Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors

We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent variable as additional explanatory variables. Combining the extended Kapoor, Kelejian, and Prucha (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and supplementing the dynamic instruments by lagged and weighted exogenous variables as suggested by Kelejian and Robinson (1993) yields new spatial dynamic panel data estimators. The performance of these spatial dynamic panel data estimators is in- vestigated by means of Monte Carlo simulations. We show that differences in bias as well as root mean squared error between spatial GMM estimates and corresponding GMM estimates in which spatial error correlation is ignored are small.

[1]  A. Cameron,et al.  Microeconometrics: Methods and Applications , 2005 .

[2]  J. Paul Elhorst.  Serial and spatial error correlation , 2008 .

[3]  David Roodman,et al.  A Note on the Theme of Too Many Instruments , 2008 .

[4]  Harry H. Kelejian,et al.  A suggested method of estimation for spatial interdependent models with autocorrelated errors, and an application to a county expenditure model , 1993 .

[5]  Harvey S. Rosen,et al.  Budget spillovers and fiscal policy interdependence: Evidence from the states , 1993 .

[6]  Madina Kukenova,et al.  Spatial Dynamic Panel Model and System GMM: A Monte Carlo Investigation , 2008 .

[7]  Lung-fei Lee,et al.  Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large , 2008 .

[8]  R. Blundell,et al.  Initial Conditions and Moment Restrictions in Dynamic Panel Data Models , 1998 .

[9]  John D. Wilson.  Theories of Tax Competition , 1999, National Tax Journal.

[10]  T. Beck,et al.  Stock Markets, Banks, and Growth: Panel Evidence , 2004 .

[11]  M. Pesaran,et al.  Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods , 2002 .

[12]  Bruce A. Blonigen,et al.  FDI in Space: Spatial Autoregressive Relationships in Foreign Direct Investment , 2004, World Scientific Studies in International Economics.

[13]  M. Nerlove,et al.  Biases in dynamic models with fixed effects , 1988 .

[14]  L. Anselin,et al.  Measuring the benefits of air quality improvement: a spatial hedonic approach , 1998 .

[15]  Michael Pfaffermayr,et al.  An unbalanced spatial panel data approach to US state tax competition , 2005 .

[16]  M. D. Ugarte,et al.  Introduction to Spatial Econometrics , 2011 .

[17]  Stan Hurn.  Panel Data Econometrics , 2010 .

[18]  J. Paul Elhorst,et al.  Unconditional maximum likelihood estimation of dynamic models for spatial panels , 2003 .

[19]  Harald Badinger,et al.  Regional Convergence in the European Union, 1985- 1999: A Spatial Dynamic Panel Analysis , 2004 .

[20]  L. Anselin.  Spatial Econometrics: Methods and Models , 1988 .

[21]  Hendrik Vrijburg,et al.  Consumption tax competition among governments: Evidence from the United States , 2007 .

[22]  J. LeSage.  Spatial Econometrics , 1998 .

[23]  Mudit Kapoor,et al.  Panel data models with spatially correlated error components , 2007 .

[24]  Badi H. Baltagi,et al.  Testing for Serial Correlation, Spatial Autocorrelation and Random Effects , 2004 .

[25]  J. Brueckner.  Strategic Interaction Among Governments: An Overview of Empirical Studies , 2003 .