A Bayesian spatial panel model with heterogeneous coefficients

LeSage, James P.; Chih, Yao‐Yu

doi:10.1016/j.regsciurbeco.2017.02.007

Cited by 30 publications

(8 citation statements)

References 10 publications

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“…In static panel data frameworks, several papers have derived heterogeneous coefficients versions of the traditional homogeneous SAR specification. (see, for further details Aquaro et al 2015, Blasques et al 2016, LeSage & Chih 2017. The GVAR also allows for heterogeneous coefficients between countries.…”

Section: Discussionmentioning

confidence: 99%

Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach

Debarsy

Dossougoin

Ertur

et al. 2018

Journal of Economic Dynamics and Control

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We contribute to the literature on international risk spillovers by developing a unified framework based on spatial econometrics that enables us to address the following questions: (i) what are the effective transmission channels-real linkages and informational channels-of international risk spillovers across countries and/or regions, (ii) what are the most dominant ones, and (iii) which countries are most at risk for their environment and which are suffering the most from international exposure. Our analysis, based on 41 advanced and emerging economies from 2008Q1 to 2012Q4, shows that among the considered channels for explaining international spillovers of sovereign bond spreads, the informational channel is of utmost importance. Our results challenge previous findings from the literature in which the empirical strategy did not accommodate altogether important features of country spillovers, such as the coexistence of multiple transmission channels in the presence of contemporaneous and time-lagged interactions. Ultimately, our stress-testing analysis reveals important insights on countries prone either to international spillovers, international exposure or both at the regional and the worldwide level.

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Section: Discussionmentioning

confidence: 99%

Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach

Debarsy

Dossougoin

Ertur

et al. 2018

Journal of Economic Dynamics and Control

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“…Since this conditional distribution is not of known form, we rely on a Metropolis–Hasting scheme for sampling the N conditional posterior distributions for the parameters

γ_{i}, i = 1, \dots, N

(see LeSage and Pace , Chapter 6). As noted earlier, this conditional distribution does not contain the log determinant term that appears in the conditional distribution for ψ i in the HSAR model (see LeSage and Chih ).…”

Section: Bayesian Mcmc Estimation Of the Hmess Modelmentioning

confidence: 73%

“…LeSage and Chih () propose a Bayesian MCMC estimation scheme for the HSAR model, and follow the approach used for cross‐sectional and static panel SAR models using the MCMC draws for the purpose of constructing empirical measures of dispersion. Aquaro, Bailey, and Pesaran () do not discuss inference regarding direct, spillin and spillout estimates for their quasi maximum likelihood estimation method.…”

Section: The Hsar and Hmess Spatial Panel Data Modelsmentioning

confidence: 99%

“…Estimates are for models based on a six nearest neighbors weight matrix. LeSage and Chih () recommend use of doubly stochastic weight matrices in applications involving the HSAR model specification. A doubly stochastic weight matrix is one where the elements of both row and column sums equal one.…”

Section: An Empirical Applicationmentioning

confidence: 99%

“…As mentioned in fourth section, LeSage and Chih () also note algorithm's potential problems with creation of a doubly stochastic weight matrix when there are a small number of regions. For our sample of 261 counties, one county resulted in weights of 0.999 and 0.001, indicating only a minor problem with the algorithm in this application.…”

Section: An Empirical Applicationmentioning

confidence: 99%

See 2 more Smart Citations

A Matrix Exponential Spatial Panel Model with Heterogeneous Coefficients

LeSage

Chih

2017

Geographical Analysis

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We extend the heterogeneous coefficients spatial autoregressive panel model (HSAR) from Aquaro, Bailey, and Pesaran (2015) to the case of a heterogeneous coefficients matrix exponential spatial specification (HMESS). The HSAR is capable of producing parameter estimates for each region in the sample, that follow a spatial autoregressive process. Spatial autoregressive processes apply geometric decay of influence to higher‐order neighboring regions. The HMESS takes a similar approach as the HSAR to produce estimates for each region in the sample, but relies on a matrix exponential function to apply exponential decay to higher‐order neighbors. The MESS introduced by LeSage and Pace (2007) for the case of cross‐sectional spatial data samples has some potential computational advantages over the spatial autoregressive specification. In addition, the spatial dependence parameter in the MESS ranges from minus to plus infinity, which allows for use of normal priors assigned to this parameter in a Bayesian setting. We extend the cross‐sectional MESS to the case of a heterogeneous coefficients model, and describe Bayesian Markov Chain Monte Carlo estimation. We illustrate the HMESS model with a panel wage curve relationship using quarterly unemployment and wage rates from 261 counties centered on the Bakken shale oil region in North Dakota and Montana.

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Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices

Aquaro

Bailey

Pesaran

2020

J of Applied Econometrics

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This paper considers the problem of identification, estimation and inference in the case of spatial panel data models with heterogeneous spatial lag coefficients, with and without (weakly) exogenous regressors, and subject to heteroskedastic errors. A quasi maximum likelihood (QML) estimation procedure is developed and the conditions for identification of spatial coefficients are derived. Regularity conditions are established for the QML estimators of individual spatial coefficients, as well as their means (the mean group estimators), to be consistent and asymptotically normal. Small sample properties of the proposed estimators are investigated by Monte Carlo simulations for Gaussian and non-Gaussian errors, and with spatial weight matrices of differing degrees of sparsity. The simulation results are in line with the paper's key theoretical findings even for panels with moderate time dimensions, irrespective of the number of cross section units. An empirical application to U.S. house price changes during the 1975-2014 period shows a significant degree of heterogeneity in spill-over effects over the 338 Metropolitan Statistical Areas considered. JEL-Codes: C210, C230.

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A Bayesian spatial panel model with heterogeneous coefficients

Cited by 30 publications

References 10 publications

Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach

Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach

A Matrix Exponential Spatial Panel Model with Heterogeneous Coefficients

Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices

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