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Articles 31 - 37 of 37
Full-Text Articles in Social and Behavioral Sciences
Asymptotic Inference About Predictive Accuracy Using High Frequency Data, Jia Li, Andrew J. Patton
Asymptotic Inference About Predictive Accuracy Using High Frequency Data, Jia Li, Andrew J. Patton
Research Collection School Of Economics
This paper provides a general framework that enables many existing inference methods for predictive accuracy to be used in applications that involve forecasts of latent target variables. Such applications include the forecasting of volatility, correlation, beta, quadratic variation, jump variation, and other functionals of an underlying continuous-time process. We provide primitive conditions under which a “negligibility” result holds, and thus the asymptotic size of standard predictive accuracy tests, implemented using a high-frequency proxy for the latent variable, is controlled. An extensive simulation study verifies that the asymptotic results apply in a range of empirically relevant applications, and an empirical application …
Extremal Quantile Regressions For Selection Models And The Black-White Wage Gap, Xavier D'Haultfoeuille, Arnaud Maurel, Yichong Zhang
Extremal Quantile Regressions For Selection Models And The Black-White Wage Gap, Xavier D'Haultfoeuille, Arnaud Maurel, Yichong Zhang
Research Collection School Of Economics
We consider the estimation of a semiparametric sample selection model without instrument or large support regressor. Identification relies on the independence between the covariates and selection, for arbitrarily large values of the outcome. We propose a simple estimator based on extremal quantile regression and establish its asymptotic normality by extending previous results on extremal quantile regressions to allow for selection. Finally, we apply our method to estimate the black-white wage gap among males from the NLSY79 and NLSY97. We find that premarket factors such as AFQT and family background play a key role in explaining the black-white wage gap.
Integrated Deviance Information Criterion For Latent Variable Models, Yong Li, Jun Yu, Tao Zeng
Integrated Deviance Information Criterion For Latent Variable Models, Yong Li, Jun Yu, Tao Zeng
Research Collection School Of Economics
Deviance information criterion (DIC) has been widely used for Bayesian model comparison, especially after Markov chain Monte Carlo (MCMC) is used to estimate candidate models. This paper studies the problem of using DIC to compare latent variable models after the models are estimated by MCMC together with the data augmentation technique. Our contributions are twofold. First, we show that when MCMC is used with data augmentation, it undermines theoretical underpinnings of DIC. As a result, by treating latent variables as parameters, the widely used way of constructing DIC based on the conditional likelihood, although facilitating computation, should not be used. …
Return And Volatility Spillovers Between The Renminbi And Asian Currencies, Hwee Kwan Chow-Tan
Return And Volatility Spillovers Between The Renminbi And Asian Currencies, Hwee Kwan Chow-Tan
Research Collection School Of Economics
This paper examines the extent of interdependence between the Chinese Renminbi and Asian currenciesafter the global financial crisis. We combine the distinct influence of offshore Renminbi with the impact ofthe onshore rate on eight Asian currencies (including the Australian dollar). Diebold-Yilmaz spilloverindexes reveal Asian foreign exchange markets are subject to considerable cross-border transmissions. Interms of the US dollar bilateral exchange rates, cross-border transfers of daily return are strongercompared to daily volatility reflecting currency management by regional authorities to curb excessiveexchange rate volatility. Return spillovers from the Renminbi markets to individual Asian foreignexchange markets are generally on par with that from …
Extremal Quantile Treatment Effects, Yichong Zhang
Extremal Quantile Treatment Effects, Yichong Zhang
Research Collection School Of Economics
This paper establishes an asymptotic theory and inference method for quantile treatment effect estimators when the quantile index is close to or equal to zero. Such quantile treatment effects are of interest in many applications, such as the effect of maternal smoking on an infant’s adverse birth outcomes. When the quantile index is close to zero, the sparsity of data jeopardizes conventional asymptotic theory and bootstrap inference. When the quantile index is zero, there are no existing inference methods directly applicable in the treatment effect context. This paper addresses both of these issues by proposing new inference methods that are …
Pythagorean Generalization Of Testing The Equality Of Two Symmetric Positive Definite Matrices, Jin Seo Cho, Peter C. B. Phillips
Pythagorean Generalization Of Testing The Equality Of Two Symmetric Positive Definite Matrices, Jin Seo Cho, Peter C. B. Phillips
Research Collection School Of Economics
We provide a new test for equality of two symmetric positive-definite matrices that leads to a convenient mechanism for testing specification using the information matrix equality or the sandwich asymptotic covariance matrix of the GMM estimator. The test relies on a new characterization of equality between two k dimensional symmetric positive-definite matrices A and B: the traces of AB−1 and BA−1 are equal to k if and only if A=B. Using this simple criterion, we introduce a class of omnibus test statistics for equality and examine their null and local alternative approximations under some mild regularity conditions. A preferred test …
Sequentially Testing Polynomial Model Hypotheses Using Power Transforms Of Regressors, Jin Seo Cho, Peter C. B. Phillips
Sequentially Testing Polynomial Model Hypotheses Using Power Transforms Of Regressors, Jin Seo Cho, Peter C. B. Phillips
Research Collection School Of Economics
We provide a methodology for testing a polynomial model hypothesis by generalizing the approach and results of Baek, Cho, and Phillips (Journal of Econometrics, 2015, 187, 376–384; BCP), which test for neglected nonlinearity using power transforms of regressors against arbitrary nonlinearity. We use the BCP quasi-likelihood ratio test and deal with the new multifold identification problem that arises under the null of the polynomial model. The approach leads to convenient asymptotic theory for inference, has omnibus power against general nonlinear alternatives, and allows estimation of an unknown polynomial degree in a model by way of sequential testing, a technique that …