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Full-Text Articles in Econometrics

Adaptive Nonparametric Regression With Conditional Heteroskedasticity, Sainan Jin, Liangjun Su, Zhijie Xiao Dec 2015

Adaptive Nonparametric Regression With Conditional Heteroskedasticity, Sainan Jin, Liangjun Su, Zhijie Xiao

Research Collection School Of Economics

In this paper, we study adaptive nonparametric regression estimation in the presence of conditional heteroskedastic error terms. We demonstrate that both the conditional mean and conditional variance functions in a nonparametric regression model can be estimated adaptively based on the local profile likelihood principle. Both the one-step Newton-Raphson estimator and the local profile likelihood estimator are investigated. We show that the proposed estimators are asymptotically equivalent to the infeasible local likelihood estimators [e.g., Aerts and Claeskens (1997) Journal of the American Statistical Association 92, 1536-1545], which require knowledge of the error distribution. Simulation evidence suggests that when the distribution of …

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New Methodology For Constructing Real Estate Price Indices Applied To The Singapore Residential Market, Liang Jiang, Peter C. B. Phillips, Jun Yu Dec 2015

New Methodology For Constructing Real Estate Price Indices Applied To The Singapore Residential Market, Liang Jiang, Peter C. B. Phillips, Jun Yu

Research Collection School Of Economics

This paper develops a new methodology for constructing a real estate price index that utilizes all transaction price information, encompassing both single-sales and repeat-sales. The method is less susceptible to specification error than standard hedonic methods and is not subject to the sample selection bias involved in indexes that rely only on repeat sales. The methodology employs a model design that uses a sale pairing process based on the individual building level, rather than the individual house level as is used in the repeat-sales method. The approach extends ideas from repeat-sales methodology in a way that accommodates much wider datasets. …

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Intraday Value-At-Risk: An Asymmetric Autoregressive Conditional Duration Approach, Shouwei Liu, Yiu Kuen Tse Dec 2015

Intraday Value-At-Risk: An Asymmetric Autoregressive Conditional Duration Approach, Shouwei Liu, Yiu Kuen Tse

Research Collection School Of Economics

We propose to compute the Intraday Value-at-Risk (IVaR) for stocks using real-time transaction data. Tick-by-tick data filtered by price duration are modeled using a two-state asymmetric autoregressive conditional duration (AACD) model, and the IVaR is calculated using Monte Carlo simulation based on the estimated AACD model. Backtesting results for the New York Stock Exchange (NYSE) show that the IVaR calculated using the AACD method outperforms those using the Dionne et al. (2009) and Giot (2005) methods.

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Linear Programming-Based Estimators In Nonnegative Autoregression, Daniel P. A. Preve Dec 2015

Linear Programming-Based Estimators In Nonnegative Autoregression, Daniel P. A. Preve

Research Collection School Of Economics

This note studies robust estimation of the autoregressive (AR) parameter in a nonlinear, nonnegative AR model. It is shown that a linear programming estimator (LPE), considered by Nielsen and Shephard (2003) among others, remains consistent under severe model misspecification. Consequently, the LPE can be used to seek sources of misspecification and to isolate certain trend, seasonal or cyclical components. Simple and quite general conditions under which the LPE is strongly consistent in the presence of heavy-tailed, serially correlated, heteroskedastic disturbances are given, and a brief review of the literature on LP-based estimators in nonnegative autoregression is presented. Finite-sample properties of …

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Testing For Multiple Bubbles: Limit Theory Of Real-Time Detectors, Peter C. B. Phillips, Shuping Shi, Jun Yu Nov 2015

Testing For Multiple Bubbles: Limit Theory Of Real-Time Detectors, Peter C. B. Phillips, Shuping Shi, Jun Yu

Research Collection School Of Economics

This article provides the limit theory of real-time dating algorithms for bubble detection that were suggested in Phillips, Wu, and Yu (PWY; International Economic Review 52 [2011], 201-26) and in a companion paper by the present authors (Phillips, Shi, and Yu, 2015; PSY; International Economic Review 56 [2015a], 1099-1134. Bubbles are modeled using mildly explosive bubble episodes that are embedded within longer periods where the data evolve as a stochastic trend, thereby capturing normal market behavior as well as exuberance and collapse. Both the PWY and PSY estimates rely on recursive right-tailed unit root tests (each with a different recursive …

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Supplement To Two Papers On Multiple Bubbles [Online Supplementary Materials], Peter C. B. Phillips, Shuping Shi, Jun Yu Nov 2015

Supplement To Two Papers On Multiple Bubbles [Online Supplementary Materials], Peter C. B. Phillips, Shuping Shi, Jun Yu

Research Collection School Of Economics

This paper provides a supplement to two companion papers by the authors: “Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500” (PSY1 hereafter); and “Testing for Multiple Bubbles: Limit Theory of Real Time Detectors” (PSY2 hereafter). Section 1 supplements the empirical application of PSY1 by examining the robustness of the bubble identification and dating results to the choice of the minimum window size parameter used in the rolling regression framework of PSY. Section 2 provides proofs of supplementary lemmas that facilitate analysis of the multiple bubble case, derives the limit behaviour of the recursive unit …

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Improved Inferences For Spatial Regression Models, Shew Fan Liu, Zhenlin Yang Nov 2015

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 …

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Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su Nov 2015

Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su

Research Collection School Of Economics

In this paper we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to …

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Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shuping Shi, Jun Yu Nov 2015

Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shuping Shi, Jun Yu

Research Collection School Of Economics

Recent work on econometric detection mechanisms has shown the effectiveness of recursive procedures in identifying and dating financial bubbles in real time. These procedures are useful as warning alerts in surveillance strategies conducted by central banks and fiscal regulators with real-time data. Use of these methods over long historical periods presents a more serious econometric challenge due to the complexity of the nonlinear structure and break mechanisms that are inherent in multiple-bubble phenomena within the same sample period. To meet this challenge, this article develops a new recursive flexible window method that is better suited for practical implementation with long …

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A Bayesian Chi-Squared Test For Hypothesis Testing, Yong Li, Xiaobin Liu, Jun Yu Nov 2015

A Bayesian Chi-Squared Test For Hypothesis Testing, Yong Li, Xiaobin Liu, Jun Yu

Research Collection School Of Economics

A new Bayesian test statistic is proposed to test a point null hypothesis based on a quadratic loss. The proposed test statistic may be regarded as the Bayesian version of the Lagrange multiplier test. Its asymptotic distribution is obtained based on a set of regular conditions and follows a chi-squared distribution when the null hypothesis is correct. The new statistic has several important advantages that make it appealing in practical applications. First, it is well-defined under improper prior distributions. Second, it avoids Jeffrey-Lindley's paradox. Third, it always takes a non-negative value and is relatively easy to compute, even for models …

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Model Selection In The Presence Of Incidental Parameters, Yeonseok Lee, Peter C. B. Phillips Oct 2015

Model Selection In The Presence Of Incidental Parameters, Yeonseok Lee, Peter C. B. Phillips

Research Collection School Of Economics

This paper considers model selection in panels where incidental parameters are present. Primary interest centers on selecting a model that best approximates the underlying structure involving parameters that are common within the panel. It is well known that conventional model selection procedures are often inconsistent in panel models and this can be so even without nuisance parameters. Modifications are then needed to achieve consistency. New model selection information criteria are developed here that use either the Kullback-Leibler information criterion based on the profile likelihood or the Bayes factor based on the integrated likelihood with a bias-reducing prior. These model selection …

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Bias-Correction For Weibull Common Shape Estimation, Yan Shen, Zhenlin Yang Oct 2015

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.

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Poverty Decomposition By Regression: An Application To Tanzania, Tomoki Fujii Oct 2015

Poverty Decomposition By Regression: An Application To Tanzania, Tomoki Fujii

Research Collection School Of Economics

We develop a poverty decomposition method that is based on a consumption regression model. Because this method uses an integral of the partial derivatives of a poverty measure with respect to time, the resulting poverty decomposition satisfies time-reversion consistency and sub-period additivity. Unlike the existing poverty decomposition methods, it allows us to ascribe the observed change in poverty to various covariates of interest collected at a disaggregate level. This method is applied to two datasets from Tanzania to assess, among others, the short- and long-term impacts of infrastructure and market access on poverty.

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Measure Of Location-Based Estimators In Simple Linear Regression, Xijia Liu, Daniel P. A. Preve Sep 2015

Measure Of Location-Based Estimators In Simple Linear Regression, Xijia Liu, Daniel P. A. Preve

Research Collection School Of Economics

In this paper we consider certain measure of location-based estimators (MLBEs) for the slope parameter in a linear regression model with a single stochastic regressor. The median-unbiased MLBEs are interesting as they can be robust to heavy-tailed samples and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the MLBEs. In the first case, the regressor and error are assumed to follow a symmetric stable distribution. In the second, other types of regressions, with potentially contaminated errors, are considered. For both cases the consistency and exact finite-sample distributions …

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Semiparametric Estimation Of Partially Linear Dynamic Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang Sep 2015

Semiparametric Estimation Of Partially Linear Dynamic Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang

Research Collection School Of Economics

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite dimensional parameter and a local polynomial estimator for the infinite dimensional parameter m based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the …

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Jackknife Model Averaging For Quantile Regressions, Xun Lu, Liangjun Su Sep 2015

Jackknife Model Averaging For Quantile Regressions, Xun Lu, Liangjun Su

Research Collection School Of Economics

In this paper we consider model averaging for quantile regressions (QR) when all models under investigation are potentially misspecified and the number of parameters is diverging with the sample size. To allow for the dependence between the error terms and regressors in the QR models, we propose a jackknife model averaging (JMA) estimator which selects the weights by minimizing a leave-one-out cross-validation criterion function and demonstrate its asymptotic optimality in terms of minimizing the out-of-sample final prediction error. We conduct simulations to demonstrate the finite-sample performance of our estimator and compare it with other model selection and averaging methods. We …

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Bias In The Estimation Of Mean Reversion In Continuous-Time Levy Processes, Yong Bao, Aman Ullah, Yun Wang, Jun Yu Sep 2015

Bias In The Estimation Of Mean Reversion In Continuous-Time Levy Processes, Yong Bao, Aman Ullah, Yun Wang, Jun Yu

Research Collection School Of Economics

This paper develops the approximate bias of the ordinary least squares estimator of the mean reversion parameter in continuous-time Levy processes. Several cases are considered, depending on whether the long-run mean is known or unknown and whether the initial condition is fixed or random. The approximate bias is used to construct a bias corrected estimator. The performance of the approximate bias and the bias corrected estimator is examined using simulated data.

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Memorial To Edmond Malinvaud, Peter C. B. Phillips Jun 2015

Memorial To Edmond Malinvaud, Peter C. B. Phillips

Research Collection School Of Economics

A great man, a wide-ranging thinker and writer, a pre-eminent researcher, andan inspiring educator passed away on 7 March 2015, leaving the world of economicsso much the poorer and narrower. Edmond Malinvaud was a giant amongmany giants in the subject and one who strode uniquely and comfortably acrossthe entire discipline like an academic colossus

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Halbert White Jr. Memorial Jfec Lecture: Pitfalls And Possibilities In Predictive Regression, Peter C. B. Phillips Jun 2015

Halbert White Jr. Memorial Jfec Lecture: Pitfalls And Possibilities In Predictive Regression, Peter C. B. Phillips

Research Collection School Of Economics

Financial theory and econometric methodology both struggle in formulating models that are logically sound in reconciling short-run martingale behavior for financial assets with predictable long-run behavior, leaving much of the research to be empirically driven. The present article overviews recent contributions to this subject, focusing on the main pitfalls in conducting predictive regression and on some of the possibilities offered by modern econometric methods. The latter options include indirect inference and techniques of endogenous instrumentation that use convenient temporal transforms of persistent regressors. Some additional suggestions are made for bias elimination, quantile crossing amelioration, and control of predictive model misspecification.

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Automated Estimation Of Vector Error Correction Models, Zhipeng Liao, Peter C. B. Phillips Jun 2015

Automated Estimation Of Vector Error Correction Models, Zhipeng Liao, Peter C. B. Phillips

Research Collection School Of Economics

Model selection and associated issues of post-model selection inference present well known challenges in empirical econometric research. These modeling issues are manifest in all applied work but they are particularly acute in multivariate time series settings such as cointegrated systems where multiple interconnected decisions can materially affect the form of the model and its interpretation. In cointegrated system modeling, empirical estimation typically proceeds in a stepwise manner that involves the determination of cointegrating rank and autoregressive lag order in a reduced rank vector autoregression followed by estimation and inference. This paper proposes an automated approach to cointegrated system modeling that …

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Asymptotic Distribution And Finite-Sample Bias Correction Of Qml Estimators For Spatial Dependence Model, Shew Fan Liu, Zhenlin Yang May 2015

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 …

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Limit Theory For Vars With Mixed Roots Near Unity, Peter C. B. Phillips, Ji Hyung Lee May 2015

Limit Theory For Vars With Mixed Roots Near Unity, Peter C. B. Phillips, Ji Hyung Lee

Research Collection School Of Economics

Limit theory is developed for nonstationary vector autoregression (VAR) with mixed roots in the vicinity of unity involving persistent and explosive components. Statistical tests for common roots are examined and model selection approaches for discriminating roots are explored. The results are useful in empirical testing for multiple manifestations of nonstationarity - in particular for distinguishing mildly explosive roots from roots that are local to unity and for testing commonality in persistence.

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Modified Qml Estimation Of Spatial Autoregressive Models With Unknown Heteroskedasticity And Nonnormality, Shew Fan Liu, Zhenlin Yang May 2015

Modified Qml Estimation Of Spatial Autoregressive Models With Unknown Heteroskedasticity And Nonnormality, Shew Fan Liu, Zhenlin Yang

Research Collection School Of Economics

In the presence of heteroskedasticity, Lin and Lee (2010) show that the quasi-maximum likelihood (QML) estimator of the spatial autoregressive (SAR) model can be inconsistent as a ‘necessary’ condition for consistency can be violated, and thus propose robust GMM estimators for the model. In this paper, we first show that this condition may hold in certain situations and when it does the regular QML estimator can still be consistent. In cases where this condition is violated, we propose a simple modified QML estimation method robust against unknown heteroskedasticity. In both cases, asymptotic distributions of the estimators are derived, and methods …

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Testing Additive Separability Of Error Term In Nonparametric Structural Models, Liangjun Su, Yundong Tu, Aman Ullah May 2015

Testing Additive Separability Of Error Term In Nonparametric Structural Models, Liangjun Su, Yundong Tu, Aman Ullah

Research Collection School Of Economics

This paper considers testing additive error structure in nonparametric structural models, against the alternative hypothesis that the random error term enters the nonparametric model non-additively. We propose a test statistic under a set of identification conditions considered by Hoderlein, Su and White (2012), which require the existence of a control variable such that the regressor is independent of the error term given the control variable. The test statistic is motivated from the observation that, under the additive error structure, the partial derivative of the nonparametric structural function with respect to the error term is one under identification. The asymptotic distribution …

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Specification Test For Panel Data Models With Interactive Fixed Effects, Liangjun Su, Sainan Jin, Yonghui Zhang May 2015

Specification Test For Panel Data Models With Interactive Fixed Effects, Liangjun Su, Sainan Jin, Yonghui Zhang

Research Collection School Of Economics

In this paper, we propose a consistent nonparametric test for linearity in panel data models with interactive fixed effects. To construct the test statistic, we need to estimate the model under the null hypothesis of linearity and then obtain the restricted residuals. We show that after being appropriately centered and standardized, the test statistic is asymptotically normally distributed both under the null hypothesis and a sequence of Pitman local alternatives. To improve the finite sample performance, we propose a bootstrap procedure to obtain the bootstrap p-values. A small set of Monte Carlo simulations illustrates that our test performs well in …

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A General Method For Third-Order Bias And Variance Corrections On A Nonlinear Estimator, Zhenlin Yang May 2015

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 …

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On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang May 2015

On Time-Varying Factor Models: Estimation And Testing, Liangjun Su, Xia Wang

Research Collection School Of Economics

Conventional factor models assume that factor loadings are fixed over a long horizon of time, which appears overly restrictive and unrealistic in applications. In this paper, we introduce a time-varying factor model where factor loadings are allowed to change smoothly over time. We propose a local version of the principal component method to estimate the latent factors and time-varying factor loadings simultaneously. We establish the limiting distributions of the estimated factors and factor loadings in the standard large N and large T framework. We also propose a BIC-type information criterion to determine the number of factors, which can be used …

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A Bayesian Specification Test, Yong Li, Tao Zeng, Jun Yu May 2015

A Bayesian Specification Test, Yong Li, Tao Zeng, Jun Yu

Research Collection School Of Economics

A Bayesian test statistic is proposed to assess the model specification after the model is estimated by Bayesian MCMC methods. The proposed approach does not require an alternative model to be specified and is applicable to a variety of models, including latent variable models, structural dynamic choice models, and dynamics stochastic general equilibrium (DSGE) models, for which frequentist methods are difficult to use. The properties of the test statistic are established and its implementation is discussed. The test is easy to use and the test statistic can be calculated from MCMC outputs even when there are latent variables. The method …

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Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin Apr 2015

Sieve Estimation Of Time-Varying Panel Data Models With Latent Structures, Liangjun Su, Xia Wang, Sainan Jin

Research Collection School Of Economics

We consider the problem of determining the number of factors and selecting the proper regressors in linear dynamic panel data models with interactive fixed effects. Based on the preliminary estimates of the slope parameters and factors a la Bai and Ng (2009) and Moon andWeidner (2014a), we propose a method for simultaneous selection of regressors and factors and estimation through the method of adaptive group Lasso (least absolute shrinkage and selection operator). We show that with probability approaching one, our method can correctly select all relevant regressors and factors and shrink the coefficients of irrelevant regressors and redundant factors to …

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Optimal Jackknife For Unit Root Models, Ye Chen, Jun Yu Apr 2015

Optimal Jackknife For Unit Root Models, Ye Chen, Jun Yu

Research Collection School Of Economics

A new jackknife method is introduced to remove the first order bias in unit root models. It is optimal in the sense that it minimizes the variance among all the jackknife estimators of the form considered in Phillips and Yu (2005) and Chambers and Kyriacou (2013) after the number of subsamples is selected. Simulations show that the new jackknife reduces the variance of that of Chambers and Kyriacou by about 10% for any selected number of subsamples without compromising bias reduction. The results continue to hold true in near unit root models. (C) 2014 Elsevier B.V. All rights reserved.

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