• MLU Halle-Wittenberg – Löwengebäude
  • Ifo Institut Dresden
  • Universität Leipzig -Treppenhaus Albertina
  • Universität Leipzig – Institutsgebäude
  • TU Dresden – Luftbild
  • Universität Madgeburg
  • IWH – Außenansicht
  • Universität Leipzig – Leibniz-Denkmal
  • TU Dresden – Vorderansicht
  • TU Dresden – Hörsaalzentrum auf dem Campus
  • IWH Innenhof
  • FSU – Vorderansicht
  • FSU – von oben
  • FSU – Saal
  • OvGU Gelände

Frontiers in Panel Data Econometrics

Frontiers in Panel Data Econometrics

Lecturer: Prof. Kajal Lahiri, Ph.D. (University at Albany, State University of New York)
Date: December 1, 2008 – December 3, 2008 (IWH, Halle)
Venue: IWH, Halle
Registration:

Course description

The term ‘panel data’ refers to data sets where we have information on the same individual observed over several periods of time. The main advantage of having panel data as compared to either a single cross section or time series is that panel data allows us to test and relax the assumptions that are implicit in models that use only cross sectional or time series data. It also allows econometricians to study and control for the effects of unobservable individual effects like ability, managerial efficiency, etc.
The lectures will provide a comprehensive survey of currently used econometric techniques that deal with panel data using many empirical examples. Recent developments and suggestions for future research will be emphasized.

Course topics

1. Analysis of variance/covariance, ANOVA, within- and between group variations, Pool or not to pool time series with cross sections.
2. One-way and other nested error structures, error components model, Seemingly Unrelated regressions, Random coefficients models, and specification tests.
3. Within and GLS estimation of error components models.
4. Errors in variables and panel data models.
5. Estimation with singly and doubly exogenous regressors, correlated individual effects.
6. Simultaneity and panel data models.
7. Dynamic panel data models, tests for heterogeneity, predetermined but not strictly exogenous instruments.
8. Dynamic panel and random coefficient models, heterogeneous panels.
9. Limited dependent and qualitative variables in panel data models.
10. Pseudo and rotating panels.
11. Spatial panels.
12. Panel data unit root tests, panel cointegration, common effects, cross sectional dependence, and factor structural errors.

Reading – text book

[1] B. Baltagi, Econometric Analysis of Panel Data, 3rd edition.
[1] W. Greene, Econometrics Analysis, 5th edition.
[1] C. Hsiao, Analysis of Panel Data, 2nd Edition.
[1] M. Arellano, Panel Data Econometrics.
[1] G.S. Maddala and K. Lahiri, Introduction to Econometrics, 4th edition.
[1] J.M. Wooldridge, Econometric Analysis of Cross Section and Panel Data.

Reading – articles

[1] Ahn, S.C. and P. Schmidt, 1999, Modified Generalized instrumental variable estimation of panel data models with strictly exogenous instrumental variables, Chapter 7 in C.
Hsiao, K. Lahiri, L.F. Lee and H. Pesaran (eds.), Analysis of Panels and Limited Dependent Variable Models.
[2] Arellano, M. and S. Bond, 1991, Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, Review of Economic Studies.
[3] Arellano, M. and O. Bover, 1995, Another look at the instrumental variable estimation of error components models. Journal of Econometrics.
[4] Bai. J, and S. Ng, 2004, A PANIC attack on unit roots and cointegration, Econometrica.
[5] Balestra, P. and M. Nerlove, 1966, Pooling of cross section and time series data in the estimation of a dynamic model: The demand for natural gas, Econometrica.
[6] Baltagi, B. H. and C. Kao, 2000, Nonstationary panels, cointegration in panels and dynamic panels: A survey. Advances in Econometrics, 15, 7-51.
[7] Baltagi, B.H., P. Egger, and M. Pfaffermayr, 2008, Estimating regional trade agreement effects on FDI in an interdependent world, Journal of Econometrics, Vol. 145.
[8] Banerjee, A. 1999, Panel Data unit roots and cointegration, Oxford Bulletin of Economics and Statistics.
[9] Bhargava, A. and J.D. Sargan, 1983, Estimating dynamic random effects models from panel data covering short time periods, Econometrica.
[10] Bhargava, A., L. Franzini, and W. Narendranathan, 1982, Serial correlation and fixed effects model, Review of economic Studies.
[11] Biorn, E. 1996, Panel data with measurement errors, chapter 10 in L. Matyas and P. Sevestre, The Econometrics of Panel data: A Handbook of Theory with Applications.
[12] Blundell, R. and S. Bond, 1998, Initial conditions and moment restrictions in dynamic panel data models, Journal of Econometrics.
[13] Breusch, T.S. and a. Pagan, 1980, The Lagrange Multiplier test and its application to model specification in econometrics, Review of Economic Studies.
[14] Breusch, T.S., Mizon, G., and P. Schmidt, 1989, Efficient estimation using panel data, Econometrica.
[15] Case, A.C., 1991, Spatial patterns in household demand, Econometrica.
[16] Deaton, A. 1985, Panel data from time series of cross sections, Journal of Econometrics.
[17] Griliches, Z. and J.A. Hausman, 1986, Errors in variables in panel data, Journal of econometrics.
[18] Hausman J.A. and W.E. Taylor, Panel data and unobservable individual effects, Econometrica, 1981.
[19] Hausman, J.A. 1978, Specification tests in econometrics, Econometrica.
[20] Holtz-Eakin, D. 1988, Testing for individual effects in autoregressive models, Journal of Econometrics.
[21] Honore, B.E. and E. Kyriazidou, 2000, Panel data discrete choice models with lagged dependent variables, Econometrica.
[22] Hsiao C, Pesaran MH, Tahmiscioglu AK. 1999. Bayes estimation of short run coefficients in dynamic panel data models. In Analysis of Panels and Limited Dependent Variable Models, Hsiao C, Lahiri K, Lee LF, Pesaran MH (eds.), Cambridge University Press: Cambridge.
[23] Davies A, Lahiri K. 1995. A new framework for analyzing survey forecasts using threedimensional panel data. Journal of Econometrics 68: 205-227.
[24] Pesaran MH, Smith R. 1995. Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics 68: 79-113.
[25] Pesaran MH, Smith R, Im K-S. 1996. Dynamic linear models for heterogeneous panels. In The Econometrics of Panel Data, 2nd ed., L. Matyas and P. Sevestre (eds). Kluwer Academic publishers: London.
[26] Kapoor, M., H. H Kelejian and I. Prucha, 2007, Panel data models with spatially correlated error components, Journal of Econometrics, Vol. 140, 97-130.
[27] Kinal, T., and K. Lahiri. 1993, A simplified algorithm for estimation of simultaneous equations error components models with an application to a model of developing country foreign trade, Journal of Applied Econometrics.
[28] Lahiri, K. Panel data models with rational expectations, in G.S. Maddala, C.R. Rao and H.D. Vinod, (eds). Handbook of Statistics, Volume 11, Chapter 26.
[29] Lahiri, K. and X. Sheng, 2008, Evolution of Forecast Disagreement in a Bayesian Learning Model, Journal of Econometrics, 144, 325-340.
[30] Maddala, G.S., and S. Wu, 1999, A comparative study of unit root tests with panel data a new simple test, Oxford Bulletin of Economics and Statistics.
[31] Mundlak, Y. 1961, Empirical Production function free of management bias, Journal of Farm Economics, 69-85.
[32] Mundlak, Y. 1978, On the pooling of time series and cross section data, Econometrica.
[33] Stock and Watson (2005), Understanding the Changes in International Business Cycle Dynamics, Journal of the European Economic Association, 3, 968-1006.
[34] Verbeek, M. 1996, Pseudo Panel data. Chapter 11 in L. Matyas and P. Sevestre, The Econometrics of Panel Data: A Handbook of Theory with Applications.
[35] Verbeek, M and F. Vella, 2005, Estimating dynamic models from repeated cross sections, Journal of Econometrics, Vol. 127, 83-102.