Specification and estimation of spatial liner regression models: Monte Carlo evaluation of pre-test estimators

Article Abstract:

Spatially dependent samples are difficult to analyze because estimators traditionally used are inefficient and biased. A spatial linear regression model is presented to identify systematic variables for spatial data using an autoregressive-error structure. Analysis of reveals that spatial variable expansion methods are better estimates of spatially dependent variables although results may be affected by the accurate specification of the weights matrix and spatial systems.

Regression analysis

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Knowledge impacts of universities on industry: an aggregate simultaneous investment model

Article Abstract:

An aggregate multiregional investment model is used to measure the regional economic impacts of universities on the location decisions of private firms, The model differentiates knowledge production from university-related expenditure impacts but incorporates them. Results show that company decisions to invest in buildings in a particular location does not depend on its proximity to an academic knowledge infrastructure.

Universities and colleges, Influence, Business and education, Industry and education, Industrial locations, Industrial location

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Simple diagnostic tests for spatial dependence

Article Abstract:

Simple diagnostic tests for spatial dependence are proposed as useful measures in the field of spatial econometrics. These tests are applied using ordinary least-squares residuals and Monte Carlo simulation methods. The robustness of such tests is backed up by results and their finite sample properties have been found to be valid. One advantage they have over other tests is their computational simplicity.

Usage, Testing, Monte Carlo method, Monte Carlo methods, Autocorrelation (Statistics)

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