Dynamic models in space and time

This paper presents a first-order autoregressive distributed lag model in both space and time. It is shown that this model encompasses a wide series of simpler models frequently used in the analysis of space-time data as well as models that better fit the data and have never been used before. A framework is developed to determine which model is the most likely candidate to study space-time data. As an application, the relationship between the labor force participation rate and the unemployment rate is estimated using regional data of Germany, France and the UK derived from Eurostat, 1983-1993.

Models for Dynamic Panels in Space and Time - an Application to Regional Unemployment in the EU

One of the central research questions in modelling space-time data is the right econometric model. At least three problems must be tackled: (i) The observations on each spatial unit might be correlated over time, (ii) The observations at each point in time might be correlated over space, and (iii) The omission of time-invariant and/or spatial-invariant background variables could bias the regression coefficients in a typical cross-section or time-series model. As we have no a priori reasons to believe that one problem is more important than another, this paper presents a general model that encompasses a wide series of simpler models frequently used in the time-series econometrics, spatial econometrics and panel data econometrics literature. A framework is developed to determine which model is the most likely candidate to study space-time data.

A note on the linear, logit and probit functional form of the labour force participation rate equation

The commonly used specification in regional economic research on labour force participation is the linear probability function. An important alternative recommended in the Handbook of Regional and Urban Economics in the contribution of Isserman et al. (1986) on `Regional Labor Market Analysis' is the logit probability function. Their argument for the logit probability function is as follows. Given that economic theory on labour force participation does not suggest to pick one functional form over another and that the parameters of the logit probability function are estimable by OLS under the usual assumptions about the error term, the benefit of the logit probability function is that any estimated value for L lies within the logical bounds [0,1]. This feature is particularly desirable in a forecasting context when out of sample data might otherwise potentially yield absurd labour force participation rates. In this note two counter-arguments are gathered against using the logit probability function which are lacking in the Handbook of Regional and Urban Economics. Furthermore, it is shown that the logit probability function in this discourse can be replaced by the probit probability function equally well. Keywords: logit, probit, labour force participation rate.

Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable

This paper surveys panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable. In particular, it focuses on the specification and estimation of four panel data models commonly used in applied research: the fixed effects model, the random effects model, the fixed coefficients model and the random coefficients model. This survey should prove useful for researchers in this area.

Unconditional maximum likelihood estimation of dynamic models for spatial panels

This paper hammers out the estimation of a fixed effects dynamic panel data model extended either to include spatial error autocorrelation or a spatially lagged dependent variable. To overcome the inconsistencies associated with the traditional least squares dummy estimator, the models are first-differenced to eliminate the fixed effects and then the unconditional likelihood function is derived taking into account the density function of the first-differenced observations on each spatial unit. When exogenous variables are omitted, the exact likelihood function of both models is found to exist. When exogenous variables are included, the presample values of these variables and thus the likelihood function must be approximated. Two leading cases are considered: the Bhargava and Sargan approximation and the Nerlove and Balestra approximation. As an application, a dynamic demand model for cigarettes is estimated based on panel data from 46 American states over the period 1963 to 1992.

Unemployment Rates At the Regional and National Levels of the European Union: An Integrated Analysis

This study investigates the causes of variation in regional unemployment rates in a cross-country perspective. The explanatory variables consist of both regional-level and macro-level variables. An appropriate econometric model of random coefficients for the former and fixed coefficients for the latter variables is developed, further taking into account that observations may be correlated over time and over space and that some of the explanatory variables are not strictly exogenous. On the basis of this model a regional unemployment rate equation is estimated, using data of 143 regions across 11 EU countries derived from Eurostat, 1983-1997, and national data on labour market institutions predominantly derived from the OECD.

Effects of Transport Improvements on Commuting and Residential Choice

This paper develops a commuter location model able to explain and simulate residential location changes of commuters that result from transport improvements. The core model is based on the assumption of constant commuting time, while two extensions incorporate substitution possibilities having an upward effect on total commuting time. Estimation errors of the residential location of the working population with the existing transport system are limited to 7%. With the extended model, the impacts on commuting and residential choice are investigated for six higher speed rail connections between Amsterdam, located in the urban core of the Netherlands, and Groningen, located in its rural periphery. The model outcomes strongly influenced the public policy debate in the Netherlands.

Evidence of Competition in Research Activity among Economic Department using Spatial Econometric Techniques

Despite the prevalence of both competitive forces and patterns of collaboration within academic communities, studies on research productivity generally treat universities as independent entities. By exploring the research productivity of all academic economists employed at 81 universities and 17 economic research institutes in Austria, Germany, and German-speaking Switzerland, this study determines whether a research unit’s productivity depends on that of neighboring research units. The significant negative relationship that is found implies competition for priority of discovery among individual researchers, as well as the universities and research institutes that employ them. In addition, the empirical results support the hypotheses that collaboration and the existence of economies of scale increase research productivity.Research productivity, Competition, Collaboration, Negative spatial autocorrelation, Geo-referenced point data