A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder

Abstract

In this paper, we make multi-step forecasts of the annual growth rates of the real GDP for each of the 16 German Länder simultaneously. We apply dynamic panel models accounting for spatial dependence between regional GDP. We find that both pooling and accounting for spatial effects help to improve the forecast performance substantially. We demonstrate that the effect of accounting for spatial dependence is more pronounced for longer forecasting horizons (the forecast accuracy gain is about 9% for a 1-year horizon and exceeds 40% for a 5-year horizon). We recommend incorporating a spatial dependence structure into regional forecasting models, especially when long-term forecasts are made.     

Firm and regional economic outcomes associated with a new,broad measure of business innovation

ABSTRACT

Most innovation-oriented studies use measures such as patent activity or research expenditures, likely ignoring the role of more home-grown upgrades or opportunity-recognizing activity common in businesses across the U.S. This study develops a broader ‘innovation index’ using a new survey of businesses that provides a wide lens for capturing innovative practices. The index is used in a series of regressions testing the relationship between innovation and both firm and regional-level economic outcomes. Results from the firm-level regressions show that the innovation index has a positive and significant relationship with wages paid to employees and product market growth. The regional analysis demonstrates that innovation is correlated with several regional economic variables, including median household income, and that spatial spillovers from innovation exist in some instances.     

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.     

Inequality and Growth in European Regions: Towards a Place-based Approach

Abstract

Does agglomeration matter for growth? This paper addresses this question by evaluating the impact that intra-regional income distribution has on regional growth in Europe. By using a spatially augmented Solow growth model, we investigate the convergence process among 188 EU regions between 1991 and 2004 and extend the traditional growth models to account for the impact of regional inequality on growth, as well as the effects of interaction among regions. Our assumption is that existing levels of inequality between sub-regional territorial units could positively contribute to regional economic growth in Europe, through the positive effects generated by the emergence of agglomeration economies. Our results show that while we cannot find any overall significant relationship in Europe between agglomeration and growth, once we distinguish between Objective 1 and Non-objective 1 regions we are able to find that agglomeration positively impacts subsequent growth, but only in the less developed regions.     

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。     

Estimation and testing for a partially linear single-index spatial regression model

ABSTRACT

Observations recorded on ‘locations’ usually exhibit spatial dependence. In an effort to take into account both the spatial dependence and the possible underlying non-linear relationship, a partially linear single-index spatial regression model is proposed. This paper establishes the estimators of the unknowns. Moreover, it builds a generalized F-test to determine whether or not the data provide evidence on using linear settings in empirical studies. Their asymptotic properties are derived. Monte Carlo simulations indicate that the estimators and test statistic perform well. The analysis of Chinese house price data shows the existence of both spatial dependence and a non-linear relationship.     

THE RELATIVE IMPORTANCE OF EXPORTS IN DETERMINING LEVELS OF REGIONAL OUTPUT AND INCOME: A Model and Empirical Evidence from Kenya

Exports have long been assumed by many to be the most important variable in driving regional growth, although factors such as government expenditure, investment demand, and remittances, among others, have also been recognized as significant. In addition, supply-side constraints to the promotion of regional growth and development have recently received increased attention in the literature. This paper evaluates the relative importance of exports, investment demand, and remittances, as well as supply-constrained agricultural production, in determining levels of regional output, value added, and household income in a single region in Kenya. The analysis is based on a mixed endogenous/ exogenous model derived from a social accounting matrix (SAM) which allows for incorporation of both demand and supply-side considerations. The paper finds that exports are, in fact, the most important factor in explaining regional output and wage income in the region studied, although not overwhelmingly so. In addition, the analysis demonstrates the importance of supply-constrained agricultural production as a determinant of income at the household level.     

On the Link between Volatility and Growth: A Spatial Econometrics Approach

This paper examines the link between macro volatility and economic growth in the lens of spatial econometrics. We present an unconstrained spatial Durbin Ramey-Ramey model. We test the extended model in a panel of 78 countries to investigate all the possible dimensions along which spatial interactions can affect the link between macro volatility and growth. In contrast to previous literature, we split the effects of volatility on growth into direct and indirect effects using partial derivative impacts approach. We found that both the direct and indirect effects of volatility on growth are negative; the latter effect suggesting the transmission of volatility shocks to neighbouring countries. Growth rates observed in neighbouring countries has a positive effect on growth rate of a particular country.     

Firm heterogeneity, productivity and spatially bounded knowledge externalities

In this paper, we argue that conceptually disentangling the ‘context versus composition’ aspects of regional growth is a multilevel issue. By applying multilevel models (also called random-effects models), we show (1) the importance of considering firm-specific characteristics simultaneously with region-specific characteristics, as we find that a large part of what is traditionally assigned to the impact of the region should be assigned to firm-specific characteristics and (2) that existing single-level methodologies can be problematic, as they are vulnerable to the charge of estimating significance levels that are too liberally assigned and promote exaggerations. This is illustrated empirically by showing that single-level approaches would lead to the conclusion that innovation spillovers are highly significant in a setting of Dutch urban growth differentials, while multilevel analyses shows less liberally assigned significance levels. We conclude that multilevel-effect models better fit research questions that combine firm and spatial characteristics simultaneously, especially because they allow firm-specific characteristics to be differently linked to their regional contexts.     

Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor

Abstract

This paper considers the problem of prediction in a panel data regression model with spatial autocorrelation in the context of a simple demand equation for liquor. This is based on a panel of 43 states over the period 1965–1994. The spatial autocorrelation due to neighbouring states and the individual heterogeneity across states is taken explicitly into account. We compare the performance of several predictors of the states’ demand for liquor for 1 year and 5 years ahead. The estimators whose predictions are compared include OLS, fixed effects ignoring spatial correlation, fixed effects with spatial correlation, random-effects GLS estimator ignoring spatial correlation and random-effects estimator accounting for the spatial correlation. Based on RMSE forecast performance, estimators that take into account spatial correlation and heterogeneity across the states perform the best for forecasts 1 year ahead. However, for forecasts 2–5 years ahead, estimators that take into account the heterogeneity across the states yield the best forecasts.     

The Spatial Durbin Model and the Common Factor Tests

Abstract

The spatial Durbin model occupies an interesting position in the field of spatial econometrics. It is the reduced form of a model with cross-sectional dependence in the errors and it may be used as the nesting equation in a more general approach of model selection. Specifically, in this equation we obtain the common factor tests (of which the likelihood ratio is the best known) whose objective is to discriminate between substantive and residual dependence in an apparently misspecified equation. Our paper tries to delve deeper into the role of the spatial Durbin model in the problem of specifying a spatial econometric model. We include a Monte Carlo study related to the performance of the common factor tests presented in the paper in small sample sizes.     

Reducing omitted-variable bias in spatial-interaction models by considering multiple neighbourhoods

ABSTRACT

A major challenge in the analysis of micro-level spatial interaction is to distinguish actual interactions from the effects of spatially correlated omitted variables. We propose extending the simple spatially lagged explanatory (SLX) model to include two spatial weighting matrices at different spatial scales to reduce omitted-variable bias. The approach is suitable when actual interaction takes place on a smaller local level, while the omitted variables are spatially correlated at a larger regional level and correlated with the included characteristics. We provide an empirical motivation and use Monte Carlo simulation to illustrate the bias-reduction effects in certain settings.     

Human Capital Spillovers and Regional Development

This paper introduces technological interdependence into the theoretical framework of Gennaioli et al. (Quarterly Journal of Economics 2013; 128 : 105–164). This extension leads to an expression for regional development with spatial effects that motivates the incorporation of the geographical dimension into their newly constructed database and empirical analysis. Our estimation results corroborate both the necessity of accounting for the presence of spatial dependence to study the determinants of regional income per capita and the importance of educational attainment in explaining regional development differences. Furthermore, we provide evidence that human capital generates positive spatial spillovers. Copyright © 2016 John Wiley & Sons, Ltd.     

Is agglomeration really good for growth? Global efficiency,interregional equity and uneven growth

According to NEG literature, spatial concentration of industrial activities increases growth at the regional and aggregate level without generating regional growth differentials. This view is not supported by the data. We extend the canonical model with an additional sector producing non-tradable goods which benefits from localized knowledge spillovers coming from the R&D performing industrial sector. This view, motivated by the evidence, generates both an anti-growth and a pro-growth effect of agglomeration for both the deindustrializing and the industrializing regions and leads to two novel results: (1) when agglomeration takes place, growth is lower in the periphery; (2) agglomeration may have a negative effect on the growth rate of real income, both at the regional and at the aggregate level. Our conclusions have relevant policy implications: contrary to the standard view, current EU and US regional policies favouring industrial dispersion might be welfare-improving both at the regional and the aggregate level and may reduce regional income disparities.     

A Spatial Modelling Framework for Income Estimation

Abstract

The main aim of this paper is to provide a spatial modelling framework for income estimation through the application of a contemporary spatial analysis technique. The application refers to the modelling of mean recorded household income in the area covered by the postcodes of the municipality of Athens in 2001. The main findings suggest that there is a very strong relationship between the proportion of people with a postgraduate qualification (namely, a Master's degree or PhD) and mean household income. Furthermore, there is evidence that this relationship is not stationary across space. This finding allows a better understanding as well as modelling of the main determinants of income in Athens.     

Entrepreneurship and path dependency in regional development

This paper describes a case study of path-dependent industrial development in a specialized medium-sized region characterized by a specialized high-tech industry cluster, located near Norway's capital, Oslo. The case study examines the path-dependent process of new industry arising from technology-related industries through three main branching mechanisms: entrepreneurship, mobility, and social networks. More particularly, the study explores the extent to which regional branching mechanisms relate to different path-dependent processes of path extension, path renewal, or path creation. The case study of industrial development in a medium-sized region that critically examines the concepts of path dependence, and argues that specialized medium-sized regions follow different path processes from core regions due to regional branching that does not happen automatically but instead may require policy action and external investment to avoid stagnation or negative lock-in.     

Incorporating Transportation Network Structure in Spatial Econometric Models of Commodity Flows

Abstract

We use a spatial econometric extension of the traditional regression-based gravity model to model commodity flows, focusing on a formal methodology for incorporating information regarding the highway network into the spatial connectivity structure of the spatial autoregressive econometric model. We show that our simple approach to incorporating this information in the model produces improved model fit and higher likelihood function values. Empirical estimates of the relative importance of the different types of origin–destination connectivity between regions indicates that the strongest spatial autoregressive effects arise when both origin and destination regions have neighbouring regions located on the highway network.     

Testing panel data regression models with spatial error correlation

This paper derives several lagrange multiplier (LM) tests for the panel data regression model with spatial error correlation. These tests draw upon two strands of earlier work. The first is the LM tests for the spatial error correlation model discussed in Anselin (Spatial Econometrics: Methods and Models, Kluwer Academic Publishers, Dordrecht; Rao's score test in spatial econometrics, J. Statist. Plann. Inference 97 (2001) 113) and Anselin et al. (Regional Sci. Urban Econom. 26 (1996) 77), and the second is the LM tests for the error component panel data model discussed in Breusch and Pagan (Rev. Econom. Stud. 47(1980) 239) and Baltagi et al. (J. Econometrics 54 (1992) 95). The idea is to allow for both spatial error correlation as well as random region effects in the panel data regression model and to test for their joint significance. Additionally, this paper derives conditional LM tests, which test for random regional effects given the presence of spatial error correlation. Also, spatial error correlation given the presence of random regional effects. These conditional LM tests are an alternative to the one-directional LM tests that test for random regional effects ignoring the presence of spatial error correlation or the one-directional LM tests for spatial error correlation ignoring the presence of random regional effects. We argue that these joint and conditional LM tests guard against possible misspecification. Extensive Monte Carlo experiments are conducted to study the performance of these LM tests as well as the corresponding likelihood ratio tests.     

Growth in a Cross-section of Cities: Location,Increasing Returns or Random Growth?

Abstract

This article analyses empirically the main existing theories on income and population city growth: increasing returns to scale, locational fundamentals and random growth. To do this we consider a large database of urban, climatological and macroeconomic data from 1,173 US cities observed in 1990 and 2000. The econometric model is robust to the presence of spatial effects. Our analysis shows the existence of increasing returns and two distinct equilibria in per-capita income and population growth. We also find important differences in the structure of productive activity, unemployment rates and geographical location between cities in low-income and high-income regimes.     

The role of spatial interdependence in local government cost efficiency: An application to waste Italian sector

The paper aims to study the effect of spatial interdependence, among nearby municipalities, on public services efficiency. An empirical analysis on the waste disposal service in 4250 Italian municipalities was carried out to evaluate the efficiency of waste management expenditure, once the impact of positive/negative externalities, of neighbouring local governments, on efficiency levels is isolated. From a methodological point of view, our study extends the spatial stochastic frontier methodology proposed in Fusco & Vidoli (2013) usable only for production analysis, allowing to admit into a cost frontier the spatial autocorrelation among residuals. Ignoring spatial autocorrelation leads to inferential problems violating one of the most important assumption of classical regression models: the non-correlation of the residuals. We found a significant spatial interdependence among neighbouring municipalities in term of cost efficiency, that, thanks to the methodology proposed, has been isolated allowing a discussion on the specific efficiency of municipalities. These results may suggest the need to consider proximity effects in future investigations about the efficiency of waste management and, more generally, of public services.