Forecasting with Spatial Panel Data This paper compares various forecasts using panel data with spatial error correlation. The true data generating process is assumed to be a simple error component regression model with spatial remainder disturbances of the autoregressive or moving average type. The best linear unbiased predictor is compared with other forecasts ignoring spatial correlation, or...

This paper applies a model of market power measurement under product differentiation to the case of the gasoline market in California, using data for the period 1983-1989. Our results show that there is a considerable degree of product differentiation among major brands. This allows firms to exercise local market power over their own specific products, but there are also signals of an important...

The aim of this paper is to study the probability to find an opportunity of collaboration among small and medium sized firms by participating in the Europartenariat meeting created to encourage co-operation links. Contacts among firms are relatively few in number and are assumed to be generated by a Poisson process. Empirical results of different Poisson regression models with reference to para...

This paper uses control variables to identify and estimate models with nonseparable, multidimensional disturbances. Triangular simultaneous equations models are considered, with instruments and disturbances independent and reduced form that is strictly monotonic in a scalar disturbance. Here it is shown that the conditional cumulative distribution function of the endogenous variable given the i...

Real estate data exhibit autocorrelation and heterogeneity across both space and time. The literature is beginning to advance methods that account for the four components. This article accordingly introduces the Bayesian Maximum Entropy (BME) method to real estate analysis. In addition to controlling for spatiotemporal autocorrelation and heterogeneity, BME allows for probabilistic and missing ...

This paper proposes a new panel model of cross-sectional dependence. The model has a number of potential structural interpretations that relate to economic phenomena such as herding in financial markets. On an econometric level, it provides a flexible approach of modelling interactions across panel units and can generate endogenous cross-sectional dependence that can resemble the dependence tha...

This paper uses an independent competing risks framework to model job tenure, with previous labour market status and the duration of the preceding unemployment spell as explanatory variables. We find that jobs that follow an unemployment spell have shorter mean duration than other jobs. Less than one half of jobs that follow unemployment last for twelve months. Multivariate results suggest that...

This study examines the causal relationship between foreign direct investment and economic growth. Methodology is based on the Toda-Yamamoto test for causality relationship and the bounds testing (ARDL). Time-series data covering the period 19702005 for Malaysia, the study found, in the case of Malaysia there is no strong evidence of a bi-directional causality and long-run relationship between ...

Random Coefficient Panel Data Models This paper provides a review of linear panel data models with slope heterogeneity, introduces various types of random coefficients models and suggest a common framework for dealing with them. It considers the fundamental issues of statistical inference of a random coefficients formulation using both the sampling and Bayesian approaches. The paper also provid...

By disaggregating price indices, it becomes apparent that the real exchange rate consists of the real exchange rate for a single good and a weighted sum of relative prices between goods. When applying a battery of panel unit root tests to this sum and its components, it is found that both the sum and the relative prices are non-stationary. This implies that PPP is invalid even if the LOP holds ...