Spatial and Spatio-temporal Engle-Granger representations, Networks and Common Correlated Effects (original) (raw)
Panel data inference under spatial dependence
2010
This paper focuses on inference based on the usual panel data estimators of a one-way error component regression model when the true specification is a spatial error component model. Among the estimators considered, are pooled OLS, random and fixed effects, maximum likelihood under normality, etc. The spatial effects capture the cross-section dependence, and the usual panel data estimators ignore this dependence. Two popular forms of spatial autocorrelation are considered, namely, spatial auto-regressive random effects (SAR-RE) and spatial moving average random effects (SMA-RE). We show that when the spatial coefficients are large, test of hypothesis based on the usual panel data estimators that ignore spatial dependence can lead to misleading inference.
A Generalized Spatial Panel Data Model with Random Effects
Econometric Reviews, 2013
This paper proposes a generalized panel data model with random effects and first-order spatially autocorrelated residuals that encompasses two previously suggested specifications. The first one is described in Anselin's (1988) book and the second one by Kapoor, Kelejian, and Prucha (2007). Our encompassing specification allows us to test for these models as restricted specifications. In particular, we derive three LM and LR tests that restrict our generalized model to obtain (i) the Anselin model, (ii) the Kapoor, Kelejian, and Prucha model, and (iii) the simple random effects model that ignores the spatial correlation in the residuals. For two of these three tests, we obtain closed form solutions and we derive their large sample distributions. Our Monte Carlo results show that the suggested tests are powerful in testing for these restricted specifications even in small and medium sized samples.
The 3rd World Conference of the Spatial Econometrics …, 2009
This paper derives several Lagrange Multiplier statistics and the corresponding likelihood ratio statistics to test for spatial autocorrelation in a fixed effects panel data model. These tests allow discriminating between the two main types of spatial autocorrelation which are relevant in empirical applications, namely endogenous spatial lag versus spatially autocorrelated errors. In this paper, five different statistics are suggested. The first one, the joint test, detects the presence of spatial autocorrelation whatever its type. Hence, it indicates whether specific econometric estimation methods should be implemented to account for the spatial dimension. In case they need to be implemented, the other four tests support the choice between the different specifications, i.e. endogenous spatial lag, spatially autocorrelated errors or both. The first two are simple hypothesis tests as they detect one kind of spatial autocorrelation assuming the other one is absent. The last two take into account the presence of one type of spatial autocorrelation when testing for the presence of the other one. We use the methodology developed in to set up and estimate the general likelihood function. Monte Carlo experiments show the good performance of our tests. Finally, they are applied to the Feldstein-Horioka puzzle. They indicate a misspecification of the investment-saving regression due to the omission of spatial autocorrelation. The traditional saving-retention coefficient is shown to be upward biased. In contrast our results favor capital mobility. JEL Classification: C12, C21, C23 LM ρ|λ and LR ρ|λ test for the presence an endogenous spatial lag when spatially autocorrelated errors are included in the specification.
Testing for spatial autocorrelation in a fixed effects panel data model
Regional Science and Urban Economics, 2010
The aim of this paper is to assess the relevance of spatial autocorrelation in a fixed effects panel data model and in the affirmative, to identify the most appropriate spatial specification as this appears to be a crucial point from the modeling perspective of interactive heterogeneity. Several LM test statistics as well as their LR counterparts, which allow discriminating between endogenous spatial lag versus spatially autocorrelated errors, are therefore proposed. Monte Carlo experiments show their good finite sample performance. Finally, an empirical application is provided in the framework of the well-known Feldstein-Horioka puzzle.
Spatial dynamic panel data models with random effects
2012
We develop a general space–time filter applied to panel data models in order to control for heterogeneity as well as both time and spatial dependence. Treatment of initial period observations is analyzed when the number of time periods is small. A second issue relates to a restriction implied by the filter specification on the space–time cross-product term that can greatly
Spatial Panel Models and Common Factors
Handbook of Regional Science, 2019
This chapter provides a survey of the existing literature on spatial panel data models. Both static, dynamic, and dynamic models with common factors will be considered. Common factors are modeled by time-period fixed effects, crosssectional averages, or principal components. It is demonstrated that spatial econometric models that include lags of the dependent variable and of the independent variables in both space and time provide a useful tool to quantify the magnitude of direct and indirect effects, both in the short term and long term. Direct effects can be used to test the hypothesis as to whether a particular variable has a significant effect on the own dependent variable, and indirect effects to test the hypothesis whether spatial spillovers affect the dependent variable of other units. To illustrate these models, their effects estimates, and the impact of the type of common factors, a demand model for cigarettes is estimated based on panel data from 46 U.S. states over the period 1963 to 1992.
Generalized Moments Estimation of a Spatially Correlated Panel Data Model
1999
This paper considers estimation of a panel data model with disturbances that are autocorrelated across cross sectional units. It is assumed that the disturbances are spatially correlated, based on some geographic or economic proximity measure. If the time dimension of the data is large, feasible and efficient estimation proceeds by random effects. For the case where the time dimension is small (the usual panel data case), we develop a generalized moments estimation approach that is a generalization of a cross sectional model due to Kelejian and Prucha (1999). We apply this approach in a stochastic frontier framework to a panel of Indonesian rice farms where spatial correlations are based on geographic proximity, altitude and weather. The correlations represent productivity shock spillovers across the rice farms in different villages on the island of Java. Using a Moran I test statistic, we demonstrate empirically that productivity shock spillovers may exist in this (and perhaps othe...
A Solution for Absent Spatial Data: The Common Correlated Effects Estimator
International Regional Science Review, 2020
Informed regional policy needs good regional data. As regional data series for key economic variables are generally absent whereas national-level time series data for the same variables are ubiquitous, we suggest an approach that leverages this advantage. We hypothesize the existence of a pervasive “common factor” represented by the national time series that affects regions differentially. We provide an empirical illustration in which national FDI is used in place of panel data for FDI, which are absent. The proposed methodology is tested empirically with respect to the determinants of regional demand for housing. We use a quasi-experimental approach to compare the results of a “common correlated effects” (CCE) estimator with a benchmark case when absent regional data are omitted. Using three common factors relating to national population, income and housing stock, we find mixed support for the common correlated effects hypothesis. We conclude by discussing how our experimental desi...