NEMS Forecast Evaluation Methodology

 

 

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 statistical matters from the American Statistical Association Committee on Energy Statistics.  The topics presented here will be discussed at EIA's fall 2006, meeting with the Committee to be held October 5 and 6, 2006.

 

 

 

Summary

 

The purpose of this note is to initiate the consideration of 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 advocated here calls for constructing statistical approximations of important energy market relationships implicit to NEMSS, e.g., crude oil supply. The approximations will be derived via regression analysis of the NEMS solutions prepared in support of the Annual Energy Outlook (AEO). The approximations are to be specified to account for important, explanatory relationships, e.g., the elasticity of crude oil supply to the price of oil. 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 crude oil supply utilizing the versions of NEMS used in support of the 1998 – 2000 AEO’s.

 

 

 

 

September 2006

 

NEMS Forecast Evaluation Methodology

 

 

Prologue. The purpose of the note below is to identify the goal of identifying differences between NEMS projections and actual data; and, given this, to partition the differences found among their major influences. The sense of the note is to identify what these influences might be and to identify a practical method for some of them for evaluating their relative roles in contributing to differences between NEMS projections and the eventual values of the variables projected. If the method outlined were implemented, there could be diagnostic value in assessing the variable/parameter sensitivities in a current version of NEMS compared to past versions. From a model management perspective, the sources of forecast differences might be used to support priorities for model component development. As it stands, since the analysis is not currently conducted, such potential benefits have not been demonstrated. As a result, as a resource allocation priority compared to other modeling issues, identifying and partitioning forecast differences in the manner proposed is problematical for the offices responsible for model maintenance and development.

An alternative perspective is that such an analysis is itself an information product that informs the community for whom the projections of energy production and consumption are prepared. In brief, that an assessment of forecast accuracy in the fashion proposed is an item of good professional practice. Accordingly, an analysis of forecast accuracy and the sources of differences between forecast and actual values might be prioritized apart from, rather than within, the process of resource allocation for model development and maintenance. While issues of method are important, perhaps the most important issue associated with this note is that of deciding if the analysis of forecast differences is a necessary activity in association with the production of the forecasts themselves.

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 and 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 via 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. In general, the size of the model and need to maintain, and then run,  non-current versions of the model over many years make this approach impracticable (at least so far for NEMS).

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 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 effects)  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.

 

Proposed Method. There are basically two methods for extracting the underlying sensitivities implicit to NEMS. One, as already noted,  is through comparative statics experiments with the model components themselves. The approach would be to solve the components, relative to a base or reference case, with each important assumption, e.g.,  the price of oil, changed, individually (with all other assumptions held constant), and compare results. The measure of sensitivity usually brought to comparative statics  results is the elasticity (the ratio of the percentage change in the variable value solved for in the model , e.g., crude oil supply, to the percentage change in the assumption, e.g., the price of oil). Forecasts of future energy production and consumption could then be compared to the values of these variables that actually occur. Using the actual values of the conditional variables and the elasticities associated with the model version at issue, the differential impact of errors in projecting the conditional variables could be assessed. Although there have been individual studies of the sensitivities of certain NEMS components using this method, it is not generally used, e.g., in the recently released Annual Energy Outlook Evaluation 2005, DOE/EIA-0640(2006), July 2006, there is no breakout of forecasting error due to errors in projecting conditional variables, or any other specific source, although the sources of error in general are initially enumerated. Extracting the sensitivities using this method would be relatively expensive.

 

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 analyses is to enable the errors in EIA forecasts to be explicitly decomposed with respect to the following influences:

            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.). Additional methods may be proposed by the ASA committee as the project is ongoing. Events influencing the actual data not accounted for by the forecasting equation will be identified and evaluated by EIA staff as appropriate, e.g., weather effects for consumption variables can be determined independent of the regressions which are based on the NEMS assumption of “normal” weather..

 

 

Example for crude oil supply. An illustration of the above procedure is provided here for projections of crude oil (and lease condensate) supply.  The market supply of crude oil is dependent upon the size of the developed resource base and the economics of oil extraction. Over time there are the additional considerations of the pace of resource development and discovery. Although (such as) projections of oil reserves and reserve additions are projected by NEMS, acquiring and processing these data was beyond the scope of preparing this initial proposal. Accordingly, for illustrative purposes, a simple specification of oil supply was brought to the NEMS solution data. For this, the explanatory variables were the world oil price (WOP) and crude oil production lagged one year. This “lagged endogenous variable” is a surrogate for such as projections of economic reserves. Accordingly, the example is intended to be illustrative and indicative of the methodology proposed, rather than a definitive example of evaluating NEMS projections of crude oil supply.

 

 

The most recent year for historical data is 2005. For this illustration, NEMS projections of crude oil production for this year were compared to actual data for the 1998-2000 versions of NEMS.3

 

 

The regression specification to be applied to NEMS solution data to represent the implicit crude oil market supply relationship is given by:

 

Qt = a + bWOPt + cQt-1,     (1)

 

where Qt = crude oil production in year t and WOPt = the world oil price in year t. As noted, the lagged endogenous variable is intended to represent the underlying trends in oil reserves; and also, the associated inter-temporal impact of changes in the WOP. For purposes of comparison, this specification was used to estimate the crude oil supply function using actual data from 1984 – 2005. A plot of the results is given below in Graphic 1. Detailed regression results are given in Appendix A.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Graphic 1: Actual and Regression Results for Crude Oil Supply

 

As summarized in the tables below, the fit of the supply function to the data was quite good (the average, absolute percent error was 1.81%); however, the statistical significance of the price elasticity was low.

 

Equation (1) was fit to the solution data for the five principal scenarios for each of the 1998-2000 AEO. The lagged endogenous variable was always significant and played by far the dominant role in the accuracy of the projection. The price effect, while small, was significant for two of the three versions of NEMS considered.

 

For the three versions of NEMS, the crude oil production projection for 2005 was high for all of them, ranging from 13.68% high in 1998 to 4.7% high in 2000. The equation was re-run for each NEMS version using actual data for both the WOP (adjusted to the monetary units of each NEMSS version) and lagged production, and then with actual data with each individually, using the NEMS projection of the other variable. The “back cast” using actual data for both explanatory variables was more accurate in for two of the three NEMS versions. Inspection revealed that the NEMS WOP projection was significantly low for all three versions of NEMS. Accordingly, use of the actual WOP with the oil supply equation made the resulting projection less inaccurate, i.e., even higher. Alternatively, simply using the correct value for lagged quantity led to a mush more accurate projection for two of the three versions of NEMS with little difference for the third.

 

Not accounted for by the analysis is the inter-temporal impact of the WOP on oil resource development. The historical time series of the WOP for the two decades before 2005 shows a flat, and significantly lower profile than the price in 2005, as given below.

 

Year           Nominal WOP    $2004 WOP     

 1984           28.54          46.02        

 1985           26.67          41.73        

 1986           16.16          24.74        

 1987           17.65          26.31        

 1988           14.08          20.29        

 1989           17.68          24.55        

 1990           21.13          28.25        

 1991           18.02          23.28        

 1992           17.75          22.41        

 1993           15.72          19.4         

 1994           15.18          18.35        

 1995           16.78          19.87        

 1996           20.31          23.61        

 1997           18.11          20.71        

 1998           11.84          13.39        

 1999           17.23          19.21        

 2000           27.53          30.04        

 2001           21.82          23.25        

 2002           23.91          25.04        

 2003           27.69          28.42        

 2004           36.07          36.07        

 2005           49.34          48.00

 

How this price profile is accounted for in the NEMS crude oil supply projection methodology might be accounted for more explicitly in a more detailed regression specification that was used here. Finally, hurricane Katrina resulted in a supply disruption sufficient to account for some the NEMS projection differences. A recent analysis concluded that the oil supply disruption due to Katrina was .647 quads, or 5.9% of measured total production.4 

 

In summary, the source of NEMS projection differences for crude oil supply is not significantly in the immediate-run impact of oil prices, although, compared to the regression results using actual data, the WOP-effect on oil supply is generally larger and more significant. The explicit accounting for projections of reserves would provide a more satisfactory partition of the basis for projection differences from actual data. Significantly, when the effect of Katrina is accounted for, actual production (10.84+.647=11.487) falls within the NEMS high and low oil supply cases for each versions of NEMS. For each version of NEMS the residual “uncertainty” not accounted for by the influences measure is small. The results of the regression analyses are provided below in Tables 1-4. Next, notes defining the basis for the table entries and elaborations of some of the points above are provided. Finally, in an Appendix, the results of the regression analyses are provided for the historical data and for the 1998-2000 versions of NEMS.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1: Domestic Crude Oil/Lease Condensate Production in 2005 (Quads)5

AEO Year

1998

1999

2000

History

Actual

10.84

10.84

10.84

10.84

NEMS High Case

 

13.31

 

12.83

 

11.71

 

NEMS Base Case

(%∆)

 

12.32

(13.68)

 

12.31

(13.56)

 

11.35

(4.7)

 

n/a

NEMS Low Case

 

10.88

 

11.54

 

10.58

 

NEMS

 Sim

(%∆)

 

12.33

(13.75)

 

12.32

(13.67)

 

11.43

(5.44)

 

11.26

(3.9)

R2

.981

.998

.991

.981

Average Absolute

% SIm Difference

 

 

.162

 

 

.163

 

 

.366

 

 

1.81

Backcast

Actual

 All

(%∆)

 

 

11.50

(6.17)

 

 

11.40

(5.12)

 

 

12.07

(11.36)

 

 

n/a

Backcast

Actual

WOP only

(%∆)

 

 

12.46

(14.95)

 

 

12.34

(13.87)

 

 

12.06

(11.23)

 

 

n/a

Backcast

Actual

Lag only

(%∆)

 

 

11.38

(4.97)

 

 

11.37

(4.92)

 

 

11.44

(5.57)

 

n/a

 

Table 2: World Oil Price (WOP) in 2005 ($/Barrel)5

AEO Year

1998

1999

2000

History

$Units

$1996

$1997

$1998

$2004

Actual

41.40

41.98

42.45

48.01

NEMS

(%∆)

20.19

(-51.1)

19.24

(-54.15)

20.49

(-51.73)

 

n/a

NEMS Sim Elasticity

(t-statistic)

 

.011

(2.24)

 

.002

(.54)

 

.054

(5.78)

 

.004

(.221)

Long Run

Elasticity

 

n/a

 

n/a

 

.527

 

.136

 

 

 

 

 

 

 

 

Table 3: Domestic Crude Oil/Lease Condensate Production in 2004 (Quads)

AEO Year

1998

1999

2000

History

Actual

11.503

11.503

11.503

11.503

NEMS

(%∆)

12.44

(8.18)

12.42

(7.94)

11.49

(-.14)

 

n/a

NEMS Sim Elasticity

(t-statistic)

 

1.02

(115)

 

1.05

(128)

 

.898

(31.2)

 

.996

(30.3)

 

 

Table 4: Sources of NEMS Base Case Projection Differences

AEO Year

1998

1999

2000

Total % Difference

+13.68

+13.56

+4.7

% Due to Price Projection

 

-1.2

 

-.20

 

-5.79

% Due to Lag Projection

 

+8.78

 

+8.75

 

-1.4

% Due to Weather

+5.9

+5.9

+5.9

% Residual

+.20

-.89

-3.41

 

 

 

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.

 

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

 

3. NEMS solutions for “early” years depend all, or in part, upon projections utilizing EIA’s short term forecasting system. Accordingly, for the illustration here, NEMS projections are assessed only when more than five years out. Since the NEMS solutions for an AEO are finalized in the fall of the year prior to publication, this makes the solutions for the 2000 AEO the most recent for assessment. Solution data of the sort proposed for processing are only available starting for the solutions prepared in support of the 1998 AEO.

 

4. In a January 5, 2006 press release the Department of the Interior’s Minerals Management Service reported that 111,633,122 barrels of oil production had been shut in due to Katrina for the period 8/26/05-1/5/06. At 5.8 million Btu’s per barrel this amounts to .647 quads. The press release is available at: http://www.mms.gov/ooc/press/2006/press0105.htm.

 

5. “NEMS Sim” refers to the projections provided by the regression equation. For this and the tables below: Actual crude oil production was from the AER 2005, Table 1.2 as posted on the EIA website on 7/27/06; the value for the actual WOP used was that quoted in the August 2006 MER for 2005 as “Landed Cost of Imported Oil;” the price index used to adjust monetary units to those used in each of the nine versions of NEMS was the “GDP Chain-Type Price Index,” as  taken from Table B-3 (Appendix B) of the  2006 Economic Report of the President. The percentage base for all differences is the actual value of the variable. The elasticities are evaluated at the means of the variables used for the forecasts, e.g., the WOP elasticity is equal to b(WOP/Q) where “b” is the estimated regression coefficient and {WOP,Q} are the means of the variables. The “long run” elasticity accounts for the impact of a change in the WOP being transmitted to future years via the lagged endogenous variable. The effect is convergent for 0 < c < 1, and multiplies the short run elasticity by (1/(1-c)). In the table, “n/a” identifies cases for c > 1 or b < 0. All of the regressions using NEMS solution data were run for data pooled from all five principal scenarios prepared for the AEO using the projected values for the years 2005-2020 inclusive. The high and low oil production cases are, respectively, from the high and low world oil price scenarios.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix Crude Oil Supply Regression Results

 

History

 

Endogenous Variable:

Table #1 Total Energy Supply and Disposition Summary (quadrillion Btu, unless otherwise noted)

  Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate

 

Exogenous Variables:

# 1) Table #1 Total Energy Supply and Disposition Summary (quadrillion Btu, unless otherwise noted)

    Supply, Disposition, and Prices: Prices (2004 dollars per unit):  World Oil Price ($ per bbl) 10/

 

# 2) Lagged Table #1 Total Energy Supply and Disposition Summary (quadrillion Btu, unless otherwise noted)

    Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     25.56688       .002101        .003739        .221085      

Variable# 2     14.74595       .970695        .996464        30.2608      

Constant                      -.00292       

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        14.36462       .341296        .980845        34.12386964681

 

Data pooled for the years  1985 to  2005 for Historical Data

 

 

Table #1 Total Energy Supply and Disposition Summary (quadrillion Btu, unless otherwise noted)

Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate

 

Year           Actual         Estimated      %abs((Est-Act)/Act)

 1985           18.992         18.38042      3.22          

 1986           18.376         18.4845       0.59          

 1987           17.675         17.88984      1.215         

 1988           17.279         17.19674      0.476         

 1989           16.117         16.8213       4.37          

 1990           15.571         15.70112      0.836         

 1991           15.701         15.16068      3.441         

 1992           15.223         15.28505      0.408         

 1993           14.494         14.81474      2.213         

 1994           14.103         14.10488      0.013         

 1995           13.887         13.72855      1.141         

 1996           13.723         13.52672      1.43          

 1997           13.658         13.36143      2.171         

 1998           13.235         13.28296      0.362         

 1999           12.451         12.88458      3.482         

 2000           12.358         12.14631      1.713         

 2001           12.282         12.04177      1.956         

 2002           12.163         11.97176      1.572         

 2003           12.026         11.86335      1.353          

 2004           11.503         11.74644      2.116         

 2005           10.84          11.26384      3.91          

Average Absolute Percent Error = 1.808953

 

 

 

 

 

 

 

 

 

 

 

 

 

AEO 1998 Simulation Results

 

Endogenous Variable:

Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

   Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate.

 

Exogenous Variables:

# 1) Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

     Supply, Disposition, and Prices: Prices (1996 dollars per unit):  World Oil Price ($ per bbl) ...

 

# 2) Lagged Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

     Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate.

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     21.09386       .006141        .011359        2.2424       

Variable# 2     11.52222       1.012341       1.022852       115.76361    

Constant                      -.390142      

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        11.40381       .055787        .998362       -81.03071063933

 

Data pooled for the years  2005 to  2020 for the solutions given below:

 

aeo98b.ran     hmac98.ran     lmac98.ran     hwop98.ran     lwop98.ran 

 

   Estimation Results for aeo98b.ran

 

Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

 Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate.

 

Year           Actual         Estimated      %(Est-Act)/Act

 2005           12.32254       12.33081      0.067         

 2006           12.20534       12.20866      0.027         

 2007           12.10194       12.09089      0.091         

 2008           11.99377       11.98713      0.055         

 2009           11.89241       11.87854      0.117         

 2010           11.78969       11.77682      0.109         

 2011           11.69125       11.67369      0.15          

 2012           11.57265       11.57496      0.02          

 2013           11.44692       11.45567      0.076         

 2014           11.25463       11.32913      0.662         

 2015           11.09442       11.13527      0.368         

 2016           10.9388        10.97394      0.321         

 2017           10.78883       10.81739      0.265         

 2018           10.65493       10.6666       0.109         

 2019           10.54534       10.53207      0.126         

 2020           10.42591       10.4224       0.034         

Average Absolute Percent Error = .1623125

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

AEO 1999 Simulation Results

 

Endogenous Variable:

Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

   Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate.

 

Exogenous Variables:

# 1) Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

     Supply, Disposition, and Prices: Prices (1997 dollars per unit):  World Oil Price ($ per bbl) ...

 

# 2) Lagged Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

     Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate.

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     21.38648       .000966        .001807        .546316      

Variable# 2     11.55995       1.038382       1.049795       127.881358   

Constant                      -.590025      

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        11.43428       .049681        .997553       -26.05387942264

 

Data pooled for the years  2005 to  2020 for the solutions given below:

 

aeo99b.ran     hmac99.ran     lmac99.ran     hwop99.ran     lwop99.ran    

 

 

Estimation Results for aeo99b.ran

 

Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

 Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate.

 

Year           Actual         Estimated      %(Est-Act)/Act

 2005           12.31041       12.32166      0.091         

 2006           12.25971       12.21235      0.386         

 2007           12.1361        12.16043      0.2           

 2008           12.02927       12.03221      0.024         

 2009           11.9313        11.9214       0.083          

 2010           11.83284       11.8198       0.11          

 2011           11.74482       11.71769      0.231         

 2012           11.6603        11.62642      0.291         

 2013           11.55284       11.53878      0.122         

 2014           11.43066       11.42729      0.03          

 2015           11.29543       11.30053      0.045         

 2016           11.1615        11.16023      0.011         

 2017           11.02854       11.02131      0.066         

 2018           10.89726       10.88341      0.127         

 2019           10.71355       10.74726      0.315         

 2020           10.50622       10.55669      0.48          

Average Absolute Percent Error = .16325

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

AEO 2000 Simulation Results

 

Endogenous Variable:

Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

   Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate.

 

Exogenous Variables:

# 1) Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

     Supply, Disposition, and Prices: Prices (1998 dollars per unit):  World Oil Price ($ per bbl) ...

 

# 2) Lagged Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

     Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate.

 

Exogenous

Variable       Mean           Coefficient    Elasticity     t-statistic   

Variable# 1     21.09438       .028584        .054042        5.77773      

Variable# 2     11.16395       .897522        .898054        31.192915    

Constant                       .53448       

 

Endogenous     Mean           SER            R-sq           LR-Multiplier 

Variable        11.15733       .068051        .990887        9.758192002185

 

Data pooled for the years  2005 to  2020 for the solutions given below:

 

aeo2k.ran      hmac2k.ran     lmac2k.ran     hwop2k.ran     lwop2k.ran   

 

 

Estimation Results for aeo2k.ran

 

Table #1 Total Energy Supply and Disposition Summary (Quadrillion Btu per Year, Unless Otherwise Noted)

 Supply, Disposition, and Prices: Production:    Crude Oil & Lease Condensate.

 

Year           Actual         Estimated      %(Est-Act)/Act

 2005           11.35001       11.42934      0.699         

 2006           11.24389       11.30978      0.586         

 2007           11.13607       11.21767      0.733         

 2008           11.04106       11.12372      0.749         

 2009           10.98465       11.04159      0.518         

 2010           10.95652       10.99378      0.34          

 2011           10.95853       10.97136      0.117         

 2012           10.96388       10.9763       0.113         

 2013           11.00002       10.98392      0.146          

 2014           11.00318       11.01949      0.148         

 2015           11.00747       11.02547      0.163         

 2016           11.11085       11.03214      0.708         

 2017           11.14443       11.12806      0.147         

 2018           11.16455       11.16102      0.032         

 2019           11.16817       11.18222      0.126         

 2020           11.12987       11.18829      0.525         

Average Absolute Percent Error = .365625