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Full-Text Articles in Social and Behavioral Sciences
Testing The Dimensionality Of Policy Shocks, Jia Li, Viktor Todorov, Qiushi. Zhang
Testing The Dimensionality Of Policy Shocks, Jia Li, Viktor Todorov, Qiushi. Zhang
Research Collection School Of Economics
This paper provides a nonparametric test for deciding the dimensionality of a policy shock as manifest in the abnormal change in asset returns' stochastic covariance matrix, following the release of a macroeconomic announcement. We use high-frequency data in local windows before and after the event to estimate the covariance jump matrix, and then test its rank. We find a one-factor structure in the covariance jump matrix of the yield curve resulting from the Federal Reserve's monetary policy shocks prior to the 2007-2009 financial crisis. The dimensionality of policy shocks increased afterwards due to the use of unconventional monetary policy tools.
A Consistent Specification Test For Dynamic Quantile Models, Peter Horvath, Jia Li, Zhipeng Liao, Andrew J. Patton
A Consistent Specification Test For Dynamic Quantile Models, Peter Horvath, Jia Li, Zhipeng Liao, Andrew J. Patton
Research Collection School Of Economics
Correct specification of a conditional quantile model implies that a particular conditional moment is equal to zero. We nonparametrically estimate the conditional moment function via series regression and test whether it is identically zero using uniform functional inference. Our approach is theoretically justified via a strong Gaussian approximation for statistics of growing dimensions in a general time series setting. We propose a novel bootstrap method in this nonstandard context and show that it significantly outperforms the benchmark asymptotic approximation in finite samples, especially for tail quantiles such as Value-at-Risk (VaR). We use the proposed new test to study the VaR …
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Yichong Zhang, Xin Zheng
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Yichong Zhang, Xin Zheng
Research Collection School Of Economics
In this paper, we study the estimation and inference of the quantile treatment effect under covariate‐adaptive randomization. We propose two estimation methods: (1) the simple quantile regression and (2) the inverse propensity score weighted quantile regression. For the two estimators, we derive their asymptotic distributions uniformly over a compact set of quantile indexes, and show that, when the treatment assignment rule does not achieve strong balance, the inverse propensity score weighted estimator has a smaller asymptotic variance than the simple quantile regression estimator. For the inference of method (1), we show that the Wald test using a weighted bootstrap standard …
M-Estimators Of U-Processes With A Change-Point Due To A Covariate Threshold, Lili Tan, Yichong Zhang
M-Estimators Of U-Processes With A Change-Point Due To A Covariate Threshold, Lili Tan, Yichong Zhang
Research Collection School Of Economics
Economic theory often predicts a “tipping point” effect due to multiple equilibria. Linear threshold regressions estimate the “tipping point” by assuming at the same time that the response variable is linear in an index of covariates. However, economic theory rarely imposes a specific functional form, but rather predicts a monotonic relationship between the response variable and the index. We propose new, rank-based, estimators for both the “tipping point” and other regression coefficients, exploiting only the monotonicity condition. We derive the asymptotic properties of these estimators by establishing a more general result for M-estimators of U-processes with a change-point due to …
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Xin Zheng, Yichong Zhang
Quantile Treatment Effects And Bootstrap Inference Under Covariate-Adaptive Randomization, Xin Zheng, Yichong Zhang
Research Collection School Of Economics
This paper studies the estimation and inference of the quantile treatment effect under covariate-adaptive randomization. We propose three estimation methods: (1) the simple quantile regression (QR), (2) the QR with strata fixed effects, and (3) the inverse propensity score weighted QR. For the three estimators, we derive their asymptotic distributions uniformly over a set of quantile indexes and show that the estimator obtained from inverse propensity score weighted QR weakly dominates the other two in terms of efficiency, for a wide range of randomization schemes. For inference, we show that the weighted bootstrap tends to be conservative for methods (1) …
Bootstrap Lm Tests For Higher-Order Spatial Effects In Spatial Linear Regression Models, Zhenlin Yang
Bootstrap Lm Tests For Higher-Order Spatial Effects In Spatial Linear Regression Models, Zhenlin Yang
Research Collection School Of Economics
This paper first extends the methodology of Yang (J Econom 185:33-59, 2015) to allow for non-normality and/or unknown heteroskedasticity in obtaining asymptotically refined critical values for the LM-type tests through bootstrap. Bootstrap refinements in critical values require the LM test statistics to be asymptotically pivotal under the null hypothesis, and for this we provide a set of general methods for constructing LM and robust LM tests. We then give detailed treatments for two general higher-order spatial linear regression models: namely the model and the model, by providing a complete set of non-normality robust LM and bootstrap LM tests for higher-order …
Improved Likelihood Inferences For Weibull Regression Model, Yan Shen, Zhenlin Yang
Improved Likelihood Inferences For Weibull Regression Model, Yan Shen, Zhenlin Yang
Research Collection School Of Economics
A general procedure is developed for bias-correcting the maximum likelihood estimators (MLEs) of the parameters of Weibull regression model with either complete or right-censored data. Following the bias correction, variance corrections and hence improved t-ratios for model parameters are presented. Potentially improved t-ratios for other reliability-related quantities are also discussed. Simulation results show that the proposed method is effective in correcting the bias of the MLEs, and the resulted t-ratios generally improve over the regular t-ratios.
Bias Correction And Refined Inferences For Fixed Effects Spatial Panel Data Models, Zhenlin Yang, Jihai Yu, Shew Fan Liu
Bias Correction And Refined Inferences For Fixed Effects Spatial Panel Data Models, Zhenlin Yang, Jihai Yu, Shew Fan Liu
Research Collection School Of Economics
This paper first presents simple methods for conducting up to third-order bias and variance corrections for the quasi maximum likelihood (QML) estimators of the spatial parameter(s) in the fixed effects spatial panel data (FE-SPD) models. Then, it shows how the bias and variance corrections lead to refined t-ratios for spatial effects and for covariate effects. The implementation of these corrections depends on the proposed bootstrap methods of which validity is established. Monte Carlo results reveal that (i) the QML estimators of the spatial parameters can be quite biased, (ii) a second-order bias correction effectively removes the bias, and (iii) the …
A Practical Test For Strict Exogeneity In Linear Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang, Jie Wei
A Practical Test For Strict Exogeneity In Linear Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang, Jie Wei
Research Collection School Of Economics
This paper provides a practical test for strict exogeneity in linear panel data models with fixed effects when the number of individuals N goes to infinity while the number of time periods T is fixed. The test is based on the supremum of a sequence of Wald test statistics. Under suitable conditions, we establish the asymptotic distribution of the test statistic and consistency of the test. A bootstrap procedure is proposed to improve the finite sample performance and the validity of the procedure is justified. We investigate the finite sample performance of the test via a small set of Monte …
Improved Inferences For Spatial Regression Models, Shew Fan Liu, Zhenlin Yang
Improved Inferences For Spatial Regression Models, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
The quasi-maximum likelihood (QML) method is popular in the estimation and inference for spatial regression models. However, the QML estimators (QMLEs) of the spatial parameters can be quite biased and hence the standard inferences for the regression coefficients (based on t-ratios) can be seriously affected. This issue, however, has not been addressed. The QMLEs of the spatial parameters can be bias-corrected based on the general method of Yang (2015b, J. of Econometrics 186, 178-200). In this paper, we demonstrate that by simply replacing the QMLEs of the spatial parameters by their bias-corrected versions, the usual t-ratios for the regression coefficients …
Bias-Correction For Weibull Common Shape Estimation, Yan Shen, Zhenlin Yang
Bias-Correction For Weibull Common Shape Estimation, Yan Shen, Zhenlin Yang
Research Collection School Of Economics
A general method for correcting the bias of the maximum likelihood estimator (MLE) of the common shape parameter of Weibull populations, allowing a general right censorship, is proposed in this paper. Extensive simulation results show that the new method is very effective in correcting the bias of the MLE, regardless of censoring mechanism, sample size, censoring proportion and number of populations involved. The method can be extended to more complicated Weibull models.
Asymptotic Distribution And Finite Sample Bias Correction Of Qml Estimators For Spatial Error Dependence Model, Shew Fan Liu, Zhenlin Yang
Asymptotic Distribution And Finite Sample Bias Correction Of Qml Estimators For Spatial Error Dependence Model, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model, as …
A General Method For Third-Order Bias And Variance Corrections On A Nonlinear Estimator, Zhenlin Yang
A General Method For Third-Order Bias And Variance Corrections On A Nonlinear Estimator, Zhenlin Yang
Research Collection School Of Economics
Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with concentrated estimating equation simplifies greatly the high-order expansions for bias and variance; a simple bootstrap procedure overcomes a major difficulty in analytically evaluating expectations of various quantities in the expansions. The method is then studied in detail using a more general SAR model, with its effectiveness in correcting bias and improving inference fully demonstrated …
Asymptotic Distribution And Finite-Sample Bias Correction Of Qml Estimators For Spatial Dependence Model, Shew Fan Liu, Zhenlin Yang
Asymptotic Distribution And Finite-Sample Bias Correction Of Qml Estimators For Spatial Dependence Model, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
In studying the asymptotic and finite sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the applications of this model, as …
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Lm Tests Of Spatial Dependence Based On Bootstrap Critical Values, Zhenlin Yang
Research Collection School Of Economics
To test the existence of spatial dependence in an econometric model, a convenient test is the Lagrange Multiplier (LM) test. However, evidence shows that, in finite samples, the LM test referring to asymptotic critical values may suffer from the problems of size distortion and low power, which become worse with a denser spatial weight matrix. In this paper, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values of the LM statistics. Conditions for their validity are clearly laid out and formal justifications are given in general, and in detail under several popular spatial LM …
Bias Correction For Fixed Effects Spatial Panel Data Models, Zhenlin Yang, Jihai Yu, Shew Fan Liu
Bias Correction For Fixed Effects Spatial Panel Data Models, Zhenlin Yang, Jihai Yu, Shew Fan Liu
Research Collection School Of Economics
This paper examines the finite sample properties of the quasi maximum likelihood (QML) estimators of the fixed effects spatial panel data (FE-SPD) models of Lee and Yu (2010). Following the general bias correction methods recently developed by Yang (2015), we derive up to third-order bias corrections for the QML estimators of the FE-SPD model, and propose a simple bootstrap method for their practical implementation. Monte Carlo results reveal that the QML estimators of the spatial parameters can be quite biased and that a second-order bias correction effectively removes the bias. The validity of the bootstrap method is established. Variance corrections …
Asymptotic Distribution And Finite-Sample Bias Correction Of Qml Estimators For Spatial Dependence Model, Shew Fan Liu, Zhenlin Yang
Asymptotic Distribution And Finite-Sample Bias Correction Of Qml Estimators For Spatial Dependence Model, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
In studying the asymptotic and finite-sample properties of quasi-maximum likelihood (QML) estimators for the spatial linear regression models, much attention has been paid to the spatial lag dependence (SLD) model; little has been given to its companion, the spatial error dependence (SED) model. In particular, the effect of spatial dependence on the convergence rate of the QML estimators has not been formally studied, and methods for correcting finite-sample bias of the QML estimators have not been given. This paper fills in these gaps. Of the two, bias correction is particularly important to the application of this model. Contrary to the …
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Research Collection School Of Economics
This paper overviews some recent advances on simulatio n-based methods of estimating time series models and asset pricing models that are widely used in finance. The simulation based methods have proven to be particularly useful when the likelihood function and moments do not have tractable forms and hence the maximum likelihood method (MLE) and the generalized method of moments (GMM) are difficult to use. They can also be useful for improving the finite sample performance of the traditional methods when financial time series are highly persistent and when the quantity of interest is a highly nonlinear function of system parameters.The …
Bias-Corrected Estimation For Spatial Autocorrelation, Zhenlin Yang
Bias-Corrected Estimation For Spatial Autocorrelation, Zhenlin Yang
Research Collection School Of Economics
The biasedness issue arising from the maximum likelihood estimation of the spatial autoregressive model (SAR) is further investigated under a broader set-up than that in Bao and Ullah (2007a). A major difficulty in analytically evaluating the expectations of ratios of quadratic forms is overcome by a simple bootstrap procedure. With that, the corrections on bias and variance of the spatial estimator can easily be made up to third-order, and once this is done, the estimators of other model parameters become nearly unbiased. Compared with the analytical approach, the new approach is much simpler, and can easily be extended to other …
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Research Collection School Of Economics
This paper overviews some recent advances on simulation-based methods of estimating time series models and asset pricing models that are widely used in finance. The simulation based methods have proven to be particularly useful when the likelihood function and moments do not have tractable forms and hence the maximum likelihood method (MLE) and the generalized method of moments (GMM) are difficult to use. They can also be useful for improving the finite sample performance of the traditional methods when financial time series are highly persistent and when the quantity of interest is a highly nonlinear function of system parameters. The …
Asymptotics And Bootstrap For Transformed Panel Data Regressions, Liangjun Su, Zhenlin Yang
Asymptotics And Bootstrap For Transformed Panel Data Regressions, Liangjun Su, Zhenlin Yang
Research Collection School Of Economics
This paper investigates the asymptotic properties of quasi-maximum likelihood estimators for transformed random effects models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoscedasticity, and simple model structure. We develop a quasi maximum likelihood-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the parameter estimates, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance matrix. Monte Carlo results reveal that these estimates perform well in finite samples, and that the gains by using bootstrap procedure for inference …
Information Recovery In A Study With Surrogate Endpoints, Song Xi Chen, Denis H. Y. Leung, Jing Qin
Information Recovery In A Study With Surrogate Endpoints, Song Xi Chen, Denis H. Y. Leung, Jing Qin
Research Collection School Of Economics
Recently, there has been a lot of interest in statistical methods for analyzing data with surrogate endpoints. In this article, we consider parameter estimation from a model that relates a variable Y to a set of covariates, X, in the presence of a surrogate, S. We assume that the data are made up of two random samples from the population, a validation set where (Y, X, S) are observed on every subject and a nonvalidation set where only (X, S) are measured. We show how information from the nonvalidation set can be incorporated to improve upon estimation of a parameter …
How Accurate Are Confidence Intervals For Impulse Responses In Large Var Models?, Lutz Kilian, Pao-Li Chang
How Accurate Are Confidence Intervals For Impulse Responses In Large Var Models?, Lutz Kilian, Pao-Li Chang
Research Collection School Of Economics
We study the finite-sample accuracy and average length of pointwise confidence intervals for impulse responses in vector autoregressive models with many variables and many lags. Our results complement existing simulation evidence based on much simpler bivariate models.