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A Nonlinear Panel Data Model of Cross-Sectional Dependence

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  • James Mitchell

    ()

  • George Kapetanios
  • Yongcheol Shin

Abstract

This paper proposes a nonlinear panel data model which can generate endogenously both `weak' and `strong' cross-sectional dependence. The model's distinguishing characteristic is that a given agent's behaviour is influenced by an aggregation of the views or actions of those around them. The model allows for considerable flexibility in terms of the genesis of this herding or clustering type behaviour. At an econometric level, the model is shown to nest various extant dynamic panel data models. These include panel AR models, spatial models, which accommodate weak dependence only, and panel models where cross-sectional averages or factors exogenously generate strong, but not weak, cross sectional dependence. An important implication is that the appropriate model for the aggregate series becomes intrinsically nonlinear, due to the clustering behaviour, and thus requires the disaggregates to be simultaneously considered with the aggregate. We provide the associated asymptotic theory for estimation and inference. This is supplemented with Monte Carlo studies and two empirical applications which indicate the utility of our proposed model as both a structural and reduced form vehicle to model different types of cross-sectional dependence, including evolving clusters.

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File URL: http://www.le.ac.uk/economics/research/repec/lec/leecon/dp12-01.pdf
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Paper provided by Department of Economics, University of Leicester in its series Discussion Papers in Economics with number 12/01.

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Date of creation: Jan 2012
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Handle: RePEc:lec:leecon:12/01
Contact details of provider: Postal: Department of Economics University of Leicester, University Road. Leicester. LE1 7RH. UK
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Keywords: Nonlinear Panel Data Model; Clustering; Cross-section Dependence; Factor Models; Monte Carlo Simulations; Application to Stock Returns and Inflation Expectations;

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Find related papers by JEL classification:
  • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
  • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
  • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
  • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
  • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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  1. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
  2. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
  3. Offer Lieberman, 2012. "A similarity‐based approach to time‐varying coefficient non‐stationary autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(3), pages 484-502, 05.
  4. Christopher D Carroll, 2002. "Macroeconomic Expectations of Households and Professional Forecasters," Economics Working Paper Archive 477, The Johns Hopkins University,Department of Economics.
  5. Itzhak Gilboa & Offer Lieberman & David Schmeidler, 2004. "Empirical Similarity," Levine's Bibliography 122247000000000684, UCLA Department of Economics.
  6. M. Hashem Pesaran, 2004. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," CESifo Working Paper Series 1331, CESifo Group Munich.
  7. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
  8. Timmermann, Allan, 1994. "Can Agents Learn to Form Rational Expectations? Some Results on Convergence and Stability of Learning in the UK Stock Market," Economic Journal, Royal Economic Society, vol. 104(425), pages 777-97, July.
  9. Korniotis, George M., 2010. "Estimating Panel Models With Internal and External Habit Formation," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 145-158.
  10. Gonzalo, Jesus & Wolf, Michael, 2005. "Subsampling inference in threshold autoregressive models," Journal of Econometrics, Elsevier, vol. 127(2), pages 201-224, August.
  11. Narasimhan Jegadeesh & Woojin Kim, 2010. "Do Analysts Herd? An Analysis of Recommendations and Market Reactions," Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 901-937, February.
  12. Richard W. Sias, 2004. "Institutional Herding," Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 165-206.
  13. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2009. "Weak and Strong Cross Section Dependence and Estimation of Large Panels," CESifo Working Paper Series 2689, CESifo Group Munich.
  14. Alexander Chudik & M. Hashem Pesaran, 2007. "Infinite Dimensional VARs and Factor Models," CESifo Working Paper Series 2176, CESifo Group Munich.
  15. Roger E. A. Farmer, 2009. "Animal Spirits: How Human Psychology Drives the Economy, and Why it Matters for Global Capitalism," The Economic Record, The Economic Society of Australia, vol. 85(270), pages 357-358, 09.
  16. Chevillon, Guillaume & Massmann, Michael & Mavroeidis, Sophocles, 2010. "Inference in models with adaptive learning," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 341-351, April.
  17. Tweedie, Richard L., 1975. "Sufficient conditions for ergodicity and recurrence of Markov chains on a general state space," Stochastic Processes and their Applications, Elsevier, vol. 3(4), pages 385-403, October.
  18. Souleles, Nicholas S, 2004. "Expectations, Heterogeneous Forecast Errors, and Consumption: Micro Evidence from the Michigan Consumer Sentiment Surveys," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(1), pages 39-72, February.
  19. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
  20. Snehal Banerjee & Ilan Kremer, 2010. "Disagreement and Learning: Dynamic Patterns of Trade," Journal of Finance, American Finance Association, vol. 65(4), pages 1269-1302, 08.
  21. Lieberman, Offer, 2010. "Asymptotic Theory For Empirical Similarity Models," Econometric Theory, Cambridge University Press, vol. 26(04), pages 1032-1059, August.
  22. Kapetanios, G., 1999. "Model Selection in Threshold Models," Cambridge Working Papers in Economics 9906, Faculty of Economics, University of Cambridge.
  23. Devenow, Andrea & Welch, Ivo, 1996. "Rational herding in financial economics," European Economic Review, Elsevier, vol. 40(3-5), pages 603-615, April.
  24. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  25. Gregory, Allan W & Smith, Gregor W & Yetman, James, 2001. "Testing for Forecast Consensus," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 34-43, January.
  26. David Hirshleifer & Siew Hong Teoh, 2003. "Herd Behaviour and Cascading in Capital Markets: a Review and Synthesis," European Financial Management, European Financial Management Association, vol. 9(1), pages 25-66.
  27. Banerjee, Abhijit V, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, MIT Press, vol. 107(3), pages 797-817, August.
  28. Kapetanios, George, 2010. "A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 397-409.
  29. Gayer Gabrielle & Gilboa Itzhak & Lieberman Offer, 2007. "Rule-Based and Case-Based Reasoning in Housing Prices," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 7(1), pages 1-37, April.
  30. Yu, Ping, 2012. "Likelihood estimation and inference in threshold regression," Journal of Econometrics, Elsevier, vol. 167(1), pages 274-294.
  31. Gilboa, Itzhak & Lieberman, Offer & Schmeidler, David, 2011. "A similarity-based approach to prediction," Journal of Econometrics, Elsevier, vol. 162(1), pages 124-131, May.
  32. Trueman, Brett, 1994. "Analyst Forecasts and Herding Behavior," Review of Financial Studies, Society for Financial Studies, vol. 7(1), pages 97-124.
  33. Snehal Banerjee & Ron Kaniel & Ilan Kremer, 2009. "Price Drift as an Outcome of Differences in Higher-Order Beliefs," Review of Financial Studies, Society for Financial Studies, vol. 22(9), pages 3707-3734, September.
  34. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
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