NON‐NESTED TESTS ON THE WEIGHT STRUCTURE IN SPATIAL AUTOREGRESSIVE MODELS: SOME MONTE CARLO RESULTS

Luc Anselin

doi:10.1111/j.1467-9787.1986.tb00820.x

NON‐NESTED TESTS ON THE WEIGHT STRUCTURE IN SPATIAL AUTOREGRESSIVE MODELS: SOME MONTE CARLO RESULTS

Luc Anselin

Research output: Contribution to journal › Article › peer-review

37 Scopus citations

Abstract

ABSTRACT. This paper presents some further results with respect to hypothesis tests for the appropriate structure of dependence in mixed regressive/spatial‐autoregressive relations. Three tests on non‐nested hypotheses are considered, which are based on the use of augmented regressions, and their small sample performance is compared in a number of Monte Carlo simulation experiments.

Original language	English (US)
Pages (from-to)	267-284
Number of pages	18
Journal	Journal of Regional Science
Volume	26
Issue number	2
DOIs	https://doi.org/10.1111/j.1467-9787.1986.tb00820.x
State	Published - 1986
Externally published	Yes

ASJC Scopus subject areas

Development
Environmental Science (miscellaneous)

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10.1111/j.1467-9787.1986.tb00820.x

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abstract = "ABSTRACT. This paper presents some further results with respect to hypothesis tests for the appropriate structure of dependence in mixed regressive/spatial‐autoregressive relations. Three tests on non‐nested hypotheses are considered, which are based on the use of augmented regressions, and their small sample performance is compared in a number of Monte Carlo simulation experiments.",

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year = "1986",

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AB - ABSTRACT. This paper presents some further results with respect to hypothesis tests for the appropriate structure of dependence in mixed regressive/spatial‐autoregressive relations. Three tests on non‐nested hypotheses are considered, which are based on the use of augmented regressions, and their small sample performance is compared in a number of Monte Carlo simulation experiments.

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NON‐NESTED TESTS ON THE WEIGHT STRUCTURE IN SPATIAL AUTOREGRESSIVE MODELS: SOME MONTE CARLO RESULTS

Abstract

ASJC Scopus subject areas

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