Spatial Dynamic Panel Data Model Research Articles

Spatial Panel VAR and Application to Forecast Influenza Incidence Rates of US States

In this paper, we propose a spatial panel vector autoregression, SPVAR(p), which generalizes the spatial dynamic panel data (SDPD) models with individual fixed effects to allow for multivariate vector observations and higher order lags. The regression residuals can be independently or spatial autoregressively (SAR) distributed. We investigate in the issues of identification and estimation via profile QMLE. We prove that the QML estimators are consistent and asymptotically normally distributed when both N and T are large. Then, we analyze the bias and bias correction. The finite sample performance of the profile QMLE is evaluated by Monte Carlo simulations. Information criteria and likelihood ratio test are used to select the lag orders and test the residual specifications in the SPVAR(p). The SPVAR(p) is then used to estimate and forecast the incidence rates of influenza like illness (ILI) in the United States. We combine weekly ILI incidence rate data by Google Flu Trends and the office of Centers for Disease Control and Prevention (CDC) into a vector measure for 48 US continental states, and then we use the vector observations to estimate the SPVAR(p) model, where the spatial weight matrix is the row normalized state geographical adjacency matrix. We use state population density, temperature, and precipitation as predictors. We find that SPVAR(p) achieves satisfactory in-sample estimates and out-of-sample forecasts based on Google Flu Trends and CDC ILI incidence rate data. The estimated SPVAR(p) is compared against univariate SDPD(p) model of Google Flu Trends ILI incidence rates. We also conduct an impulse response analysis of the dynamic diffusions of California flu shock.

Province-level convergence of China’s carbon dioxide emissions

Abstract This study offers a unique contribution to the literature by investigating the convergence of province-level carbon dioxide emission intensities among a panel of 30 provinces in China over the period 1990–2010. We use a novel, spatial dynamic panel data model to evaluate an empirically testable hypothesis of convergence among provinces. Our results suggest that: (1) CO 2 emission intensities are converging across provinces in China; (2) the rate of convergence is higher with the dynamic panel data model than the cross-sectional regression models; and, (3) province-level CO 2 emission intensities are spatially correlated and the rate of convergence, when controlling for spatial autocorrelation, is higher than with the non-spatial models.

Estimation of partially specified dynamic spatial panel data models with fixed-effects

This paper studies estimation of a partially specified spatial dynamic panel data regression with fixed-effects. Under the assumption of strictly exogenous regressors and strictly exogenous spatial weighting matrix, the model is estimated by 2SLS method aided by the sieve method and through the instrumental variable. Under some sufficient conditions, the proposed estimator for the finite dimensional parameter is shown to be root-N consistent and asymptotically normally distributed. The proposed estimator for the unknown function is shown to be consistent and asymptotically distributed as well, though at a rate slower than root-N. Consistent estimators for the asymptotic variance–covariance matrices of both estimators are provided. The results can be generalized to several spatial weighting matrices and spatial matrix which vary with time. The simulation results suggest that the proposed approach has some practical value.

Do Regions Benefit from Active Labour Market Policies? A Macroeconometric Evaluation Using Spatial Panel Methods

Dauth W., Hujer R. and Wolf K. Do regions benefit from active labour market policies? A macroeconometric evaluation using spatial panel methods, Regional Studies. The effect of active labour market policies on all job seekers in a region is assessed using the variation in the participation in these programmes across regions over time. The starting point is an augmented regional matching function, which is extended by allowing for a heterogeneous search effectiveness parameter for current participants and job seekers who finished a measure recently. A spatial dynamic panel data model is estimated by using a quasi-ML estimation procedure. It is found that job schemes in the non-profit sector, wage subsidies and advancement of apprenticeships significantly increase the regional number of matches.

Local employment growth in the coastal area of Tunisia: spatial filtering approach

Since the mid-1980s, Tunisia has conducted a structural adjustment program characterized by more privatization and economic opening. This transition has created unequal growth in the economic performance and the employment opportunities between coastal and interior regions (inland areas). The 14 January revolution has started as social demands against unequal employment opportunities and corruption. The identification of various factors explaining the employment growth in Tunisia's coastal area is necessary to understand regional disparities. In this study, we attempt to assess the impacts of institutional factors and industrial structures on manufacturing employment growth. We seek to answer the following key question: Why does employment grow in one region and not in other? Using panel data on five manufacturing sectors associated to 138 Tunisian coastal small localities (delegations) along six years (2002–2007), we will give some empirical evidence on regional employment growth. We use a dynamic spatial panel data model in order to consider the spatial and temporal effects in the analysis of the local employment growth. Our results show that spillovers have been found to be significantly effective only within a range of 15 km for high-tech industries and 50 km for low-tech. Our econometric result stand that agglomeration and diversity have a positive effect on the local employment manufacturing growth. However, competition has a negative effect. In addition, our results show that industrial zones do not increase employment.

Efficient GMM estimation of spatial dynamic panel data models with fixed effects

In this paper we derive the asymptotic properties of GMM estimators for the spatial dynamic panel data model with fixed effects when n is large, and T can be large, but small relative to n. The GMM estimation methods are designed with the fixed individual and time effects eliminated from the model, and are computationally tractable even under circumstances where the ML approach would be either infeasible or computationally complicated. The ML approach would be infeasible if the spatial weights matrix is not row-normalized while the time effects are eliminated, and would be computationally intractable if there are multiple spatial weights matrices in the model; also, consistency of the MLE would require T to be large and not small relative to n if the fixed effects are jointly estimated with other parameters of interest. The GMM approach can overcome all these difficulties. We use exogenous and predetermined variables as instruments for linear moments, along with several levels of their neighboring variables and additional quadratic moments. We stack up the data and construct the best linear and quadratic moment conditions. An alternative approach is to use separate moment conditions for each period, which gives rise to many moments estimation. We show that these GMM estimators are nT consistent, asymptotically normal, and can be relatively efficient. We compare these approaches on their finite sample performance by Monte Carlo.

The Impact of Interaction Effects among Neighbouring Countries on Financial Liberalization and Reform: A Dynamic Spatial Panel Data Approach

Using data from Abiad et al. (2008), we estimate a dynamic spatial panel data model with country and time-period fixed effects to test for spatial cointegration in the financial liberalization index. Parameter estimates are obtained by reformulating the model in spatial first-differences. Next, by considering the error correction model representation of the dynamic spatial panel data model, we examine financial reform interaction effects and the extent to which a change in a single explanatory variable in a particular country affects financial reform in other countries. Finally, by comparing the performance of different specifications of the spatial weights matrix describing the spatial arrangement of the countries in the sample, we show that the popular regional leader matrix must be rejected in favour of an inverse distance matrix with a cut-off point of 3,000 km. RÉSUMÉ En utilisant des données de Abiad et al. (2008), nous procédons à l'estimation d'un modèle de données spatiales dynamiques, avec effets fixes sur le plan des pays et des périodes, pour tester la co-intégration spatiale dans l'indice de libéralisation financière. On obtient des estimations paramétriques en reformulant le modèle dans des premiers éléments de différence spatiale. Ensuite, en examinant la représentation du modèle de rectification des erreurs du modèle de données spatiales dynamiques, nous examinons les effets d'une interaction de réformes financières, et la mesure dans laquelle un changement d'une variable explicative unique dans un certain pays affecte les réformes financières dans d'autres pays. Enfin, en comparant les performances de différentes spécifications de la matrice des pondérations spatiales décrivant la disposition spatiale des pays de l’échantillon, nous montrons qu'il est nécessaire de rejeter la matrice leader régionale populaire au profit d'une matrice de distance inverse avec un point limite de 3 000 km. EXTRACTO Utilizando datos procedentes de Abiad et al. (2008), estimamos un modelo de datos de panel dinámico espaciales y los efectos fijos de período de tiempo y país para comprobar la cointegración espacial en el índice de liberalización financiera. Las estimaciones de parámetros se obtienen reformulando el modelo en las primeras diferencias espaciales. A continuación, considerando la representación del modelo de corrección de errores del modelo de datos de panel dinámico espaciales, examinamos los efectos de la interacción de la reforma financiera y el punto hasta el que un cambio en una sola variable explicativa en un país particular afecta a la reforma financiera en otros países. Finalmente, comparando el rendimiento de diferentes especificaciones de la matriz de ponderaciones espaciales que describen la disposición espacial de los países de la muestra, mostramos que la matriz de líderes regionales populares debe ser rechazada a favor de una matriz de distancia inversa con un punto de corte de 3000km. 摘要:我们使用阿比亚德等人(2008)提供的数据估算带有国家及时间段恒定效应的动态空间面板数据模型 ' 以测试金融自由化指数中的区域整合程度 。通过以一阶差分对模型进行重建 。接下来 ' 通过分析动态空间面板数据模型的纠错模型表示 ' 我们考查了金融改革互动效应 ' 以及某一特定国家中单个解释变量的变化对其他国家金融改革影响的程度 。最后 ' 通过比较用来描述样本中国家和地区分布的不同规格的地区权重矩阵性能, 我们得出 ' 为了获得分界点为3,000km的逆向距离矩阵, 必须丢弃常用的地区领导人矩阵。

Estimating and Forecasting with a Dynamic Spatial Panel Data Model*

AbstractThis study focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006), a dynamic spatial generalized method of moments (GMM) estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the spatial autoregressive (SAR) error model. The main idea is to mix non‐spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non‐spatial estimators and illustrate our approach with an application to new economic geography.

Spatial dynamic panel data models with random effects

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 simplify interpretation of model estimates as well as the estimation procedure. An applied illustration of the method is provided using a Solow growth model. The application shows that the theoretical restriction implied for the cross-product term in our space–time filter specification is consistent with this particular dynamic space–time panel data set.

Migration and Regional Unemployment in Italy

Does interregional migration equilibrate regional labor market performances? We answer this question focusing on regional unemployment dynamics in Italy over the 1995-2006 period, when a strong flow of out-migration from the South to the North occurred. Using System-GMM estimators for spatial dynamic panel data models in the presence of endogenous variables, the empirical analysis documents that past migration flows exert a negative effect on current regional unemployment. By falsifying the common wisdom, our results thus indicate that migration flows are likely to magnify spatial disparities in unemployment rates rather than mitigate them.

QML Estimation of Spatial Dynamic Panel Data Models with Time Varying Spatial Weights Matrices

This paper investigates the quasi-maximum likelihood estimation of spatial dynamic panel data models where spatial weights matrices can be time varying. We find that QML estimate is consistent and asymptotically normal. We investigate marginal impacts of explanatory variables in this system via space–time multipliers. Monte Carlo results are reported to investigate the finite sample properties of QML estimates and marginal effects. When spatial weights matrices are substantially varying over time, a model misspecification of a time invariant spatial weights matrix may cause substantial bias in estimation. Slowly time varying spatial weights matrices would be of less concern. RÉSUMÉ la présente communication se penche sur l'estimation du quasi maximum de vrai semblance de modèles de données du groupe des dynamiques spatiales, où les matrices de poids spatiales peuvent varier en fonction du temps. Nous relevons que l'estimation de QML est homogène et normale sur un plan asymptotique. Nous nous penchons sur des impacts marginaux de variables causales dans ce système, par le biais de multiplicateurs spatio-temporels. Des résultats Monte Carlo sontfournis pour l'examen d’échantillons finis d'estimations QML et d'effets marginaux. Lorsque les matrices de poids spatiales varient de façon substantielle avec le temps, une erreur de spécification de modèle d'une matrice de poids spatiale ne variant pas avec le temps risquerait de fausser sensiblement les estimations. Les matrice de poids spatiale variant avec le temps auraientune importance moindre. RESUMEN Este estudio investiga la estimación casi-máxima de probabilidad de semejanza de modelos dinámicos de datos de panel en donde las matrices ponderadas espaciales pueden variar con el tiempo. Indicamos que la estimación QML es constante y asimptóticamente normal. Investigamos impactos marginales de variables explicativas en este sistema mediante multiplicadores espacio-temporales. Se informan los resultados de Monte Carlo para investigar las propiedades de muestra finitas de las estimaciones QML y los efectos marginales. Cuando las matrices ponderadas espaciales varían considerablemente en el tiempo, los errores de especificación del modelo para una matriz ponderada espacial invariable en el tiempopodrían causar una considerable parcialidad en la estimación. Las matrices de pesos espaciales variables lentos serían menos preocupantes.

Dynamic spatial panels: models, methods, and inferences

This paper provides a survey of the existing literature on the specification and estimation of dynamic spatial panel data models, a collection of models for spatial panels extended to include one or more of the following variables and/or error terms: a dependent variable lagged in time, a dependent variable lagged in space, a dependent variable lagged in both space and time, independent variables lagged in time, independent variables lagged in space, serial error autocorrelation, spatial error autocorrelation, spatial-specific and time-period-specific effects. The survey also examines the reasoning behind different model specifications and the purposes for which they can be used, which should be useful for practitioners.

Spatial Dynamic Panel Data Models with Random Effects

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 an implied restriction on the space-time cross-product term that can greatly simplify the estimation procedures. An applied illustration of the method is provided using a Solow growth model. The application shows that the theoretical restriction implied for the cross-product term in our space-time filter specification is consistent with this particular dynamic space-time panel data set.

The impact of spatial elements on the forecasting of Spanish labour series

In this paper, we analyse the ability of a dynamic spatial panel data model without explanatory variables to explain a variable of interest, in this case employment in the fifty Spanish provinces. The best model is a dynamic fixed effect with a spatial lag structure in an equation estimated through the unconditional ML procedure. Predictions derived from this selected model are compared with those derived from fifty seasonal ARIMA models that also treat outlier observations. The results indicate that forecasts derived from a single estimated spatial panel data model are as accurate as those derived from the estimation of fifty seasonal ARIMA models. This shows that spatial panel data models play an important role in forecasting.

ESTIMATION OF UNIT ROOT SPATIAL DYNAMIC PANEL DATA MODELS

This paper examines the asymptotics of the QMLE for unit root dynamic panel data models with spatial effect and fixed effects. We consider a unit root dynamic panel data model with spatially correlated disturbances and a unit root spatial dynamic panel data model. For both models the estimate of the dynamic coefficient is$\root \of {nT^3 }$consistent and the estimates of other parameters are$\root \of {nT}$consistent, and all of them are asymptotically normal. For the latter model the sum of the contemporaneous spatial effect and dynamic spatial effect converges at$\root \of {nT^3 }$rate. We also propose a bias-correction procedure so that the asymptotic biases of those estimates are eliminated as long asn/T3→ 0.