Selected Challenges From Spatial Statistics For Spatial Econometricians

Griffith and Paelinck (2011) present selected non-standard spatial statistics and spatial econometrics topics that address issues associated with spatial econometric methodology. This paper addresses the following challenges posed by spatial autocorrelation alluded to and/or derived from the spatial statistics topics of this book: the Gaussian random variable Jacobian term for massive datasets; topological features of georeferenced data; eigenvector spatial filtering-based georeferenced data generating mechanisms; and, interpreting random effects.Artykuł prezentuje wybrane, niestandardowe statystyki przestrzenne oraz zagadnienia ekonometrii przestrzennej. Rozważania teoretyczne koncentrują się na wyzwaniach wynikających z autokorelacji przestrzennej, nawiązując do pojęć Gaussowskiej zmiennej losowej, topologicznych cech danych georeferencyjnych, wektorów własnych, filtrów przestrzennych, georeferencyjnych mechanizmów generowania danych oraz interpretacji efektów losowych

Comparing estimation methods for spatial econometrics techniques using R.

Recent advances in spatial econometrics model fitting techniques have made it more desirable to be able to compare results and timings. Results should correspond between implementations using different applications, while timings are more readily compared within a single application. A broad range of model fitting techniques are provided by the contributed R packages for spatial econometrics. These model fitting techniques are associated with methods for estimating impacts and some tests, which will also be presented and compared. This review constitutes an up-to-date demonstration of techniques now available in R, and mentions some that will shortly become more generally available.Spatial autoregression; Econometric software.

The relation between global migration and trade networks

In this paper we develop a methodology to analyze and compare multiple global networks, focusing our analysis on the relation between human migration and trade. First, we identify the subset of products for which the presence of a community of migrants significantly increases trade intensity, where to assure comparability across networks we apply a hypergeometric filter that lets us identify those links which intensity is significantly higher than expected. Next, proposing a new way to define country neighbors based on the most intense links in the trade network, we use spatial econometrics techniques to measure the effect of migration on international trade, while controlling for network interdependences. Overall, we find that migration significantly boosts trade across countries and we are able to identify product categories for which this effect is particularly strong

Computing the Jacobian in spatial models: an applied survey.

Despite attempts to get around the Jacobian in fitting spatial econometric models by using GMM and other approximations, it remains a central problem for maximum likelihood estimation. In principle, and for smaller data sets, the use of the eigenvalues of the spatial weights matrix provides a very rapid and satisfactory resolution. For somewhat larger problems, including those induced in spatial panel and dyadic (network) problems, solving the eigenproblem is not as attractive, and a number of alternatives have been proposed. This paper will survey chosen alternatives, and comment on their relative usefulness.Spatial autoregression; Maximum likelihood estimation; Jacobian computation; Econometric software.

Spatial Labor Markets and Technology Spillovers - Analysis from the US Midwest

In this paper we examine the relation between geographic location and innovative behavior. Knowledge spillins, as opposed to knowledge spillovers, are modeled as an externality which exists between geographically close economic agents and enters the representative inventor production function explicitly from neighboring regions. To proxy new innovative behavior and new knowledge generated we use counts of patent filings per county. The proposed geographic spillin is tested for the US Midwestern States of Iowa, Minnesota, Missouri, Kansas, Nebraska, South Dakota and North Dakota using a newly constructed data set and implementing spatial statistical methods. The data set is comprised of primary inventor utility patent filings per county for the years 1975-2000. The results do indeed suggest spatial interaction does occur and innovative activity in surrounding counties is an important factor in explaining new innovative behavior. Further analysis also reveals lagged patenting behavior within the county also has a significant impact on patenting activity suggesting innovative externalities exist over both space and time.patents; employment growth; technology spillovers; spatial spillovers