No abstract

This article reflects on the relevance of the concept of unit roots in the spatial context. The initial introduction of this topic in the time-series literature caused significant changes in the mainstream econometric methodology. However, the literature specialized in spatial econometric modeling has not extensively dealt with this issue. The current article continues the discussion of the concept of unit roots employed in a spatial context and presents a series of peculiarities that should be noticed. Subsequently, attention focuses on the topic of deterministic trends associated with the scale factor that intervenes in autoregressive spatial processes. The incidence of this type of trend should not be neglected. It induces the risk of finding spurious correlation that should be taken into account.

No abstract

Dynamic spatial panel data models, ARIMA models, Forecast, Spanish provinces employment, C21, C22, C23, C53, R15,

Problems associated with employment have become a major issue in regional economics, especially in those countries, such as Spain, where unemployment represents a serious threat to regional stability. In this context, the purpose of our paper is twofold: first, to develop a prediction instrument for regional employment, one which should be flexible and efficient; secondly, to use this instrument in order to make a short term forecast of sectoral regional employment (and the labour force) of a characteristic region of Spain, namely Aragón.The type of instrument used is an econometric time series model specified in a regional context. This means that attention must be given to the spatial structure of the labour markets. One way to introduce this effect may be through the specification of a transfer function which connects the regional market to the national one. This approach is an improvement on the traditional technique of univariate time series. Furthermore, for small labour markets it is important to assess the incidence of outliers, especially for forecasting purposes, because they distort the statistical inference.