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### A Simulation Model of the U.S. Oil Market

Alicia K. Birky

University of Maryland School of Public Affairs

PhD Dissertation Work in Progress

November 19, 2003

Overview

- Motivation
- Methodology
- Model Description
- Model Results
- Issues

Research Question

Under what conditions can the U.S. transportation system transition from conventional petroleum while reducing carbon emissions: can development of a superior alternate technology regime enable this transition, or will it only occur as the result of a sudden disturbance?

Motivation

- The world’s total endowment of oil is fixed
- Transportation accounts for 2/3 of U.S. oil consumption
- Many analysts are predicting that half this ultimate endowment will be produced by 2020-2030
- Then production will begin to decline, they claim
- Standard economics argues that a transition to alternatives will occur via market mechanisms
- What if standard economics is wrong?
- Carbon emissions from fossil fuels are the main contributor to climate change
- Will the future fuel for transport also contribute?

Conventional Economic Analysis

- Rational agents optimize an objective function (utility or profit)
- Objective function is exogenous and stable
- Depletion is accounted for in rational expectations
- Diminishing returns result in technologies sharing the market
- Technological change is exogenously specified

Alternative Framework

- Agents are boundedly rational
- Limited cognition and resources
- Unknown or uncertain future
- Preferences evolve endogenously with the social, economic and technical environment
- Adaptive preferences and expectations
- Endogenous learning
- Positive feedbacks can lead to lock-in

Methodology

- Dynamic simulation model focusing on U.S. highway vehicles
- Agents include vehicle manufacturers, vehicle and fuel consumers, fuel feedstock producers, and fuel refiners
- Fuels include conventional oil, unconventional oil, ethanol, and hydrogen
- Positive feedbacks will be modeled
- Bias toward the status quo
- Adaptive expectations
- Evolving preferences

Oil Sector Model

U.S. OSM Boundary

- Refiners
- Input level
- Output mix
- Capacity

- Consumers

Product

Price

World Oil Price

- Personal income

Finished

Products

- Production costs
- Yields
- Product inventory

Domestic

Oil Price

Crude Oil

- Domestic Producers
- Production level
- Capacity
- Exploration
- R&D expenditures

World Oil Market

World Oil Price

- Reserve Estimates
- Production costs

Exogenous to OSM

- World oil price
- Currently only historic data is used
- Will eventually be calculated by iteration to clear the world oil market
- Product demand
- Currently represented by a simple regression model for gasoline only
- Will eventually include distillates demand by all sectors
- GDP and personal income
- Oil price, product price and sales, and vehicle price and sales will eventually “feed back” into GDP and income

Endogenous to OSM

- Domestic production
- Refinery input
- Product mix
- Gasoline and distillate proportions
- Not currently modeled
- Refinery yield
- Depends on crude quality, regulations, and technology
- A measure of production cost
- Not yet modeled
- Net imports = refinery input – domestic production
- Gasoline inventory coverage
- Gasoline price

OSM Derivation

- Monthly time-step
- Want higher resolution than the shortest planning cycle, which is quarterly
- Seasonal dynamics shape perceptions
- Time series regression models
- Autoregressive structure
- Agents base current behavior on past behavior
- OLS is biased and inefficient, but consistent
- Generally adopted as the most appropriate estimator for habit-persistence theory
- Use Cochrane-Orcutt iterative method to account for inefficiency

Historic Data 1974-2000

- EIA Monthly Energy Review
- Domestic production
- Refinery input
- Net imports
- Gasoline production
- Oil and gasoline price
- Gasoline stock
- BEA
- GDP
- Personal income
- Census Bureau - Population

Problem: GDP only available quarterly!

Domestic Production

Domestic production (prod, million bpd) is a function of:

prodt-1 Lagged production

dcRt-1 Lagged real refiner acquisition cost of domestic crude, ln(1996 ¢/bbl)

Grt-1 Lagged GDP growth rate

rest-1/prodt-1 Lagged reserve estimate/lagged total production, years

dmo dummy for month, 1 or 0, January omitted

Domestic Production Results

Source | SS df MS Number of obs = 315

---------+------------------------------ F( 16, 298) = 3951.36

Model | 10.0057954 16 .625362213 Prob > F = 0.0000

Residual | .047162965 298 .000158265 R-squared = 0.9953

---------+------------------------------ Adj R-squared = 0.9951

Total | 10.0529584 314 .032015791 Root MSE = .01258

------------------------------------------------------------------------------

lnprod | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lnprod1 | .9825311 .0071591 137.243 0.000 .9684423 .9966198

lndcR1 | .0089638 .0020314 4.413 0.000 .0049661 .0129614

Grate1 | .3545127 .2015847 1.759 0.080 -.0421972 .7512225

lnrp1 | .0001805 .0116465 0.015 0.988 -.0227393 .0231003

dxlnrp1 | .0017712 .0009053 1.957 0.051 -.0000103 .0035527

feb | .0090832 .0043204 2.102 0.036 .0005809 .0175856

mar | -.0016321 .00349 -0.468 0.640 -.0085003 .0052362

apr | -.0003315 .0037717 -0.088 0.930 -.0077539 .007091

may | -.0009574 .0036614 -0.261 0.794 -.0081629 .0062481

jun | -.0056966 .0036991 -1.540 0.125 -.0129763 .001583

jul | -.0035638 .0037124 -0.960 0.338 -.0108696 .003742

aug | .0010737 .0037337 0.288 0.774 -.0062741 .0084215

sep | .003933 .0036954 1.064 0.288 -.0033394 .0112054

oct | .0104439 .0038009 2.748 0.006 .0029639 .0179239

nov | .0017131 .0034902 0.491 0.624 -.0051555 .0085817

dec | -.0030986 .00432 -0.717 0.474 -.0116002 .0054029

_inter | .0821799 .0723027 1.137 0.257 -.0601086 .2244683

------------------------------------------------------------------------------

rho | -0.3477 0.0528 -6.581 0.000 -0.4516 -0.2437

------------------------------------------------------------------------------

Durbin-Watson statistic (original) 2.662617

Durbin-Watson statistic (transformed) 2.163513

Refinery Input

Refinery input (million bpd) as a function of:

reft-1 Lagged refinery input

invgt-1 Lagged gasoline inventory coverage (inventory/consumption, days)

ccRt-1 Lagged real refiner acquisition cost of crude, composite of domestic and import, (1996 ¢/bbl)

Irt-1 Lagged personal income growth rate

yldt-1 Lagged total refinery yield (gasoline+distillate production/input, unitless)

dmo dummy for month, 1 or 0, January omitted

Refinery Input Results

Source | SS df MS Number of obs = 316

---------+------------------------------ F( 16, 299) = 400.04

Model | 2.5610934 16 .160068337 Prob > F = 0.0000

Residual | .119638012 299 .000400127 R-squared = 0.9554

---------+------------------------------ Adj R-squared = 0.9530

Total | 2.68073141 315 .008510258 Root MSE = .02

------------------------------------------------------------------------------

lnrefine | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lnref1 | .8452734 .0216908 38.969 0.000 .8025875 .8879593

lninvg1 | -.0929428 .015881 -5.852 0.000 -.1241956 -.0616901

lnccR1 | -.0069287 .0039028 -1.775 0.077 -.014609 .0007517

Irate2 | .5123249 .2136754 2.398 0.017 .0918267 .9328231

lnrefty1 | -.2282638 .0449093 -5.083 0.000 -.3166422 -.1398854

feb | .0136456 .0062379 2.188 0.029 .0013698 .0259214

mar | .0231774 .0058262 3.978 0.000 .0117119 .0346429

apr | .0274634 .0061472 4.468 0.000 .0153661 .0395607

may | .0365577 .006004 6.089 0.000 .0247422 .0483731

jun | .0328917 .0059396 5.538 0.000 .021203 .0445804

jul | .0147628 .005988 2.465 0.014 .0029788 .0265467

aug | .0117029 .0059884 1.954 0.052 -.0000819 .0234877

sep | .002765 .0061727 0.448 0.655 -.0093824 .0149123

oct | -.014249 .0058539 -2.434 0.016 -.0257692 -.0027289

nov | .0255798 .0058506 4.372 0.000 .0140661 .0370934

dec | .0224412 .0059941 3.744 0.000 .0106452 .0342373

_inter | 1.759536 .2415983 7.283 0.000 1.284087 2.234984

------------------------------------------------------------------------------

rho | -0.1403 0.0556 -2.524 0.012 -0.2496 -0.0309

------------------------------------------------------------------------------

Durbin-Watson statistic (original) 2.252471

Durbin-Watson statistic (transformed) 2.047180

Gasoline Price

Real gasoline price (1996 ¢/gal), all grades, as a function of:

gpRt-1 Lagged price

icR Real refiner acquisition cost of imported crude, (1996 ¢/bbl)

dsh Dummy for price shocks and Gulf Wars

dcR Real refiner acquisition cost of domestic crude, (1996 ¢/bbl)

invgt-1 Lagged gasoline inventory coverage (inventory/consumption, days)

refu Refinery capacity utilization rate, percentage points

dmo dummy for month, 1 or 0, January omitted

Gasoline Price Results

Source | SS df MS Number of obs = 316

---------+------------------------------ F( 19, 296) = 392.71

Model | 2.15203438 19 .113264967 Prob > F = 0.0000

Residual | .08537115 296 .000288416 R-squared = 0.9618

---------+------------------------------ Adj R-squared = 0.9594

Total | 2.23740553 315 .007102875 Root MSE = .01698

------------------------------------------------------------------------------

lngpR | Coef. Std. Err. t P>|t| [95% Conf. Interval]

---------+--------------------------------------------------------------------

lngpR1 | .5646027 .0311939 18.100 0.000 .5032127 .6259927

lndcR | .1023198 .0206559 4.954 0.000 .0616688 .1429707

pshlndcR | -.0464461 .0321457 -1.445 0.150 -.1097092 .0168171

lnicR | .1083551 .0159321 6.801 0.000 .0770005 .1397096

pshlnicR | .0882323 .0267616 3.297 0.001 .0355653 .1408993

lnginv1 | -.0680625 .0203612 -3.343 0.001 -.1081335 -.0279915

lnrefu | .1214082 .0363829 3.337 0.001 .0498063 .1930101

pshocks | -.3167965 .1161874 -2.727 0.007 -.5454546 -.0881384

feb | .0122338 .0046646 2.623 0.009 .0030539 .0214137

mar | .0143259 .0051619 2.775 0.006 .0041672 .0244847

apr | .0236266 .0052404 4.509 0.000 .0133135 .0339397

may | .0241168 .0055134 4.374 0.000 .0132663 .0349673

jun | .0234251 .0058634 3.995 0.000 .0118858 .0349644

jul | .012525 .006056 2.068 0.039 .0006068 .0244431

aug | .010976 .0059649 1.840 0.067 -.000763 .0227151

sep | .0038817 .0058776 0.660 0.509 -.0076855 .0154488

oct | .003091 .0052662 0.587 0.558 -.0072729 .0134548

nov | -.0007631 .0048775 -0.156 0.876 -.0103621 .0088358

dec | .0004197 .0038956 0.108 0.914 -.0072468 .0080862

_inter | .9108137 .3655979 2.491 0.013 .1913132 1.630314

------------------------------------------------------------------------------

rho | 0.5931 0.0452 13.128 0.000 0.5042 0.6820

------------------------------------------------------------------------------

Durbin-Watson statistic (original) 1.157476

Durbin-Watson statistic (transformed) 1.910017

Further Work

- Resolve GDP issue for domestic production regression
- Inclusion of omitted variables to improve fit
- Environmental regulations (fuel formulation)
- Tax laws
- Weather forecasts (heating/cooling fuel demand)
- Counter-historic simulations and predictions
- Add:
- Refinery yield
- Refinery mix
- Capacity additions and retirement
- Exploration
- Move on to other sectors!

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