1 / 25

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. Motivation Methodology Model Description Model Results Issues. Research Question.

niveditha
Télécharger la présentation

A Simulation Model of the U.S. Oil Market

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Overview • Motivation • Methodology • Model Description • Model Results • Issues

  3. 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?

  4. 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?

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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!

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. Historic Simulation Results

  20. Historic Simulation Results

  21. Historic Simulation Results

  22. Historic Simulation Results

  23. Historic Simulation Results

  24. Historic Simulation Results

  25. 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!

More Related