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Modeling Autoregressive Conditional Skewness and Kurtosis with Multi-Quantile CAViaR
Halbert White,Tae-Hwan Kim,Simone Manganelli 한국계량경제학회 2008 한국계량경제학회 학술대회 논문집 Vol.2008 No.2
Engle and Manganelli (2004) propose CAViaR, a class of models suitable for estimating conditional quantiles in dynamic settings. Engle and Manganelli apply their approach to the estimation of Value at Risk, but this is only one of many possible applications. Here we extend CAViaR models to permit joint modeling of multiple quantiles, Multi-Quantile (MQ) CAViaR. We apply our new methods to estimate measures of conditional skewness and kurtosis de…ned in terms of conditional quantiles, analogous to the unconditional quantile-based measures of skewness and kurtosis studied by Kim and White (2004). We investigate the performance of our methods by simulation, and we apply MQ-CAViaR to study conditional skewness and kurtosis of S&P 500 daily returns.
조진서,정다울,Halbert White 한국계량경제학회 2011 계량경제학보 Vol.22 No.2
We study the properties of the likelihood-ratio test for unobserved heterogeneity in duration models using mixtures of exponential and Weibull distributions proposed by Cho and White (2010). As they note, this involves a nuisance parameter identified only under the alternative. We apply the asymptotic critical values in Cho and White (2010) and compare these with Hansen's (1996) weighted bootstrap. Our Monte Carlo experiments show that the weighted bootstrap provides superior asymptotic critical values.
Jin Seo Cho,Ta Ul Cheong,Halbert White 한국계량경제학회 2011 JOURNAL OF ECONOMIC THEORY AND ECONOMETRICS Vol.22 No.2
We study the properties of the likelihood-ratio test for unobserved heterogeneity in duration models using mixtures of exponential and Weibull distributions proposed by Cho and White (2010). As they note, this involves a nuisance parameter identified only under the alternative. We apply the asymptotic critical values in Cho and White (2010) and compare these with Hansen’s (1996) weighted bootstrap. Our Monte Carlo experiments show that the weighted bootstrap provides superior asymptotic critical values.
Revisiting tests for neglected nonlinearity using artificial neural networks.
Cho, Jin Seo,Ishida, Isao,White, Halbert MIT Press 2011 Neural computation Vol.23 No.5
<P>Tests for regression neglected nonlinearity based on artificial neural networks (ANNs) have so far been studied by separately analyzing the two ways in which the null of regression linearity can hold. This implies that the asymptotic behavior of general ANN-based tests for neglected nonlinearity is still an open question. Here we analyze a convenient ANN-based quasi-likelihood ratio statistic for testing neglected nonlinearity, paying careful attention to both components of the null. We derive the asymptotic null distribution under each component separately and analyze their interaction. Somewhat remarkably, it turns out that the previously known asymptotic null distribution for the type 1 case still applies, but under somewhat stronger conditions than previously recognized. We present Monte Carlo experiments corroborating our theoretical results and showing that standard methods can yield misleading inference when our new, stronger regularity conditions are violated.</P>