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Propagation of data error and parametric sensitivity in computable general equilibrium models. (English) Zbl 1242.91004
Summary: While computable general equilibrium (CGE) models are a well-established tool in economic analyses, it is often difficult to disentangle the effects of policies of interest from that of the assumptions made regarding the underlying calibration data and model parameters. To characterize the behavior of a CGE model of carbon output with respect to two of these assumptions, we perform a large-scale Monte Carlo experiment to examine its sensitivity to base year calibration data and elasticity of substitution parameters in the absence of a policy change. By examining a variety of output variables at different levels of economic and geographic aggregation, we assess how these forms of uncertainty impact the conclusions that can be drawn from the model simulations. We find greater sensitivity to uncertainty in the elasticity of substitution parameters than to uncertainty in the base-year data as the projection period increases. While many model simulations were conducted to generate large output samples, we find that few are required to capture the mean model response of the variables tested. However, characterizing standard errors and empirical probability distribution functions is not possible without a large number of simulations.
MSC:
91-08 Computational methods for problems pertaining to game theory, economics, and finance
62P05 Applications of statistics to actuarial sciences and financial mathematics
Software:
AMPL; Duali; PATH Solver
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[1] Abler D., Rodriguez A. G., Shortle J. (1999) Parameter uncertainty in CGE modeling of the environmental impacts of economic policies. Environmental and Resource Economics 14: 75–94 · doi:10.1023/A:1008362712759
[2] Adams P., Higgs P. (1990) Calibration of applied general equilibrium models from synthetic benchmark equilibrium data sets. Economic Record 66: 110–126 · doi:10.1111/j.1475-4932.1990.tb01712.x
[3] Babiker, M. H., Reilly, J., Mayer, M., Eckaus, R. S., Wing, I. S., & Hyman, R. C. (2001). The MIT Emissions Prediction and Policy Analysis (EPPA) model: Revisions, sensitivities, and comparisons of results. Technical Report 71, MIT Joint Program Report Series.
[4] Balistreri, E. J., McDaniel, C. A., & Wong, E. V. (2003). An estimation of U.S. industry-level capital-labor substitution. Computational Economics 0303001, EconWPA, http://ideas.repec.org/p/wpa/wuwpco/0303001.html .
[5] Berrittella M., Bigano A., Roson R., Tol R. S. J. (2006) A general equilibrium analysis of climate change impacts on tourism. Tourism Management 27(5): 913–924. doi: 10.1016/j.tourman.2005.05.002 · doi:10.1016/j.tourman.2005.05.002
[6] Berrittella M., Hoekstra A. Y., Rehdanz K., Roson R., Tol R. S. J. (2007) The economic impact of restricted water supply: A computable general equilibrium analysis. Water Research 41(8): 1799–1813. doi: 10.1016/j.watres.2007.01.010 · doi:10.1016/j.watres.2007.01.010
[7] Boehringer, C., Rutherford, T. F., & Wiegard, W. (2003). Computable general equilibrium analysis: Opening a black box. Discussion Paper 03-56, ZEW.
[8] Bosello F., Roson R., Tol R. S. J. (2006) Economy-wide estimates of the implications of climate change: Human health. Ecological Economics 58(3): 579–591. doi: 10.1016/j.ecolecon.2005.07.032 · doi:10.1016/j.ecolecon.2005.07.032
[9] Bosello F., Roson R., Tol R. S. J. (2007) Economy-wide estimates of the implications of climate change: Sea level rise. Environmental and Resource Economics 37(3): 549–571. doi: 10.1007/s10640-006-9048-5 · doi:10.1007/s10640-006-9048-5
[10] Bosello F., Roson R., Tol R. S. J. (2008) Economy-wide estimates of the implications of climate change–A rejoinder. Ecological Economics 66(1): 14–15. doi: 10.1016/j.ecolecon.2007.03.013 · doi:10.1016/j.ecolecon.2007.03.013
[11] Burniaux, J. M., & Truong, T. (2002). GTAP-E: An energy-environmental version of the GTAP model. GTAP Technical Papers 923, Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University, http://ideas.repec.org/p/gta/techpp/923.html .
[12] Clements K. W. (1980) A general equilibrium econometric model of an open economy. International Economic Review 21: 469–488 · doi:10.2307/2526192
[13] Dawkins C. (2005) Extended sensitivity analysis for applied general equilibrium models. Revista de Economia del Rosario 8: 85–111
[14] DeVuyst E. A., Preckel P. V. (1997) Sensitivity analysis revisited: A quadrature-based approach. Journal of Policy Modeling 19: 175–185 · doi:10.1016/0161-8938(95)00145-X
[15] Dirkse, S. P., & Ferris, M. C. (1995). The PATH solver: A non-monotone stabilization scheme for mixed complementarity problems. Optimization Methods and Software, 5, 123–156. ftp://ftp.cs.wisc.edu/tech-reports/reports/1993/tr1179.ps .
[16] Effron B., Tibshirani R. (1993) An introduction to the bootstrap. Chapman and Hall, New York
[17] Elliott, J., Foster, I., Judd, K., Moyer, E., & Munson, T. (2009). CIM-EARTH: Community integrated model of economic and resource trajectories for humankind. Technical Memorandum ANL/MCS-TM-307 Version 0.1, MCS Division, Argonne National Laboratory.
[18] Ferris, M. C., & Munson, T. (1999). Interfaces to PATH 3.0: Design, implementation and usage. Computational Optimization and Applications, 12, 207–227. ftp://ftp.cs.wisc.edu/math-prog/tech-reports/97-12.ps . · Zbl 1040.90549
[19] Ferris, M. C., & Munson, T. (2000). GAMS/PATH user guide: Version 4.3. Madison: Department of Computer Sciences, University of Wisconsin.
[20] Fourer R., Gay D. M., Kernighan B. W. (2003) AMPL: A modeling language for mathematical programming, 2nd edn. Brooks/Cole-Thomson Learning, Pacific Grove, CA · Zbl 0701.90062
[21] Gerlagh R., Van der Zwaan B. (2004) A sensitivity analysis of timing and costs of greenhouse gas emission reductions. Climatic Change 65(1–2): 39–71 · doi:10.1023/B:CLIM.0000037497.49722.c5
[22] Gopalakrishnan, B. N., & Walmsley, T. L. (Eds.). (2008). Global trade, assistance, and production: The GTAP 7 data base. West Lafayette: Global Trade Analysis Center, Department of Agricultural Economics, Purdue University.
[23] Harrison G. W., Jones R. C., Kimbell L. J., Wigle R. M. (1992) How robust is applied general equilibrium analysis?. Journal of Policy Modeling 15: 99–115 · doi:10.1016/0161-8938(93)90024-K
[24] Harrison G., Vinod H. (1992) The sensitivity analysis of applied general equilibrium models: Completely randomized factorial sampling designs. The Review of Economics and Statistics 74: 357–362 · doi:10.2307/2109672
[25] Hertel T., Hummels D., Ivanic M., Keeney R. (2007) How confident can we be of CGE-based assessments of free trade agreements. Economic Modelling 24(4): 611–635. doi: 10.1016/j.econmod.2006.12.002 · doi:10.1016/j.econmod.2006.12.002
[26] Jorgenson D. W. (1984) Econometric methods for applied general equilibrium analysis. In: Scarf H. E., Shoven J. B. (eds) Applied general equilibrium analysis. Cambridge University Press, Cambridge
[27] Jorgenson D. W. (1998) Growth, Vol. 1: Econometric general equilibrium modeling. MIT Press, Cambridge
[28] Jorgenson D.W., Slesnick D.T., Wilcoxen P.J. (1992) Carbon taxes and economic welfare. Brookings Papers on Economic Activity, Microeconomics, 1992: 393–441 · doi:10.2307/2534767
[29] Kendrick D. (2005) Stochastic control for economic models: Past, present and the paths ahead. Journal of Economic Dynamics and Control 29: 3–30 · Zbl 1202.93173 · doi:10.1016/j.jedc.2003.02.002
[30] Kim S. (2004) Uncertainty, political preferences, and stabilization: Stochastic control using dynamic CGE models. Computational Economics 24: 97–116 · Zbl 1094.91045 · doi:10.1023/B:CSEM.0000049438.56302.14
[31] Liu J., Arndt C., Hertel T. (2004) Parameter estimation and measures of fit in a global, general equilibrium model. Journal of Economic Integration 19: 626–649 · doi:10.11130/jei.2004.19.3.626
[32] McKitrick R. (1998) The econometric critique of computable general equilibrium modeling: The role of functional forms. Economic Modelling 15: 543–573 · doi:10.1016/S0264-9993(98)00028-5
[33] Pagan A. R., Shannon J. H. (1985) Sensitivity analysis for linearized computable general equilibrium models. In: Piggott J., Whalley J. (eds) New developments in applied general equilibrium analysis. Cambridge University Press, Cambridge
[34] Pagan A. R., Shannon J. H. (1987) How reliable are ORANI conclusions?. Economic Record 63: 33–45 · doi:10.1111/j.1475-4932.1987.tb00635.x
[35] Roberts B. M. (1994) Calibration procedure and the robustness of CGE models: Simulations with a model for Poland. Economics of Planning 27: 189–210 · doi:10.1007/BF01265332
[36] Sokolov, A., Schlosser, C., Dutkiewicz, S., Paltsev, S., Kicklighter, D., Jacoby, H., Prinn, R., Forest, C., Reilly, J., Wang, C., Felzer, B., Sarofim, M., Scott, J., Stone, P., Melillo, J., & Cohen, J. (2005). The MIT integrated global system model (IGSM) version 2: Model description and baseline evaluation. Technical Report 124, MIT Joint Program Report Series.
[37] Sokolov A. P., Stone P. H., Forest C. E., Prinn R., Sarofim M. C., Webster M., Paltsev S., Schlosser C. A., Kicklighter D., Dutkiewicz S., Reilly J., Wang C., Felzer B., Melillo J. M., Jacoby H. D. (2009) Probabilistic forecast for twenty-first-century climate based on uncertainties in emissions (without policy) and climate parameters. Journal of Climate 22(19): 5175–5204. doi: 10.1175/2009JCLI2863.1 · doi:10.1175/2009JCLI2863.1
[38] Sue Wing, I. (2004). Computable general equilibrium models and their use in economy-wide policy analysis. Technical Note 6, Joint Program on the Science and Policy of Global Change.
[39] Tol, R., & Fankhauser, S. (1998). On the representation of impact in integrated assessment models of climate change. Environmental Modeling and Assessment, 3(1), 63–74. doi: 10.1023/A:1019050503531 . http://dx.doi.org/10.1023/A:1019050503531 .
[40] Webster, M., Paltsev, S., Parsons, J., Reilly, J., & Jacoby, H. (2008). Uncertainty in greenhouse emissions and costs of atmospheric stabilization. Technical Report 165, MIT Joint Program Report Series.
[41] Wigle R. M. (1991) The Pagan–Shannon approximation: Unconditional systematic sensitivity in minutes. Empirical Economics 16: 35–49 · doi:10.1007/BF01205344
[42] Wilde M., Foster I., Iskra K., Beckman P., Zhang Z., Espinosa A., Hategan M., Clifford B., Raicu I. (2009) Parallel scripting for applications at the petascale and beyond. Computer, 42(11): 50–60 · doi:10.1109/MC.2009.365
[43] Zhao, Y., Hategan, M., Clifford, B., Foster, I., von Laszewski, G., Nefedova, V., Raicu, I., Stef-Praun, T., & Wilde, M. (2007). Swift: Fast, Reliable, Loosely Coupled Parallel Computation. In 1st IEEE international workshop on Scientific workflows, pp. 199–206.
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