TY - JOUR
T1 - Diagnostic tests for homoskedasticity in spatial cross-sectional or panel models
AU - Baltagi, Badi H.
AU - Pirotte, Alain
AU - Yang, Zhenlin
N1 - Funding Information:
We would like to thank Gianfranco Piras, James LeSage, and the participants of the XII World Conference of the Spatial Econometrics Association, WVPU Vienna, June 2018, for their valuable suggestions and helpful comments. We are grateful to Editor Serena Ng for her encouragements in revising the paper, and for her helpful and constructive comments that have led to a much enhanced version of the paper. Helpful comments from three anonymous referees and two associate editors are also gratefully acknowledged. Zhenlin Yang gratefully acknowledges the research support from Singapore Management University under Grant C244/MSS16E003 .
Funding Information:
We would like to thank Gianfranco Piras, James LeSage, and the participants of the XII World Conference of the Spatial Econometrics Association, WVPU Vienna, June 2018, for their valuable suggestions and helpful comments. We are grateful to Editor Serena Ng for her encouragements in revising the paper, and for her helpful and constructive comments that have led to a much enhanced version of the paper. Helpful comments from three anonymous referees and two associate editors are also gratefully acknowledged. Zhenlin Yang gratefully acknowledges the research support from Singapore Management University under Grant C244/MSS16E003.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/10
Y1 - 2021/10
N2 - We propose an Adjusted Quasi-Score (AQS) method for constructing tests for homoskedasticity in spatial econometric models. We first obtain an AQS function by adjusting the score-type function from the given model to achieve unbiasedness, and then develop an Outer-Product-of-Martingale-Difference (OPMD) estimate of its variance. In standard problems where a genuine (quasi) score vector is available, the AQS–OPMD method leads to finite sample improved tests over the usual methods. More importantly in non-standard problems where a genuine (quasi) score is not available and the usual methods fail, the proposed AQS–OPMD method provides feasible solutions. The AQS tests are formally derived and asymptotic properties examined for three representative models: spatial cross-sectional, static and dynamic panel models. Monte Carlo results show that the proposed AQS tests have good finite sample properties.
AB - We propose an Adjusted Quasi-Score (AQS) method for constructing tests for homoskedasticity in spatial econometric models. We first obtain an AQS function by adjusting the score-type function from the given model to achieve unbiasedness, and then develop an Outer-Product-of-Martingale-Difference (OPMD) estimate of its variance. In standard problems where a genuine (quasi) score vector is available, the AQS–OPMD method leads to finite sample improved tests over the usual methods. More importantly in non-standard problems where a genuine (quasi) score is not available and the usual methods fail, the proposed AQS–OPMD method provides feasible solutions. The AQS tests are formally derived and asymptotic properties examined for three representative models: spatial cross-sectional, static and dynamic panel models. Monte Carlo results show that the proposed AQS tests have good finite sample properties.
KW - Adjusted quasi-scores
KW - Fixed effects
KW - Heteroskedasticity
KW - Incidental parameters
KW - Martingale difference
KW - Non-normality
KW - Short dynamic panels
KW - Spatial effects
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U2 - 10.1016/j.jeconom.2020.10.002
DO - 10.1016/j.jeconom.2020.10.002
M3 - Article
AN - SCOPUS:85097065904
SN - 0304-4076
VL - 224
SP - 245
EP - 270
JO - Journal of Econometrics
JF - Journal of Econometrics
IS - 2
ER -