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.     

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.     

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.     

The interplay between the Bayesian and frequentist approaches: a general nesting spatial panel data model

The interplay between the Bayesian and Frequentist approaches: a general nesting spatial panel-data model. Spatial Economic Analysis. An econometric framework mixing the Frequentist and Bayesian approaches is proposed in order to estimate a general nesting spatial model. First, it avoids specific dependency structures between unobserved heterogeneity and regressors, which improves mixing properties of Markov chain Monte Carlo (MCMC) procedures in the presence of unobserved heterogeneity. Second, it allows model selection based on a strong statistical framework, characteristics that are not easily introduced using a Frequentist approach. We perform some simulation exercises, finding good performance of the properties of our approach, and apply the methodology to analyse the relation between productivity and public investment in the United States.     

Spatial Growth Regressions: Model Specification,Estimation and Interpretation

Abstract

We attempt to clarify a number of points regarding use of spatial regression models for regional growth analysis. We show that as in the case of non-spatial growth regressions, the effect of initial regional income levels wears off over time. Unlike the non-spatial case, long-run regional income levels depend on: own region as well as neighbouring region characteristics, the spatial connectivity structure of the regions, and the strength of spatial dependence. Given this, the search for regional characteristics that exert important influences on income levels or growth rates should take place using spatial econometric methods that account for spatial dependence as well as own and neighbouring region characteristics, the type of spatial regression model specification, and weight matrix. The framework adopted here illustrates a unified approach for dealing with these issues.     

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.     

Innovation and Co-location

Abstract

Here we attempt to advance the understanding of the impact of co-locative factors on regional innovation performance. The objectives are to answer what role co-location plays in explaining differences in regional innovation performance and what methodological improvements can compensate for the shortcomings of existing econometric analyses. The study is based on register data from the Business Register of Statistics Norway and the patent data from the Norwegian Patent Office for the period 1995–2003, aggregated to 161 labour market regions of Norway. A Bayesian spatial autoregressive (heteroscedastic) estimation procedure is applied. The results confirm the role of various co-locative factors in the spatial distribution of innovation.     

Entrepreneurship and regional dynamics: the case of Chile

ABSTRACT

During the last 20 years, social and political consensus has afforded the successful gradual implementation of entrepreneurship policy in Chile, transforming the country into one of the world’s most productive entrepreneurship ecosystems. However, the excessive political and economic centralization that has characterized Chile raises the question of whether spatial dependence influences entrepreneurship and what factors have led to this condition. By applying spatial econometric tools to data from 320 districts in Chile during the period 2013–2014, we conclude that there is spatial dependence among districts in Chile in relation to the creation of new businesses and that the immigrant population, the presence of different categories of universities and local patenting capacity are the variables with the greatest positive effect on this dependence.     

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.     

Raising the bar (8)

This editorial summarizes the papers published in issue 13(3) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper challenges the home market hypothesis that large countries host more firms relative to their size than small countries by considering the lobbying activities of multinational firms. The second paper analyzes the implications of a spatial weight matrix used to estimate a spatial econometric model that depends on an endogenous economic variable. By adding a spatial context, the third paper provides a novel contribution to the literature on international norms in de facto measures of human rights performance. The fourth paper examines the determinants of accepting informal work in Poland. The fifth paper deals with non-stationarity and cointegration in a dynamic spatial econometric panel data model when the number of observations in the time – rather than in the cross-sectional– domain tends to infinity.     

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.     

A two-step approach to account for unobserved spatial heterogeneity

A two-step approach to account for unobserved spatial heterogeneity. Spatial Economic Analysis. Empirical analysis in economics often faces the difficulty that the data are correlated and heterogeneous in some unknown form. Spatial econometric models have been widely used to account for dependence structures, but the problem of directly dealing with unobserved spatial heterogeneity has been largely unexplored. The problem can be serious particularly if we have no prior information justified by economic theory. In this paper we propose a two-step procedure to identify endogenously spatial regimes in the first step and to account for spatial dependence in the second step. This procedure is applied to hedonic house price analysis.     

When the difference makes a difference – the regional embeddedness of entrepreneurship

Abstract

Regional determinants of new firm formation are of interest to researchers and policymakers. In the analysis of new firm formation, most studies use econometric approaches that mask intra-unit variations, not recognizing counterbalancing and dilution effects as a result. Recent advances in spatial statistics such as Geographically Weighted Regression (GWR) take local variations into account. However, these approaches operate only on a bivariate level, making it impossible to detect the homogenous parts of the area under examination with regard to a number of relationships between new firm formation and its determinants. Based on a sample of 412 German regions, we apply GWR and subsequent graph-partitioning clustering to identify multi-relationally homogeneous sub-areas. Being that the results suggest a four-cluster solution, ‘one size fits all’ policies and premature unit zoning can be called into question.     

A review of Phillips-type right-tailed unit root bubble detection tests

Recent developments on the right-tailed unit root tests of Phillips et al., which are used to date stamp the origination and collapse dates of asset price bubbles, have generated considerable interest. This paper provides a review for both empirical researchers that adopt these new econometric tools to detect the presence of asset price bubbles, and theoretical papers that extend these testing procedures. This paper also uses the psymonitor package in R to demonstrate the practical use of such tests using an example based on data for British Railway Mania of the 1840s.     

Specification and estimation of spatial econometric models: A discussion of alternative strategies for spatial economic modelling

The semantical insufficiency of (spatial) economic theories necessitates the making of additional assumptions — thereby introducing substantial specification uncertainty — in order to arrive at a fully specified econometric model. The traditional or current approach to econometric modelling treats specification uncertainty inadequately. This proposition is illustrated by two well-known examples from the spatial economic literature. Two alternative specification strategies for spatial economic modelling — designed to improve the current spatial econometric modelling approach — are proposed. One of these strategies is used for a specification analysis of agricultural output in Eire.     

Parking location and transit demand: A case study of endogenous attributes in disaggregate mode choice models

This paper discusses a simultaneous model of transport mode choice and optimal parking location for the auto mode. In developing this model, four extensions of disaggregate choice theory are made that should be useful in other applications. These extensions are: (1) the formulation of an econometric model that allows for continous endogenous attributes in discrete choice decisions; (2) the use of an econometric estimation technique that is implementable using existing computer programs; (3) the development of an explicit reduced form expansion path cost model of location decisions; and (4) the extension of aggregation procedures to predict both transit demand and the spatial distribution of parking.     

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.     

New methods for analyzing structural models of labor force dynamics

The economic theory of decision-making under uncertainty is used to produce three econometric models of dynamic discrete choice: (1) for a single spell of unemployment; (2) for an equilibrium two-state model of employment and non-employment; (3) for a general three-state model with a non-market sector. The paper provides a structural economic motivation for the continuous time Markov (or more generally ‘competing risks’) model widely used in longitudinal analysis in biostatistics and sociology, and it extends previous work on dynamic discrete choice to a continuous time setting. An important feature of identification analysis is separation of economic parameters that can only be identified by assuming arbitrary functional forms from economic parameters that can be identified by non-parametric procedures. The paper demonstrates that most econometric models for the analysis of truncated data are non-parametrically under-identified. It also demonstrates that structural estimators frequently violate standard regularity conditions. The standard asymptotic theory is modified to account for this essential feature of many structural models of labor force dynamics. Empirical estimates of an equilibrium two-state model of employment and non-employment are presented.     

Knowledge diversity and entrepreneurship following an economic crisis: an empirical study of regional resilience in Great Britain

ABSTRACT

This article argues that the ability of entrepreneurs to facilitate regional adaptation to economic crises is mediated by the size and diversity of local knowledge stocks. The specific research question addressed is the hypothesis that, in the aftermath of a crisis, the birth rate of new firms will recover more rapidly in regions with a strong and diverse knowledge stock. It is theorised that unrelated knowledge diversity is of particular importance in stimulating new entrepreneurial opportunities and structural change, whilst the incentive to exploit opportunities differs according to region-specific factors. In addition to this theoretical contribution, the article develops spatial econometric models to test these research hypotheses using data on sub-regions of Great Britain for 2004–2014. The results support the central theoretical hypotheses and emphasise the positive significance of unrelated knowledge diversity and employment in knowledge intensive services to regional recovery from an economic shock. A key implication for policy-makers wishing to facilitate regional adaptation to crises is that it is important to focus on fostering entrepreneurship by developing a region’s stock of knowledge intensive services and the diversity of the knowledge creating sector, rather than relying on specialised clusters of firms.     

Forecasting economies in transition: The case of Romania

Structural change is endemic in the Eastern European economies and the newly emerging Commonwealth of Independent States yet conventional econometric modelling techniques proceed under the assumption that there is a structurally stable true economy to be discovered. This paper extends the analysis of Hall (1993) by exploring the consequences of endemic structural change for forecasting and building structural econometric models. We propose a model formulation which makes the econometric model itself able to adjust its parameters in the light of economic change using the Kalman Filter.The paper then illustrates this approach by building a small model of Romania and investigating its forecasting properties.This work was presented at a meeting of the MEET II network funded by the ACE project, Econometric Inference into the Macroeconomic dynamics of East European Economies.