Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

   Sector and Source:  Commercial:      Delivered Energy...........

 

Exogenous Variables:

# 1) Table #3 Energy Prices by Sector and Source (1996 Dollars per Million Btu)

     Sector and Source:  Commercial.....................:

 

# 2) Table #5 Commercial Sector Key Indicators and Consumption (Quadrillion Btu per year, Unless otherwise noted)

     Key Indicators and Consumption:  Total Floorspace(bill. sq. ft.):      Total......................

 

# 3) Lagged Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

     Sector and Source:  Commercial:      Delivered Energy...........

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     11.60786      -.046574       -.063328       -12.408086    

Variable# 2     82.13966       .0588          .565757        28.645436    

Variable# 3     8.4741         .377014        .374241        18.558989    

Constant                       1.052853     

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        8.536895       .003931        .999913        1.605172507889

 

Data pooled for the years  2000 to  2020 for the solutions given below:

 

aeo98b.ran     hwop98.ran     lwop98.ran    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Endogenous Variable:

Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

   Sector and Source:  Commercial:      Delivered Energy...........

 

Exogenous Variables:

# 1) Table #3 Energy Prices by Sector and Source (1997 Dollars per Million Btu)

     Sector and Source:  Commercial.....................:

 

# 2) Table #5 Commercial Sector Key Indicators and Consumption (Quadrillion Btu per year, Unless otherwise noted)

     Key Indicators and Consumption:  Total Floorspace(bill. sq. ft.):      Total......................

 

# 3) Lagged Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

     Sector and Source:  Commercial:      Delivered Energy...........

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     11.94725      -.125904       -.172156       -10.667412    

Variable# 2     68.93118       .050367        .397353        14.026527    

Variable# 3     8.663424       .541122        .536537        17.853183    

Constant                       2.081833     

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        8.737453       .008466        .999703        2.179228465953

 

Data pooled for the years  2000 to  2020 for the solutions given below:

 

aeo99b.ran     hwop99.ran     lwop99.ran    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Endogenous Variable:

Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

   Sector and Source:  Commercial:      Delivered Energy...........

 

Exogenous Variables:

# 1) Table #3 Energy Prices by Sector and Source (1998 Dollars per Million Btu)

     Sector and Source:  Commercial.....................:

 

# 2) Table #5 Commercial Sector Key Indicators and Consumption (Quadrillion Btu per year, Unless otherwise noted)

     Key Indicators and Consumption:  Total Floorspace(bill. sq. ft.):      Total......................

 

# 3) Lagged Table #2 Energy Consumption by Sector and Source (Quadrillion Btu per Year, Unless Otherwise Noted)

     Sector and Source:  Commercial:      Delivered Energy...........

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     12.22253      -.060376       -.085136       -10.431859    

Variable# 2     69.99389       .030945        .249884        10.613828    

Variable# 3     8.595922       .692154        .686408        30.985085    

Constant                       1.290166     

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        8.667882       .01009         .999555        3.248377435470

 

Data pooled for the years  2000 to  2020 for the solutions given below:

 

aeo2k.ran      hwop2k.ran     lwop2k.ran    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Endogenous Variable:

Table #2 Energy Consumption by Sector and Source (quadrillion Btu, unless otherwise noted)

  Sector and Source:  Commercial:      Delivered Energy          

 

Exogenous Variables:

# 1) Table #3 Energy Prices by Sector and Source (2004 dollars per million Btu)

    Sector and Source:  Commercial:

 

# 2) Table #5 Commercial Sector Key Indicators and Consumption (quadrillion Btu)

    Key Indicators and Consumption:  Total Floorspace (billion square Feet: Total

 

# 3) Lagged Table #2 Energy Consumption by Sector and Source (quadrillion Btu, unless otherwise noted)

    Sector and Source:  Commercial:      Delivered Energy          

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     16.49366      -.073351       -.132454       -5.652417     

Variable# 2     82.44836       .035254        .318224        4.778979     

Variable# 3     8.997299       .659264        .649403        8.872552     

Constant                       1.505515     

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        9.133919       .085399        .990365        2.934823441021

 

Data pooled for the years  2000 to  2020 for the solutions given below:

 

aeo2006.1119a.rlp2006.1201a.rahp2006.1130a.ra