A Primer for Spatial Econometrics

Arbia, Giuseppe

doi:10.1057/9781137317940

Cited by 124 publications

(107 citation statements)

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“…For further details see table 2. After normalization the set of indicators was weighted because the different sub-indices determine the territorial capital with different weight (Arbia, 2006). Subsequently the figures were corrected with the method of penalty for bottleneck.…”

Section: The Model Of Territorial Capitalmentioning

confidence: 99%

Determinants of Hungarian Sub-Regions’ Territorial Capital

Jóna

2015

ESR&P

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Section: The Model Of Territorial Capitalmentioning

confidence: 99%

Determinants of Hungarian Sub-Regions’ Territorial Capital

Jóna

2015

ESR&P

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“…The main reason is the availability of more complete data sets in which units characterized by spatial features are observed over time. In general, a spatial panel data set contains more information and less multicollinearity among the variables than a cross-section spatial counterpart -see Anselin (1988Anselin ( , 2010, Elhorst (2014b) and Arbia (2014) for an introduction to this literature.…”

Section: Spatial Panelsmentioning

confidence: 99%

A Panel Data Toolbox for MATLAB

Álvarez¹,

Barbero²,

Zofío³

2017

J. Stat. Soft.

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Panel Data Toolbox is a new package for MATLAB that includes functions to estimate the main econometric methods of balanced and unbalanced panel data analysis. The package includes code for the standard fixed, between and random effects estimation methods, as well as for the existing instrumental panels and a wide array of spatial panels. A full set of relevant tests is also included. This paper describes the methodology and implementation of the functions and illustrates their use with well-known examples. We perform numerical checks against other popular commercial and free software to show the validity of the results.

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“…) where λ (1) < · · · < λ (n) are the ordered eigenvalues of W (Banerjee et al (2014), Arbia (2014)). …”

Section: Spatial Autoregressive Conditional Heteroskedasticity Modelmentioning

confidence: 99%

Spatial Autoregressive Conditional Heteroskedasticity Models

Sato

Matsuda

2017

JJSS

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This study proposes a spatial extension of time series autoregressive conditional heteroskedasticity (ARCH) models to those for areal data. We call the spatially extended ARCH models as spatial ARCH (S-ARCH) models. S-ARCH models specify conditional variances given surrounding observations, which constitutes a good contrast with time series ARCH models that specify conditional variances given past observations. We estimate the parameters of S-ARCH models by a two-step procedure of least squares and the quasi maximum likelihood estimation, which are validated to be consistent and asymptotically normal. We demonstrate the empirical properties by simulation studies and real data analysis of land price data in Tokyo areas.

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A Primer for Spatial Econometrics

Cited by 124 publications

References 0 publications

Determinants of Hungarian Sub-Regions’ Territorial Capital

Determinants of Hungarian Sub-Regions’ Territorial Capital

A Panel Data Toolbox for MATLAB

Spatial Autoregressive Conditional Heteroskedasticity Models

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