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GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA

Published online by Cambridge University Press:  17 March 2016

Xun Lu ,
Liangjun Su  and
Halbert White
Xun Lu*
Affiliation:
Hong Kong University of Science and Technology
Liangjun Su
Affiliation:
Singapore Management University
Halbert White
Affiliation:
University of California, San Diego
*
*Address correspondence to Xun Lu, Department of Economics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong; e-mail: xunlu@ust.hk.
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Abstract

Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G–) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable, G–non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G–causality. This justifies using tests of G–non-causality to test for structural noncausality under the key conditional exogeneity assumption for both cross-section and panel data. We pay special attention to heterogeneous populations, allowing both structural heterogeneity and distributional heterogeneity. Most of our results are obtained for the general case, without assuming linearity, monotonicity in observables or unobservables, or separability between observed and unobserved variables in the structural relations.

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ET LECTURE
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Econometric Theory , Volume 33 , Issue 2 , April 2017 , pp. 263 - 291
Copyright
Copyright © Cambridge University Press 2016 

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References

REFERENCES

Altonji, J. & Matzkin, R. (2005) Cross section and panel data estimators for nonseparable models with endogenous regressors. Econometrica 73, 1053–1102.CrossRefGoogle Scholar
Angrist, J., Graddy, K., & Imbens, G. (2000) The interpretation of instrumental variables estimators in simultaneous equations models with an application to the demand for fish. Review of Economic Studies 67, 499–527.CrossRefGoogle Scholar
Angrist, J., Jordà, Ò., & Kuersteiner, G. (2013) Semiparametric estimates of monetary policy effects: string theory revisited. NBER Working Paper 19355.CrossRefGoogle Scholar
Angrist, J. & Kuersteiner, G. (2004) Semiparametric causality tests using the policy propensity score. NBER Working Paper No. 10975.CrossRefGoogle Scholar
Angrist, J. & Kuersteiner, G. (2011) Causal effects of monetary shocks: semiparametric conditional independence tests with a multinomial propensity score. Review of Economics and Statistics 93, 725–747.CrossRefGoogle Scholar
Angrist, J. & Pischke, J. (2009) Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press.CrossRefGoogle Scholar
Bester, C.A. & Hansen, C.B. (2016) Grouped effects estimators in fixed effects models. Journal of Econometrics 190, 197–208.CrossRefGoogle Scholar
Bonhomme, S. & Manresa, E. (2015) Grouped patterns of heterogeneity in panel data. Econometrica 83, 1147–1184.CrossRefGoogle Scholar
Browning, M. & Carro, J.M. (2007) Heterogeneity and microeconometrics modelling. In Blundell, R., Newey, W. K. and Persson, T. (Eds.), Advances in Economics and Econometrics, Theory and Applications: Ninth World Congress of the Econometric Society, Volume 3, pp. 45–74. Cambridge University Press.Google Scholar
Browning, M. & Carro, J.M. (2010) Heterogeneity in dynamic discrete choice models. Econometrics Journal 13, 1–39.CrossRefGoogle Scholar
Burda, M., Harding, M., & Hausman, J. (2015) A Bayesian semi-parametric competing risk model with unobserved heterogeneity. Journal of Applied Econometrics 30, 353–376.CrossRefGoogle Scholar
Chalak, K. & White, H. (2011) An extended class of instrumental variables for the estimation of causal effects. Canadian Journal of Economics 44, 1–51.CrossRefGoogle Scholar
Chalak, K. & White, H. (2012) Causality, conditional independence, and graphical separation in settable systems. Neural Computation 24, 1611–1668.CrossRefGoogle Scholar
Chamberlain, G. (1982) The general equivalence of Granger and Sims causality. Econometrica 50, 569–581.CrossRefGoogle Scholar
Chamberlain, G. (1984) Panel data. In Griliches, Z. & Intriligator, M. (eds.), Handbook of Econometrics, vol. 2, ch. 22, pp.1247–1318. Elsevier.Google Scholar
Chesher, A. (2003) Identification in nonseparable models. Econometrica 71, 1405–1441.CrossRefGoogle Scholar
Chesher, A. (2005) Nonparametric identification under discrete variation. Econometrica 73, 1525–1550.CrossRefGoogle Scholar
Dawid, A.P. (1979) Conditional independence in statistical theory. Journal of the Royal Statistical Society, Series B 41, 1–31.Google Scholar
Deb, P. & Trivedi, P.K. (2013) Finite mixture for panels with fixed effects. Journal of Econometric Methods 2, 35–51.CrossRefGoogle Scholar
Delgado, M. & González-Manteiga, W. (2001) Significance testing in nonparametric regression based on the bootstrap. Annals of Statistics 29, 1469–1507.CrossRefGoogle Scholar
Dumitrescu, E-I. & Hurlin, C. (2012) Testing for Granger non-causality in heterogeneous panels. Economic Modelling 29, 1450–1460.CrossRefGoogle Scholar
Evdokimov, K. (2010) Identification and estimation of a nonparametric panel data model with unobserved heterogeneity. Working paper, Department of Economics, Princeton University.Google Scholar
Florens, J.P. (2003) Some technical issues in defining causality. Journal of Econometrics 112, 127–128.CrossRefGoogle Scholar
Florens, J.P. & Fougère, D. (1996) Non-causality in continuous time. Econometrica 64, 1195–1212.CrossRefGoogle Scholar
Florens, J.P. & Mouchart, M. (1982) A note on non-causality. Econometrica 50, 583–591.CrossRefGoogle Scholar
Granger, C.W.J. (1969) Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424–438.CrossRefGoogle Scholar
Granger, C.W.J. (1980) Testing for causality, a personal viewpoint. Journal of Economic Dynamics and Control 2, 329–352.CrossRefGoogle Scholar
Granger, C.W.J. (1988) Some recent developments in a concept of causality. Journal of Econometrics 39, 199–211.CrossRefGoogle Scholar
Granger, C.W.J. & Newbold, P. (1986) Forecasting Economic Time Series. Academic Press, 2nd edition.Google Scholar
Hausman, J. & Woutersen, T. (2014) Estimating a semi-parametric duration model without specifying heterogeneity. Journal of Econometrics 178, 114–131.CrossRefGoogle Scholar
Haavelmo, T. (1943) The statistical implications of a system of simultaneous equations. Econometrica 11, 1–12.CrossRefGoogle Scholar
Haavelmo, T. (1944) The probability approach in econometrics. Econometrica 12 (supp.), iii-vi, 1–115.CrossRefGoogle Scholar
Hahn, J. (1998) On the role of the propensity score in efficient semiparametric estimation of average treatment effect. Econometrica 66, 315–331.CrossRefGoogle Scholar
Hahn, J. & Moon, R. (2010) Panel data models with finite number of multiple equilibria. Econometric Theory 26, 863–881.CrossRefGoogle Scholar
Heckman, J.J. (2000) Causal parameters and policy analysis in economics: a twentieth century retrospective. Quarterly Journal of Economics 115, 45–97.CrossRefGoogle Scholar
Heckman, J.J. (2008) Econometric causality. International Statistical Review 76, 1–27.CrossRefGoogle Scholar
Heckman, J.J. & Pinto, R. (2015) Causal analysis after Haavelmo. Econometric Theory 31, 115–151.CrossRefGoogle ScholarPubMed
Hirano, K., Imbens, G., & Ridder, G. (2003) Efficient estimation of average treatment effects using the estimated propensity score. Econometrica 71, 1161–1189.CrossRefGoogle Scholar
Hoderlein, S. & Mammen, E. (2007) Identification of marginal effects in nonseparable models without monotonicity. Econometrica 75, 1513–1519.CrossRefGoogle Scholar
Hoderlein, S. & Mammen, E. (2009) Identification and estimation of local average derivatives in non-separable models without monotonicity. Econometrics Journal 12, 1–25.CrossRefGoogle Scholar
Hoderlein, S. & White, H. (2012) Nonparametric identification in nonseparable panel data models with generalized fixed effects. Journal of Econometrics 168, 300–314.CrossRefGoogle Scholar
Holland, P. (1986) Statistics and causal inference. Journal of the American Statistical Association 81, 945–970.CrossRefGoogle Scholar
Holtz-Eakin, D., Newey, W., & Rosen, H.S. (1988) Estimating vector autoregressions with panel data. Econometrica 56, 1371–1396.CrossRefGoogle Scholar
Hoover, K. (2008) Causality in economics and econometrics. In Durlauf, S. and Blume, L. (eds.), The New Palgrave Dictionary of Economics, 2nd edition, Palgrave Macmillan.Google Scholar
Hsiao, C. (2014) Analysis of Panel Data. Cambridge University Press.CrossRefGoogle Scholar
Huang, M., Sun, Y. & White, H. (2016) A flexible nonparametric test for conditional independence. Econometric Theory, forthcoming.CrossRefGoogle Scholar
Hurwicz, L. (1950) Generalization of the concept of identification. In Koopmans, T.C. (eds.), Statistical Inference in Dynamic Economic Models, pp. 238–257. John Wiley.Google Scholar
Im, K., Pesaran, H., & Shin, Y. (2003) Testing for unit roots in heterogeneous panels. Journal of Econometrics 115, 53–74.CrossRefGoogle Scholar
Imbens, G. (2000) The role of the propensity score in estimating dose-response functions. Biometrika 87, 706–710.CrossRefGoogle Scholar
Imbens, G. (2004) Nonparametric estimation of average treatment effects under exogeneity: a review. Review of Economics and Statistics 86, 4–29.CrossRefGoogle Scholar
Imbens, G. & Newey, W. (2009) Identification and estimation of triangular simultaneous equations models without additivity. Econometrica 77, 1481–1512.Google Scholar
Imbens, G. & Wooldridge, J. (2009) Recent developments in the econometrics of program evaluation. Journal of Economic Literature 47, 5–86.CrossRefGoogle Scholar
Kasy, M. (2011) Identification in triangular systems using control functions. Econometric Theory 27, 663–671.CrossRefGoogle Scholar
Koopmans, T. (1950) Statistical Inference in Dynamic Economic Models. Cowles Commission Monograph No. 10, Wiley.Google Scholar
Kuersteiner, G. (2008) Granger–Sims causality. In Durlauf, S. and Blume, L. (Eds.), The New Palgrave Dictionary of Economics, 2nd edition, Palgrave Macmillan.Google Scholar
Lechner, M. (2001) Identification and estimation of causal effect of multiple treatments under conditional independence assumption. In Lechner, M., Pfeiller, F. (eds), Econometric Evaluations of Labour Market Policies, Hildelberg, Physical, 43–58.CrossRefGoogle Scholar
Lechner, M. (2011) The relation of different concepts of causality used in time series and microeconometrics. Econometric Reviews 30, 109–127.CrossRefGoogle Scholar
Lin, C-C. & Ng, S. (2012) Estimation of panel data models with parameter heterogeneity when group membership is unknown. Journal of Econometric Methods 1, 42–55.CrossRefGoogle Scholar
Lindsay, B. (1995) Mixture Models: Theory, Geometry and Applications. Hayward: Institute for Mathematical Statistics.CrossRefGoogle Scholar
Linton, O. & Gozalo, P. (2014) Testing conditional independence restrictions. Econometric Reviews 33, 523–552.CrossRefGoogle Scholar
Lu, X. & Su, L. (2014) Determining the number of groups in latent panel structures. Working paper, School of Economics, Singapore Management University.Google Scholar
Matzkin, R. (2003). Nonparametric estimation of nonadditive random functions. Econometrica 71, 1339–1375.CrossRefGoogle Scholar
Matzkin, R. (2007). Nonparametric identification. In Heckman, J.J. and Leamer, E.E. (eds.), The Handbook of Econometrics, 6B. North-Holland.Google Scholar
Mclachlan, G. & Basford, K. (1988) Mixture Models: Inference and Applications to Clustering. Marcel Dekker.Google Scholar
Nair-Reichert, U. & Weinhold, D. (2001) Causality tests for cross-country panels: a new look at FDI and economic growth in developing countries. Oxford Bulletin of Economics and Statistics 63, 153–171.CrossRefGoogle Scholar
Pearl, J. (2009) Causality: Models, Reasoning, and Inference (2nd edition). Cambridge University Press.CrossRefGoogle Scholar
Pearl, J. (2015) Trygve Haavelmo and the emergence of causal calculus. Econometric Theory 31, 152–179.CrossRefGoogle Scholar
Rosenbaum, P. & Rubin, D. (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55.CrossRefGoogle Scholar
Rubin, D. (1974) Estimating causal effects of treatments in randomized and non-randomized studies. Journal of Educational Psychology 66, 688–701.CrossRefGoogle Scholar
Rubin, D. (2004) Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics 31, 161–170.CrossRefGoogle Scholar
Sarafidis, V. & Weber, N. (2015) A partially heterogeneous framework for analyzing panel data. Oxford Bulletin of Economics and Statistics 77, 274–296.CrossRefGoogle Scholar
Sims, C. (1972) Money, income and causality. American Economic Review 62, 540–552.Google Scholar
Song, K. (2009) Testing conditional independence via Rosenblatt transforms. Annals of Statistics 37, 4011–4015.CrossRefGoogle Scholar
Strotz, R. & Wold, H. (1960) Recursive vs. nonrecursive systems: An attempt at synthesis. Econometrica 28, 417–427.CrossRefGoogle Scholar
Su, L., Shi, Z., & Phillips, P.C.B. (2014) Identifying latent structures in panel data. Working paper, Department of Economics, Yale University.CrossRefGoogle Scholar
Su, L. & White, H. (2007) A Consistent characteristic function-based test for conditional independence. Journal of Econometrics 141, 807–834.CrossRefGoogle Scholar
Su, L. & White, H. (2008) A nonparametric Hellinger metric test for conditional independence. Econometric Theory 24, 829–864.CrossRefGoogle Scholar
Su, L. & White, H. (2014) Testing conditional independence via empirical likelihood. Journal of Econometrics 182, 27–44.CrossRefGoogle Scholar
Sun, Y. (2005) Estimation and inference in panel structure models. Working paper, Department of Economics, UCSD.CrossRefGoogle Scholar
White, H. & Chalak, K. (2009) Settable systems: An extension of Pearl’s causal model with optimization, equilibrium, and learning. Journal of Machine Learning Research 10, 1759–1799.Google Scholar
White, H. & Chalak, K. (2010) Testing a conditional form of exogeneity. Economics Letters 109, 88–90.CrossRefGoogle Scholar
White, H. & Chalak, K. (2013) Identification and identification failure for treatment effects using structural systems. Econometric Reviews 32, 273–317.CrossRefGoogle Scholar
White, H., Chalak, K., & Lu, X. (2011) Linking Granger causality and the Pearl causal model with settable systems. Journal of Machine Learning Research, Workshop and Conference Proceedings, 12, 1–29.Google Scholar
White, H. & Kennedy, P. (2009) Retrospective estimation of causal effects through time. In Castle, J. & Shephard, N. (eds.), The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry, pp. 59–87. Oxford University Press.CrossRefGoogle Scholar
White, H. & Lu, X. (2010) Granger causality and dynamic structural systems. Journal of Financial Econometrics 8, 193–243.CrossRefGoogle Scholar
White, H. & Lu, X. (2011) Causal diagrams for treatment effect estimation with application to selection of efficient covariates. Review of Economics and Statistics 93, 1453–1459.CrossRefGoogle Scholar
Wooldridge, J. (2005) Simple solutions to the initial conditions problem for dynamic, nonlinear panel data models with unobserved heterogeneity. Journal of Applied Econometrics 20, 39–54.CrossRefGoogle Scholar
Zellner, A. (1979) Causality and econometrics. In Brunner, K. and Meltzer, A.H. (eds), Three Aspects of Policy and Policymaking, Carnegie-Rochester Conference Series, Vol. 10, North-Holland, 9–54.CrossRefGoogle Scholar