Econometrics II


This module on topics in time series econometrics introduces some key topics in economics and finance. It is designed to help students understand and apply several important contributions in Dynamic Panel data econometrics. By the end of the course, students should be able to analyze economic problems using rigorous econometric techniques. This course will emphasize solid foundations and major empirical applications with real data.

General characterization





Responsible teacher

Paulo M. M. Rodrigues


Weekly - Available soon

Total - Available soon

Teaching language



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Arellano, M (2003) Panel Data Econometrics, Oxford University Press.
Arellano, M. and O. Bover (1995): Another Look at the Instrumental Variable Estimation of Error Components Models, Journal of Econometrics 68, 29-51.
Bai, J. (2003) Inferential Theory for Factor Models of Large Dimensions,
Econometrica 71(1) 135-172.
Bai, J. (2009) Panel Data models with interactive fixed effects, Econometrica 77(4) 1229-1279.
Bai, J & Ng, S (2002) Determining the number of factors in approximate factor models, Econometrica 70, 191-221.
Bai, J & Ng, S (2004) A PANIC attack on unit roots and cointegration, Econometrica
72(4) 1127-1178.
Baldwin, R.E. and D. Taglioni (2006): Gravity for Dummies and Dummies for Gravity Equations, NBER Working Paper 12516.
Baltagi, B.H. (2008) Econometric Analysis of Panel Data, 4th edition New  York: Wiley.
Baltagi B H, G Bresson & A Pirotte (2003) Fixed effects, random effects or Hausman- Taylor? A pre-test estimator, Economics Letters 79 p361-369.
Bernanke, B.S. J. Boivin & P. Eliasz (2005) Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) approach, Quarterly Journal of Economics 387-422.
Bick, A., Kremer, S., Nautz, D. (2013) Inflation and Growth: New Evidence From a Dynamic Panel Threshold Analysis. Empirical Economics 44: 861-878.
Binder, M., C. Hsiao & M.H. Pesaran (2005) Estimation and Inference in Short Panel Vector Autoregression with Unit Roots and Cointegration, Econometric Theory  21 (4) 795-837.
Blundell, R. and S. Bond (1998): Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics 87, 115-143.
Breitung J. & S. Das (2008) Testing for unit roots in panels with a factor structure,
Econometric Theory 24, p88-106.
Bruno, G.S.F. (2005) Approximating the Bias of the LSDV estimator for dynamic unbalanced panel data models, Economics Letters 87, 361-366.
Bun, M.J.G. & M.A. Caree (2006) Bias-corrected estimation in dynamic panel data models with heteroskedasticity, Economics Letters 92, p220-227.
Bussiere, M. A. Chudik & A. Mehl (2011) Does the Euro make a difference: spatio- temporal transmission of global shocks to real effective exchange rates in an infinite VAR, ECB working paper 1292.
Chudik, A. M.H. Pesaran & E Tosetti (2011) Weak and strong cross- section dependence and estimation of large panels, Econometrics Journal 14, C45-C90.
Dang, V.A., M. Kim and Y. Shin (2014): In search of robust methods for dynamic  panel data models in empirical corporate finance, mimeo. University of York.
Favero, C.A. M. Marcellini & F. Neglia (2005) Principal Components at Work: The empirical analysis of monetary policy with large data sets, Journal of Applied Econometrics 20, 603-620.
Fedderke, J., Y. Shin and P. Vaze (2012) Trade, Technology and Wage Inequality in the South African Manufacturing. Oxford Bulletin of Economics and Statistics 74:
Hall S, S Lavarova & G Urga (1999) A principal components analysis of common stochastic trends in heterogeneous panel data: some Monte Carlo Evidence, Oxford Bulletin of Economics and Statistics 61, 749-767.
Harris, R.D.F. & E. Tzavalis (1999a) Inference for Unit Roots in Dynamic Panels Where the Time Dimension is fixed, Journal of Econometrics 91, 201-226.
Hsiao, C. (2003) Analysis of Panel Data, 2/e, Cambridge: Cambridge University Press. Im, K.S., M.H. Pesaran & Y. Shin (2003) Testing for unit roots in heterogenous panels,
Journal of Econometrics 115, 53-74.
Kapetanios, G. (2007) Dynamic Factor Extraction of Cross-sectional Dependence in panel unit root tests, Journal of Applied Econometrics 22, p313-338.
Lagana G. & A. Mountford (2005) Measuring Monetary Policy in the UK: A Factor- Augmented Vector Autoregression Model Approach, Manchester School Supplement, 77-98.
Mitchell, J., K. Mouratis & M.Weale (2005) An assessment of factor based economic indicators: a comparison of factor and regression based preliminary estimates of euro-area GDP growth, NIESR.
Nickell, S. (1981) Biases in Dynamic Models with Fixed Effects, Econometrica 49, 1417-1426.
Onatski, Alexei (2009) Testing hypotheses about the number of factors in large factor models, Econometrica 77(5), 1447-1479.
Pesaran, M.H. (2006) Estimation and Inference in Large Heterogeneous Panels with a multifactor error structure, Econometrica 74(4) 967-1012.
Pesaran, M.H. (2013) Testing Weak Cross-Sectional Dependence in Large Panels, Cambridge Working Paper 1208.
Pesaran, M.H., Y. Shin & R.P. Smith (1999) Pooled Mean Group Estimation of Dynamic Heterogenous Panels, Journal of the American Statistical Association 94, 621-634.
Pesaran, M.H. & R.P. Smith (1995) Estimating Long-run relationships from Dynamic Heterogenous Panels, Journal of Econometrics 68, 79-113.
Robertson, D. & J. Symons (1992) Some Strange Properties of Panel Data Estimators,
Journal of Applied Econometrics 7, 175-89.
Serlenga, L. and Y. Shin (2013): "The Euro Effect on Intra-EU Trade: Evidence from the Cross Sectionally Dependent Panel Gravity Models," mimeo. University of York.
Smith R.P & A. Fuertes (2012) Panel Time-Series. cemmap course

Teaching method

While lectures cover the core material, it is important that students supplement classroom time with pre-class preparation, through independent study. Background reading is strongly recommended.

Evaluation method

Students will be assessed on one assignment (40%) and a final exam (60%).

Subject matter

1.    Estimation of linear panel data models
2.    Specification tests for panel data models
3.    Estimation of autocorrelated panel data models
4.    Instrumental variables estimation and Hausman-Taylor models
5.    Dynamic panel data models
6.    Panel VAR (if time allows)


Programs where the course is taught: