Heteroskedasticity, temporal and spatial correlation matter

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F13%3A00209699" target="_blank" >RIV/62156489:43110/13:00209699 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.11118/actaun201361072151" target="_blank" >http://dx.doi.org/10.11118/actaun201361072151</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.11118/actaun201361072151" target="_blank" >10.11118/actaun201361072151</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Heteroskedasticity, temporal and spatial correlation matter

Popis výsledku v původním jazyce

As economic time series or cross sectional data are typically affected by serial correlation and/or heteroskedasticity of unknown form, panel data typically contains some form of heteroskedasticity, serial correlation and/or spatial correlation. Therefore, robust inference in the presence of heteroskedasticity and spatial dependence is an important problem in spatial data analysis. In this paper we study the standard errors based on the HAC of cross-section averages that follows Vogelsang's (2012) fixed-b asymptotic theory, i.e. we continue with Driscoll and Kraay approach (1998). The Monte Carlo simulations are used to investigate the finite sample properties of commonly used estimators both not accounting and accounting for heteroskedasticity and spatiotemporal dependence (OLS, GLS) in comparison to brand new estimator based on Vogelsang's (2012) fixed-b asymptotic theory in the presence of cross-sectional heteroskedasticity and serial and spatial correlation in panel data with fixed

Název v anglickém jazyce

Heteroskedasticity, temporal and spatial correlation matter

Popis výsledku anglicky

As economic time series or cross sectional data are typically affected by serial correlation and/or heteroskedasticity of unknown form, panel data typically contains some form of heteroskedasticity, serial correlation and/or spatial correlation. Therefore, robust inference in the presence of heteroskedasticity and spatial dependence is an important problem in spatial data analysis. In this paper we study the standard errors based on the HAC of cross-section averages that follows Vogelsang's (2012) fixed-b asymptotic theory, i.e. we continue with Driscoll and Kraay approach (1998). The Monte Carlo simulations are used to investigate the finite sample properties of commonly used estimators both not accounting and accounting for heteroskedasticity and spatiotemporal dependence (OLS, GLS) in comparison to brand new estimator based on Vogelsang's (2012) fixed-b asymptotic theory in the presence of cross-sectional heteroskedasticity and serial and spatial correlation in panel data with fixed

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AH - Ekonomie

OECD FORD obor

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Projekt

<a href="/cs/project/7E12049" target="_blank" >7E12049: Welfare, Wealth and Work for Europe</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2013

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

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Svazek periodika

61

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

5

Strana od-do

2151-2155

Kód UT WoS článku

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EID výsledku v databázi Scopus

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