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.Dynamic panel models;spatial lag;spatial error;GMM estimation

Consumption tax competition among governments: Evidence from the United States

The paper contributes to a small but growing literature that estimates tax re- action functions of governments competing with other governments. We analyze consumption tax competition between US states, employing a panel of state-level data for 1977-2003. More specifically, we study the impact of a state's spatial characteristics|that is, its size, geographic position, and border length on the strategic interaction with its neighbors. For this purpose, we calculate for each state an average effective consumption tax rate, which covers both sales and excise taxes. In addition, we pay attention to dynamics by including lagged dependent variables in the tax reaction function. We find overwhelming evidence for strategic interaction among state governments, but only partial support for the effect of spatial character- istics on tax setting. Tax competition seems to have lessened in the 1990s compared to the early 1980s.

GMM Estimation of Fixed Effects Dynamic Panel Data Models with Spatial Lag and Spatial Errors

We extend the three-step generalized methods of moments (GMM) approach of Kapoor et al. (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 et al. (2007) approach with the dynamic panel data model GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) and specifying moment conditions for various time lags, spatial lags, and sets of exogenous variables yields new spatial dynamic panel data estimators. We prove their consistency and asymptotic normality for a large number of spatial units N and a xed small number of time periods T. Monte Carlo simulations demonstrate that the root mean squared error of spatially corrected GMM estimates|which are based on a spatial lag and spatial error correction|is generally smaller than that of corresponding spatial GMM estimates in which spatial error correlation is ignored. We show that the spatial Blundell-Bond estimators outperform the spatial Arellano-Bond estimators.Dynamic panel models;spatial lag;spatial error;GMM estimation

A Kleene theorem for polynomial coalgebras

For polynomial functors G, we show how to generalize the classical notion of regular expression to G-coalgebras. We introduce a language of expressions for describing elements of the final G-coalgebra and, analogously to Kleene’s theorem, we show the correspondence between expressions and finite G-coalgebras