Spatial econometric analysis of spatial general equilibrium

A structural spatial econometric model for nine regions of Israel is estimated using non-stationary spatial panel data during 1987–2015. The model focuses on the relation between regional markets in labour, housing and capital when there is imperfect internal migration between regions, when capital is imperfectly mobile between regions, and when building contractors operate across regions. Since the regional panel data are non-stationary, the econometric methodology is based on spatial panel cointegration. The estimated model is used to simulate the temporal and spatial propagation of regional shocks induced, for example, by regional policy (land for housing, regional investment grants). Impulse responses are temporally and spatially state dependent. They are also highly persistent because of longevity in housing and capital.     

Structural FECM: Cointegration in large‐scale structural FAVAR models

Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a factor‐augmented vector autoregressive (FAVAR) model is positively related to the strength of the error correction mechanism and the cross‐section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified permanent real (productivity) and monetary policy shocks.     

The multi-country transmission of sovereign and banking risk: a spatial vector autoregressive approach

ABSTRACT

This paper develops a spatial vector autoregressive (SpVAR) model to investigate the transmission of sovereign, banking and corporate default risks among 11 Eurozone countries for the period January 2008–December 2013. The results show that a significant proportion of default risk variation is explained by foreign shocks. However, the cross-border sovereign–bank nexus is statistically significant, but economically moderate. Among the three sectors, shocks to the banking sector play the most critical role. On average, for the 11 countries, a foreign banking shock can explain 7%, 23% and 18% of the forecast error variance of changes in sovereign, banking and corporate credit default swap spreads respectively.     

Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices

This paper considers the estimation and inference of spatial panel data models with heterogeneous spatial lag coefficients, with and without weakly exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of the spatial coefficients are derived. The QML estimators of individual spatial coefficients, as well as their mean group estimators, are shown to be consistent and asymptotically normal. Small‐sample properties of the proposed estimators are investigated by Monte Carlo simulations and results are shown to be in line with the paper's key theoretical findings, even for panels with moderate time dimensions and irrespective of the number of cross‐section units. A detailed empirical application to US house price changes during the 1975–2014 period shows a significant degree of heterogeneity in spatiotemporal dynamics over the 338 Metropolitan Statistical Areas considered.     

Assessing the Transmission of Monetary Policy Using Time‐varying Parameter Dynamic Factor Models*

This article extends the current literature which questions the stability of the monetary transmission mechanism, by proposing a factor‐augmented vector autoregressive (VAR) model with time‐varying coefficients and stochastic volatility. The VAR coefficients and error covariances may change gradually in every period or be subject to abrupt breaks. The model is applied to 143 post‐World War II quarterly variables fully describing the US economy. I show that both endogenous and exogenous shocks to the US economy resulted in the high inflation volatility during the 1970s and early 1980s. The time‐varying factor augmented VAR produces impulse responses of inflation which significantly reduce the price puzzle. Impulse responses of other indicators of the economy show that the most notable changes in the transmission of unanticipated monetary policy shocks occurred for gross domestic product, investment, exchange rates and money.     

Past price ‘memory’ in the housing market: testing the performance of different spatio-temporal specifications

Recent methodological developments provide a way to incorporate the temporal dimension when accounting for spatial effects in hedonic pricing. Weight matrices should decompose the spatial effects into two distinct components: bidirectional contemporaneous spatial connections; and unidirectional spatio-temporal effects from past transactions. Our iterative estimation approach explicitly analyses the role of time in price determination. The results show that both spatio-temporal components should be included in model specification; past transaction information stops contributing to price determination after eight months; and limited temporal friction is exhibited within this period. These findings highlight the decidedly non-linear temporal patterns of such information effects.     

Exploring the international linkages of the euro area: a global VAR analysis

This paper presents a quarterly global model combining individual country vector error‐correcting models in which the domestic variables are related to the country‐specific foreign variables. The global VAR (GVAR) model is estimated for 26 countries, the euro area being treated as a single economy, over the period 1979–2003. It advances research in this area in a number of directions. In particular, it provides a theoretical framework where the GVAR is derived as an approximation to a global unobserved common factor model. Using average pair‐wise cross‐section error correlations, the GVAR approach is shown to be quite effective in dealing with the common factor interdependencies and international co‐movements of business cycles. It develops a sieve bootstrap procedure for simulation of the GVAR as a whole, which is then used in testing the structural stability of the parameters, and for establishing bootstrap confidence bounds for the impulse responses. Finally, in addition to generalized impulse responses, the current paper considers the use of the GVAR for ‘structural’ impulse response analysis with focus on external shocks for the euro area economy, particularly in response to shocks to the US. Copyright © 2007 John Wiley & Sons, Ltd.     

A heterogeneous coefficient approach to the knowledge production function

Past literature has used conventional spatial autoregressive panel data models to relate patent production output to knowledge production inputs. However, research conducted on regional innovation systems points to regional disparities in both regions’ ability to turn their knowledge inputs into innovation and to access external knowledge. Applying a heterogeneous coefficients spatial autoregressive panel model, we estimate region-specific knowledge production functions (KPFs) for 94 NUTS-3 regions in France using a panel covering 21 years from 1988 to 2008 and four high-technology industries. A great deal of regional heterogeneity in the KPF relationship exists across regions, providing new insights regarding spatial spillin and spillout effects between regions.     

均衡性转移支付、税制结构与共同富裕

新发展阶段，要加快实现共同富裕，就需要重视均衡性转移支付和税制结构的调节作用。基于极值熵值法测度的共同富裕综合指数构建面板Tobit模型、空间滞后模型和门槛模型，从均衡性转移支付和税制结构的不同视角，探究上述因素对共同富裕的多重影响，结果发现：从基准回归和空间滞后模型看，共同富裕存在显著的空间溢出效应，均衡性转移支付对共同富裕的直接和空间促进效应尚未体现，以直接税和间接税为主体的税制结构的直接和空间效应显著为正，且均衡性转移支付能够调节税制结构的直接和空间溢出效应。进一步分析发现，均衡性转移支付与共同富裕之间存在由“门槛效应”引起的非线性关系，合理的均衡性转移支付显著促进共同富裕，且随着均衡性转移支付的提升，其促进效应不断减弱。动态空间滞后模型显示，共同富裕滞后一期的直接和空间溢出效应均显著为正。以上研究结论对中国政府优化财税体制、推进共同富裕具有重要启示。     

Does Evidence on Regional Economic Convergence Depend on the Estimation Strategy? Outcomes from Analysis of a Set of NUTS2 EU Regions

Abstract

The regional economic convergence/divergence issue has been discussed extensively recently, but results obtained are not always interpretable unequivocally as a consequence of the different estimation strategies used. As it is widely recognized, the most common theoretical framework applied to measure the speed of economic convergence among countries or regions remains the β-convergence approach, linked to the neoclassical Solow model. There have been many attempts to consider variations of the basic cross-sectional specification ranging from panel data models to Bayesian spatial econometric techniques. The application of spatial econometric methodologies is an essential tool for proper statistical inference on regional data. In this context, the aim of this paper is to connect the different results obtained in the literature. More specifically, we address whether or not evidence on convergence depends upon the estimation strategy, by taking the same set of data and systematically comparing the results obtained from different estimation strategies. The results from a set of NUTS2 EU regions conclude that both the model implied by the cross-sectional analysis and the one referring to the space-time dynamics incorporated in the panel specification point to convergence. The concept of convergence implied is, however, quite different, as demonstrated throughout the paper.     

Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration

Yu et al. (2008) establish asymptotic properties of quasi-maximum likelihood estimators for a stable spatial dynamic panel model with fixed effects when both the number of individuals n and the number of time periods T are large. This paper investigates unstable cases where there are unit roots generated by temporal and spatial correlations. We focus on the spatial cointegration model where some eigenvalues of the data generating process are equal to 1 and the outcomes of spatial units are cointegrated as in a vector autoregressive system. The asymptotics of the QML estimators are developed by reparameterization, and bias correction for the estimators is proposed. We also consider the 2SLS and GMM estimations when T could be small.     

Hedonic Housing Prices in Paris: An Unbalanced Spatial Lag Pseudo‐Panel Model with Nested Random Effects

This paper estimates a hedonic housing model based on flats sold in the city of Paris over the period 1990–2003. This is done using maximum likelihood estimation, taking into account the nested structure of the data. Paris is historically divided into 20 arrondissements, each divided into four quartiers (quarters), which in turn contain between 15 and 169 blocks (îlot, in French) per quartier. This is an unbalanced pseudo?panel data containing 156,896 transactions. Despite the richness of the data, many neighborhood characteristics are not observed, and we attempt to capture these neighborhood spillover effects using a spatial lag model. Using likelihood ratio tests, we find significant spatial lag effects as well as significant nested random error effects. The empirical results show that the hedonic housing estimates and the corresponding marginal effects are affected by taking into account the nested aspects of the Paris housing data as well as the spatial neighborhood effects.Copyright © 2014 John Wiley & Sons, Ltd.     

教育人力资本与区域经济增长的关系研究

目前国内关于人力资本对经济增长作用机制的研究,很少考虑空间依赖性引起的区域间的相互影响。文章引入空间变量,利用卢卡斯内生经济增长函数,构建空间面板回归模型,对1990-2006年我国30个省市自治区教育人力资本与区域经济增长之间可能存在的空间关系进行了实证研究,验证了空间依赖性的存在,证实了教育人力资本对区域经济增长具有推动作用,且空间面板回归模型较传统面板回归模型更好地诠释了教育人力资本与区域经济增长之间的关系。     

The Spatial Structure of Financial Development in Brazil

Abstract

Using a GMM estimator for a spatial panel model with an endogenous spatial lag and spatial moving average errors we investigate the spatial structure of the financial system in Brazil. The results point to a negative spatial association between the Brazilian municipalities' financial system, in the way that a municipality with a more developed financial system tends to be surrounded by municipalities with less developed financial systems.

La structure spatiale du développement financier au Brésil

Rèsumè En utilisant un estimateur GMM pour un modèle à panneau spatial, avec un décalage spatial endogène et des erreurs à moyenne mobile spatiale, nous examinons la structure spatiale du système financier au Brésil. Les résultats semblent indiquer l'existence d'une association spatiale négative entre les municipalités brésiliennes sur le plan de leur système financier, dans le cadre de laquelle une municipalité dotée d'un système financier plus évolué a tendance à être entourée de municipalités possédant des systèmes financiers moins évolués.

La estructura espacial del desarrollo financiero en Brasil

Extracto Utilizando un estimador GMM (Método General de Momentos) para un modelo de panel espacial con un lapso espacial endógeno y errores de promedios móviles espaciales, investigamos la estructura espacial del sistema financiero en Brasil. Los resultados apuntan hacia una asociación espacial negativa entre el sistema financiero de las municipalidades brasileñas, de forma que una municipalidad con un sistema financiero más desarrollado tiende a estar rodeada de municipalidades con sistemas financieros menos desarrollados.

     

Raising the bar (9)

ABSTRACT

This editorial summarizes the papers published in issue 13(4) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper develops an economic geography model with trade costs in all sectors and different shares of unskilled labour in all locations. The second paper translates an economic geography model into a dynamic spatial econometric model and then estimates the unknown parameters to test for congestion spillover effects among Chinese cities. The following paper also investigates spillover effects, but of sovereign and banking risks across countries. The fourth paper empirically examines if a higher market potential results in higher average productivity and lower productivity dispersion of Italian retailers. The fifth paper demonstrates that modelling more than one spatial lag in the independent variables, using different specifications of the spatial weight matrix, can be used as a tool to correct for an omitted variable bias. The final paper develops a test for the existence of non-parametric non-linearities in a linear spatial econometric model.     

Recent Developments in Modelling Nonstationary Vector Autoregressions

In this paper we review some recent developments in the modelling of nonstationary vector autoregressions (VARs) which we feel have great potential for furthering applied researchers understanding of the relationships linking the variables making up a VAR. The developments surveyed are the use of model determination criteria in selecting lag length, trend order and cointegrating rank, causality testing in vector error correction models, FM-VAR estimation of levels VARS, common trends and cycles analysis, permanent and transitory decompositions, impulse response asymptotics, and the links between cointegrated VARs and structural models. The techniques are illustrated by applications to the modelling of U.K. equities, dividends and interest rates.     

Space-varying Coefficient Simultaneous Autoregressive Models for the Structural Analysis of Residential Water Demand

Abstract

This study develops two space-varying coefficient simultaneous autoregressive (SVC-SAR) models for areal data and applies them to the discrete/continuous choice model, which is an econometric model based on the consumer's utility maximization problem. The space-varying coefficient model is a statistical model in which the coefficients vary depending on their location. This study introduces the simultaneous autoregressive model for the underlying spatial dependence across coefficients, where the coefficients for one observation are affected by the sum of those for the other observations. This model is named the SVC-SAR model. Because of its flexibility, we use the Bayesian approach and construct its estimation method based on the Markov chain Monte Carlo simulation. The proposed models are applied to estimate the Japanese residential water demand function, which is an example of the discrete/continuous choice model.     

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 illustrate 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.     

Raising the bar (5)

Raising the bar (5). Spatial Economic Analysis. This editorial summarizes and comments on the papers published in this issue 12(1) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper examines the impact of the level of education on the decision to migrate and finds that it is approximately twice as large if both variables are modelled simultaneously. The second paper is one of the first papers to introduce a spatial component to models of international environmental agreements and to develop an exciting overlap with New Economic Geography. The third paper provides a tool, applied to Beijing, with which urban economic planners can investigate the role of variation and selection mechanisms in cluster development and identify possible paths of growth. The fourth paper contributes to the existing literature on retail geography by examining the role of consumption possibilities as an urban amenity. The fifth paper develops a Bayesian estimator of a linear regression model with spatial lags among the dependent variable, the explanatory variables and the disturbances. Finally, the sixth paper develops a semi-parametric generalized method of moments (GMM) estimator for a spatial autoregressive model with space-varying coefficients of the explanatory variables and a spatial autoregressive coefficient common to all units.     

Traffic density,congestion externalities,and urbanization in China

ABSTRACT

Although there is an abundant regional literature analyzing traffic congestion, only a few studies have explored extending such analysis with spatial effects. This study uses a dynamic spatial Durbin model and city-level panel data for the period 2003–14 to investigate the spatial spillover effects of traffic congestion on urbanization in China. The results show that there is an inverted ‘U’-shaped relationship between urbanization and traffic density in local and neighbouring cities, and congestion effects have appeared. In the short and long run, the spatial effects of traffic congestion have become an important force restricting the effective promotion of urbanization in China.