Modified harmonic mean method for spatial autoregressive models

Osman Doğan

doi:10.1016/j.econlet.2023.110978

Modified harmonic mean method for spatial autoregressive models

Osman Doğan

Department of Economics

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

Abstract

In this paper, I suggest using the modified harmonic mean method for estimating marginal likelihood functions of cross-sectional spatial autoregressive models. In a Bayesian estimation setting, I show how this method can be used for popular cross-sectional spatial autoregressive models. In a simulation study, I investigate the finite sample performance of this estimator along with some other popular information criteria for the nested and non-nested model selection problems. The simulation results show that the modified harmonic mean estimator performs satisfactorily, and can be useful for the specification search exercises in spatial econometrics.

Original language	English
Article number	110978
Journal	Economics Letters
Volume	223
DOIs	https://doi.org/10.1016/j.econlet.2023.110978
Publication status	Published - Feb 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

AIC
BIC
DIC
Marginal likelihood
Model selection
Modified harmonic mean
SAR

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10.1016/j.econlet.2023.110978

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title = "Modified harmonic mean method for spatial autoregressive models",

abstract = "In this paper, I suggest using the modified harmonic mean method for estimating marginal likelihood functions of cross-sectional spatial autoregressive models. In a Bayesian estimation setting, I show how this method can be used for popular cross-sectional spatial autoregressive models. In a simulation study, I investigate the finite sample performance of this estimator along with some other popular information criteria for the nested and non-nested model selection problems. The simulation results show that the modified harmonic mean estimator performs satisfactorily, and can be useful for the specification search exercises in spatial econometrics.",

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author = "Osman Doğan",

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year = "2023",

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doi = "10.1016/j.econlet.2023.110978",

language = "English",

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AB - In this paper, I suggest using the modified harmonic mean method for estimating marginal likelihood functions of cross-sectional spatial autoregressive models. In a Bayesian estimation setting, I show how this method can be used for popular cross-sectional spatial autoregressive models. In a simulation study, I investigate the finite sample performance of this estimator along with some other popular information criteria for the nested and non-nested model selection problems. The simulation results show that the modified harmonic mean estimator performs satisfactorily, and can be useful for the specification search exercises in spatial econometrics.

KW - AIC

KW - BIC

KW - DIC

KW - Marginal likelihood

KW - Model selection

KW - Modified harmonic mean

KW - SAR

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DO - 10.1016/j.econlet.2023.110978

M3 - Article

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SN - 0165-1765

VL - 223

JO - Economics Letters

JF - Economics Letters

M1 - 110978

ER -

Modified harmonic mean method for spatial autoregressive models

Abstract

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Keywords

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