Causal misspecifications in econometric models

Itkonen, Juha (2011): Causal misspecifications in econometric models. Published in: HECER Discussion Paper No. No 327

Abstract

We bridge together the graph-theoretic and the econometric approach for defining causality in statistical models to consider model misspecification problems. By presenting a solution to disagreements between the existing frameworks, we build a causal framework that allows us to express causal implications of econometric model specifications. This allows us to reveal possible inconsistencies in models used for policy analysis. In particular, we show how a common practice of doing policy analysis with vector error-correction models fails. As an example, we apply these concepts to discover fundamental flaws in a resent strand of literature estimating the carbon Kuznetz curve, which postulates that carbon dioxide emissions initially increase with economic growth but that the relationship is eventually reversed. Due to a causal misspecification, the compatibility between climate and development policy goals is overstated.

• Item Type: MPRA Paper
• Language: English
• Keywords: Causality, Policy evaluations, Energy consumption, Carbon dioxide emissions, Economic growth, Environmental Kuznets curve.
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C50 - General; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q54 - Climate ; Natural Disasters and Their Management ; Global Warming; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q43 - Energy and the Macroeconomy

References

Aldrich, J., 1989. Autonomy. Oxford Economic Papers 41, pp. 15–34.

Ang, J.B., 2007. CO2 emissions, energy consumption, and output in France. Energy Policy 35, 4772–4778.

Angrist, J.D., Krueger, A.B., 1999. Chapter 23 empirical strategies in labor economics, Elsevier. volume 3, Part 1 of Handbook of Labor Economics, pp. 1277–1366.

Apergis, N., Payne, J.E., 2009. CO2 emissions, energy usage, and output in Central America. Energy Policy 37, 3282–3286.

Apergis, N., Payne, J.E., 2010. The emissions, energy consumption, and growth nexus: Evidence from the Commonwealth of Independent States. Energy Policy 38, 650–655.

Basmann, R.L., 1963a. The causal interpretation of non-triangular systems of economic relations. Econometrica 31, pp. 439–448.

Basmann, R.L., 1963b. On the causal interpretation of non-triangular systems of economic relations: A rejoinder. Econometrica 31, pp. 451–453.

Boden, T., Marland, G., Andres, R., 2009. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A.

Dash, D., Druzdzel, M.J., 2008. A note on the correctness of the causal ordering algorithm. Artificial Intelligence 172, 1800–1808.

Demetrescu, M., Lütkepohl, H., Saikkonen, P., 2009. Testing for the cointegrating rank of a vector autoregressive process with uncertain deterministic trend term. Econometrics Journal 12, 414–435.

Druzdzel, M., Simon, H., 1993. Causality in bayesian belief networks, in: In Proceedings of the Ninth Annual Conference on Uncertainty in Artificial Intelligence (UAI–93, Morgan Kaufmann Publishers, Inc. pp. 3–11.

Engle, R.F., Granger, C.W.J., 1987. Co-integration and error correction: Representation, estimation, and testing. Econometrica 55, pp. 251–276.

Engle, R.F., Granger, C.W.J., Hallman, J.J., 1989. Merging short-and longrun forecasts: An application of seasonal cointegration to monthly electricity sales forecasting. Journal of Econometrics 40, 45–62.

Granger, C.W.J., 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–38.

Haavelmo, T., 1943. The statistical implications of a system of simultaneous equations. Econometrica 11, pp. 1–12.

Haavelmo, T., 1944. The probability approach in econometrics. Econometrica 12, pp. iii–vi+1–115.

Halicioglu, F., 2009. An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 37, 1156– 1164.

Hamilton, J.D., 1994. Time Series Analysis. Princeton University Press. 1 edition.

Heckman, J.J., 2005. The scientific model of causality. Sociological Methodology 35, 1–97.

Heckman, J.J., 2008. Econometric causality. International Statistical Review 76, 1–27.

Heckman, J.J., 2010. Building bridges between structural and program evaluation approaches to evaluating policy. Journal of Economic Literature 48, 356–98.

Heckman, J.J., Vytlacil, E.J., 2007. Chapter 70 econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation, Elsevier. volume 6, Part 2 of Handbook of Econometrics, pp. 4779–4874.

Holland, P.W., 1986. Statistics and causal inference. Journal of the American Statistical Association 81, pp. 945–960.

Hoover, K.D., 2003. Review: Causality by Judea Pearl. The Economic Journal 113, pp. F411–F413.

Hoover, K.D., 2008. causality in economics and econometrics, in: Durlauf, S.N., Blume, L.E. (Eds.), The New Palgrave Dictionary of Economics. Palgrave Macmillan, Basingstoke.

Imbens, G.W., Wooldridge, J.M., 2009. Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47, 5–86.

Iwata, H., Okada, K., Samreth, S., 2010. Empirical study on the environmental Kuznets curve for CO2 in France: The role of nuclear energy. Energy Policy 38, 4057–4063.

Jalil, A., Feridun, M., 2011. The impact of growth, energy and financial development on the environment in China: A cointegration analysis. Energy Economics 33, 284–291.

Jalil, A., Mahmud, S.F., 2009. Environment Kuznets curve for CO2 emissions: A cointegration analysis for China. Energy Policy 37, 5167–5172.

Johansen, S., 1988. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12, 231–254.

Johansen, S., 1995. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press.

Kaufmann, R.K., 1992. A biophysical analysis of the energy/real GDP ratio: implications for substitution and technical change. Ecological Economics 6, 35–56.

Leamer, E.E., 1985. Vector autoregressions for causal inference? Carnegie- Rochester Conference Series on Public Policy 22, 255–304.

Leroy, S.F., 2001. A review of Judea Pearl’s Causality. Journal of Economic Methodology 9, 100–103.

Lütkepohl, H., 2005. New Introduction to Multiple Time Series Analysis. Springer.

Marschak, J., 1950. Statistical inference in economics, in: Statistical Inference in Dynamic Economic Models, John Wiley and Sons.

Matzkin, R.L., 2007. Chapter 73 nonparametric identification, Elsevier. volume 6, Part 2 of Handbook of Econometrics, pp. 5307–5368.

Mäki, U., 2008. scientific realism and ontology, in: Durlauf, S.N., Blume, L.E. (Eds.), The New Palgrave Dictionary of Economics. Palgrave Macmillan, Basingstoke.

Mäki, U., 2011. Models and the locus of their truth. Synthese 180(1) , 47-63.

Neuberg, L.G., 2003. Causality: Models, reasoning, and inference, by Judea Pearl, Cambridge University Press, 2000. Econometric Theory 19, 675– 685.

Neyman, J., 1935. Statistical problems in agricultural experimentation. Supplement to the Journal of the Royal Statistical Society 2, pp. 107–180.

Pao, H.T., Tsai, C.M., 2010. CO2 emissions, energy consumption and economic growth in BRIC countries. Energy Policy 38, 7850–7860. Special Section: Carbon Reduction at Community Scale.

Pao, H.T., Tsai, C.M., 2011. Multivariate granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries. Energy 36, 685–693.

Pearl, J., 1993. Bayesian analysis in expert systems: Comment: Graphical models, causality and intervention. Statistical Science 8, pp. 266–269.

Pearl, J., 2003. Comments on Neuberg’s review of Causality. Econometric Theory 19, 686–689.

Pearl, J., 2009. Causality: Models, Reasoning and Inference. Cambridge University Press, New York, NY, USA.

Richmond, A.K., Kaufmann, R.K., 2006. Is there a turning point in the relationship between income and energy use and/or carbon emissions? Ecological Economics 56, 176–189.

Rubin, D.B., 1974. Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology .

Rubin, D.B., 1978. Bayesian inference for causal effects: The role of randomization. The Annals of Statistics 6, pp. 34–58.

Simon, H., 1953. Causal ordering and identifiability, in: Hood, W., Koopmans, J. (Eds.), Studies in econometric method. New York, NY: Wiley.. chapter 3.

Simon, H.A., Iwasaki, Y., 1988. Causal ordering, comparative statics, and near decomposability. Journal of Econometrics 39, 149–173.

Simon, H.A., Rescher, N., 1966. Cause and counterfactual. Philosophy of Science 33, 323–340.

Soytas, U., Sari, R., 2009. Energy consumption, economic growth, and carbon emissions: Challenges faced by an EU candidate member. Ecological Economics 68, 1667–1675. Eco-efficiency: From technical optimisation to reflective sustainability analysis.

Soytas, U., Sari, R., Ewing, B.T., 2007. Energy consumption, income, and carbon emissions in the United States. Ecological Economics 62, 482–489.

Strotz, R.H., Wold, H.O.A., 1960. Recursive vs. nonrecursive systems: An attempt at synthesis (part I of a triptych on causal chain systems). Econometrica 28, pp. 417–427.

Strotz, R.H., Wold, H.O.A., 1963. The causal interpretability of structural parameters: A reply. Econometrica 31, pp. 449–450.

Watson, M.W., 1994. Chapter 47 vector autoregressions and cointegration, Elsevier. volume 4 of Handbook of Econometrics, pp. 2843–2915.

Wooldridge, J.M., 2002. Econometric Analysis of Cross Section and Panel Data. volume 1 of MIT Press Books. The MIT Press.