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ERSA Training Workshop Econometric Methods for Panel Data Steve Bond (University of Oxford) Måns Söderbom (University of Gothenburg) Monday 12 January ­ Friday 16 January, 2009 University of Stellenbosch Course Outline

The course will focus on econometric methods for panel data where the cross-section dimension is large and the time-series dimension is small. This is typical of micro panels on households or firms. The implementation of standard methods using Stata will be presented, and empirical applications using panel data on firms will be discussed. Particular emphasis will be given to the estimation of firm-level production functions. The first lecture will review classical methods for linear regression models with unobserved heterogeneity in the form of time-invariant individual-specific effects, which may be correlated with the explanatory variables. This will focus on Within Groups (or Fixed Effects) and related estimators, and their implementation using the xtreg command in Stata. The core part of the course will then cover Generalised Method of Moments (GMM) estimators for dynamic linear models which may include lagged dependent variables. This will cover first-differenced GMM estimators which transform the original model to eliminate the unobserved effects and rely on limited serial correlation in the transformed error process to obtain valid moment conditions or instrumental variables. This will also cover extended GMM estimators which incorporate additional moment conditions for the untransformed equations in levels, relying on instrumental variables that are orthogonal to the individual-specific effects. Inference and specification tests will be considered in this context, and finite sample bias issues related to `weak instruments' will be discussed. Computer classes will demonstrate the implementation of these methods using the xtabond2 command in Stata. The main application we consider will be the estimation of Cobb Douglas production functions using firm-level panel data. Alternative approaches to dealing with unobserved heterogeneity in this context will be presented and discussed. The final part of the course will introduce methods for non-linear models, focusing on the estimation of both static and dynamic binary choice models with unobserved individual-specific effects.

Core Reading

GMM estimators for linear panel data models Bond, S.R. (2002) `Dynamic panel data models: a guide to micro data methods and practice', Portuguese Economic Journal, 1, 141-162 (a working paper version can be found at: http://cemmap.ifs.org.uk/wps/cwp0209.pdf) Blundell, R.W. and Bond, S.R. (2000), `GMM estimation with persistent panel data: an application to production functions', Econometric Reviews, 19, 321-340 (a working paper version can be found at: http://www.ifs.org.uk/publications.php?publication_id=2722)

Estimation of production functions Ackerberg, D., C.L. Benkard, S. Berry and A. Pakes (2006). Econometric Tools for Analyzing Market Outcomes, Section 2. This paper, prepared for Handbook of Econometrics (eds. J. Heckman and E.Leamer), can be found at: http://www.stanford.edu/~lanierb/research/tools8l-6-8.pdf

Binary choice panel data models i) Econometrics Wooldridge, J.M. (2001), Econometric Analysis of Cross Section and Panel Data, MIT Press, Chapter 15. ii) Application: Modelling the decision to export Bigsten, A., P. Collier, S. Dercon, M. Fafchamps, B. Gauthier, J.W. Gunning, A. Oduro, R. Oostendorp, C. Pattillo, M. Söderbom, F. Teal, and A. Zeufack. 2004. `Do African Manufacturing Firms Learn from Exporting?', Journal of Development Studies 40 (3): 115­171. Roberts, M. and J. Tybout (1997), `The Decision to Export in Colombia: An Empirical Model of Entry with Sunk Costs,' American Economic Review, No. 87, pp. 545-64.

Additional References

The main papers that developed GMM estimators for linear panel data models include: Arellano, M. and Bond, S.R. (1991), `Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations', Review of Economic Studies, 58, 277-297 Arellano, M. and Bover, O. (1995), `Another look at the instrumental variable estimation of error-components models', Journal of Econometrics, 68, 29-52 Blundell, R.W. and Bond, S.R. (1998), `Initial conditions and moment restrictions in dynamic panel data models', Journal of Econometrics, 87, 115-143

A comprehensive textbook treatment can be found in: Arellano, M. (2003), Panel Data Econometrics, Oxford University Press

Other textbook treatments include: Baltagi, B.H. (2005), Econometric Analysis of Panel Data, 3rd edition, Wiley Cameron, A.C. and Trivedi, P.K. (2006), Microeconometrics: Methods and Applications, Cambridge University Press Hsiao, C. (2003), Analysis of Panel Data, 2nd edition, Cambridge University Press Wooldridge, J.M. (2001), Econometric Analysis of Cross Section and Panel Data, MIT Press

A useful overview and guide to the Stata implementation is: Roodman, D.M. (2006), `How to do xtabond2: an introduction to `difference' and `system' GMM in Stata', Center for Global Development Working Paper no. 103 (http://www.cgdev.org/content/publications/detail/11619)

The finite sample correction for two-step GMM standard errors is developed in: Windmeijer, F. (2005), `A finite sample correction for the variance of linear efficient two-step GMM estimators', Journal of Econometrics, 126, 25-51

The estimation of firm-level production functions is considered further in: Ackerberg, D., K. Caves, and G. Frazer (2006). `Structural Identification of Production Functions', mimeo. Downloadable at: http://www.econ.ucla.edu/ackerber/ACF20withtables.pdf Griliches Z. and J. Mairesse (1998). `Production functions: The Search for Identification', in Econometrics and Economic Theory in the Twentieth Century: The Ragnar Frisch Centennial Symposium, 169-203. Cambridge University Press. Levinsohn, J. and A. Petrin (2003). `Estimating Production Functions Using Inputs to Control for Unobservables', Review of Economic Studies 70, 317-341. Olley, S. and Pakes, A. (1996) `The dynamics of productivity in the telecommunications equipment industry', Econometrica 64, 1263-1297.

Additional material on methods for non-linear models includes: Arellano, Manuel and Raquel Carrasco (2003). `Binary choice panel data models with predetermined variables', Journal of Econometrics, 115(1), 125-157. Heckman, James (1981), `The Incidental Parameters Problem and the Problem of Initial Conditions in Estimating a Discrete Time ­ Discrete Data Stochastic Process,' in Structural Analysis of Discrete Panel Data with Econometric Applications, eds. C. Manski and D. McFadden, Cambridge: MIT Press. Honoré, Bo (2002), `Nonlinear models with panel data', Portuguese Economic Journal 1: 163-179. Wooldridge, Jeffrey (2005). `Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity', Journal of Applied Econometrics 20, 39-54.

An overview of the research on African manufacturing firms is provided in: Bigsten, A. and M. Söderbom (2006). `What Have We Learned from a Decade of Manufacturing Enterprise Surveys in Africa?', World Bank Research Observer 21:2, 241-265.

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