A simulation model of the u s oil market
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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.

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A simulation model of the u s oil market

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
Overview

  • Motivation

  • Methodology

  • Model Description

  • Model Results

  • Issues


Research question
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
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
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
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
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

Net Imports

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
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
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
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
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

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
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

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
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
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
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
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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