Estimation and Testing of Higher-Order Spatial Autoregressive Panel Data Error Component Models

This paper develops an estimator for higher-order spatial autoregressive panel data error component models with spatial autoregressive disturbances, SARAR(R,S). We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define a generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their joint asymptotic distribution, and provide Monte Carlo evidence on their small sample performance

Regional Unemployment Forecasting Using Structural Component Models With Spatial Autocorrelation

Labour-market policies are increasingly being decided on a regional level. This implies that institutions have an increased need for regional forecasts as a guideline for their decision-making process. Therefore, we forecast regional unemployment in the 176 German labour market districts. We use an augmented structural component (SC) model and compare the results from this model with those from basic SC and autoregressive integrated moving average (ARIMA) models. Basic SC models lack two important dimensions: First, they only use level, trend, seasonal and cyclical components, although former periods of the dependent variable generally have a significant influence on the current value. Second, as spatial units become smaller, the influence of “neighbour-effects†becomes more important. In this paper we augment the SC model for structural breaks, autoregressive components and spatial autocorrelation. Using unemployment data from the Federal Employment Services in Germany for the period December 1997 to August 2005, we first estimate basic SC models with components for structural breaks and ARIMA models for each spatial unit separately. In a second stage, autoregressive components are added into the SC model. Third, spatial autocorrelation is introduced into the SC model. We assume that unemployment in adjacent districts is not independent for two reasons: One source of spatial autocorrelation may be that the effect of certain determinants of unemployment is not limited to the particular district but also spills over to neighbouring districts. Second, factors may exist which influence a whole region but are not fully captured by exogenous variables and are reflected in the residuals. We test the quality of the forecasts from the basic models and the augmented SC model by ex-post-estimation for the period September 2004 to August 2005. First results show that the SC model with autoregressive elements and spatial autocorrelation is superior to basic SC and ARIMA models in most of the German labour market districts.

Persistence of Regional Unemployment: Application of a Spatial Filtering Approach to Local Labour Markets in Germany

The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analysing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region-specific information (e.g., the endowment of natural resources, or the size of the ‘home market’) that is usually incorporated in the fixed effects parameters. The advantages of our proposed procedure are that the spatial filter, by incorporating region-specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time-stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive parameters estimated for unemployment data for German NUTS-3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates.

Persistence of Regional Unemployment: Application of a Spatial Filtering Approach to Local Labour Markets in Germany

The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analysing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region-specific information (e.g., the endowment of natural resources, or the size of the ‘home market’) that is usually incorporated in the fixed effects parameters. The advantages of our proposed procedure are that the spatial filter, by incorporating region-specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time-stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive parameters estimated for unemployment data for German NUTS-3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates.unemployment persistence, dynamic panel, hysteresis, spatial filtering, fixed effects

Persistence of regional unemployment : Application of a spatial filtering approach to local labour markets in Germany

"The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analysing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region-specific information (e.g., the endowment of natural resources, or the size of the 'home market') that is usually incorporated in the fixed effects parameters. The same argument applies for the spatial filter modelling of the heterogenous dynamics. The advantages of our proposed procedure are that the spatial filter, by incorporating region-specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time-stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive parameters estimated for unemployment data for German NUTS-3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates." (Author's abstract, IAB-Doku) ((en))Arbeitslosenquote, Persistenz, Schätzung, regionale Disparität

Spatial and non spatial approaches to agricultural convergence in Europe

The paper starts from the critiques to the Barro-style methodology for convergence analysis with the aim of reviewing the econometric approaches for testing spatial effects in convergence process related to both cross sectional and panel data regressions, a framework that is applied to a sample of 80 regions of the EU-15 at NUTS-2 level over the time period from 1980 to 2007. The empirical analysis compares results from approaches and, at the same time, provides empirical evidence from techniques that are now widely recognised in the understanding of regional growth and the influence of space but never or rarely applied to the agricultural context. Results point out the complexity of the process of agricultural regional convergence in Europe that cannot be adequately captured by the non-spatial growth regression models that have dominated the research and policy debate in this field. Evidence for convergence and spatial dependence emerges especially when estimations refers to spatial panel models while the effects of spatial heterogeneity and the existence of convergence clubs come out from the geographically weighted regression approach. The paper represents a point of departure for further researches in this field whose most important directions are underlined.Convergence, Spatial approaches, Non spatial approaches, Community/Rural/Urban Development, C21, C33, Q19.,

Institutions, geography, trade, and income per capita: A spatial-simultaneous equation approach

This paper tests a series of prominent hypotheses regarding how institutions, geography, and trade interact to influence income per capita using a novel spatial econometric approach to control for both spillovers among neighboring countries and spatially correlated omitted variables. Simultaneous equations are used to identify alternative channels through which country characteristics might affect income through trade and institutions, and then to test the robustness of those effects. Evidence indicated that both institutions and trade influence growth. Geographical factors such as whether a country is landlocked and its distance to the equator influence income, but only through trade. Data covering 95 countries across the world from 1960 through 2002 was used to construct a pooled dataset of 5-year averages (9 in all) centered on 1960, 1965, and so on through 2000. Both limited and full information estimators, partly based on a generalized moments (GM) estimator for spatial autoregressive coefficients, were used. These allow for spatial error correlation, correlation across equations, and the presence of spatially lagged dependent variables.economic growth, Geography, Institutions, simultaneous equations, spatial econometrics, trade,

Maximum Likelihood Estimation and Lagrange Multiplier Tests for Panel Seemingly Unrelated Regressions with Spatial Lag and Spatial Errors: An Application to Hedonic Housing Prices in Paris

This paper proposes maximum likelihood estimators for panel seemingly unrelated regressions with both spatial lag and spatial error components. We study the general case where spatial effects are incorporated via spatial errors terms and via a spatial lag dependent variable and where the heterogeneity in the panel is incorporated via an error component specification. We generalize the approach of Wang and Kockelman (2007) and propose joint and conditional Lagrange Multiplier tests for spatial autocorrelation and random effects for this spatial SUR panel model. The small sample performance of the proposed estimators and tests are examined using Monte Carlo experiments. An empirical application to hedonic housing prices in Paris illustrates these methods. The proposed specification uses a system of three SUR equations corresponding to three types of flats within 80 districts of Paris over the period 1990-2003. We test for spatial effects and heterogeneity and find reasonable estimates of the shadow prices for housing characteristics.spatial lag, panel spatial dependence, maximum likelihood, Lagrange multiplier tests, hedonic housing prices, spatial error, SUR

Thirty Years of Spatial Econometrics

In this paper, I give a personal view on the development of the field of spatial econometrics during the past thirty years. I argue that it has moved from the margins to the mainstream of applied econometrics and social science methodology. I distinguish three broad phases in the development, which I refer to as preconditions, takeoff and maturity. For each of these phases I describe the main methodological focus and list major contributions. I conclude with some speculations about future directions.

Persistent Disparities in Regional Unemployment: Application of a Spatial Filtering Approach to Local Labour Markets in Germany

The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analysing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region-specific information (e.g., the endowment of natural resources, or the size of the ‘home market’) that is usually incorporated in the fixed effects coefficients. The advantages of our proposed procedure are that the spatial filter, by incorporating region-specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time-stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. In the paper we present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive coefficients estimated for unemployment data for German NUTS-3 regions.unemployment persistence, dynamic panel, hysteresis, spatial filtering, fixed effects