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Articles 361 - 390 of 828
Full-Text Articles in Econometrics
Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White
Granger Causality And Structural Causality In Cross-Section And Panel Data, Xun Lu, Liangjun Su, Halbert White
Research Collection School Of Economics
Granger non-causality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to cross-section and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G-) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable, G- non-causality follows from structural non-causality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G-causality. This justifies using tests of G- non-causality …
Common Threshold In Quantile Regressions With An Application To Pricing For Reputation, Liangjun Su, Pai Xu, Heng Ju
Common Threshold In Quantile Regressions With An Application To Pricing For Reputation, Liangjun Su, Pai Xu, Heng Ju
Research Collection School Of Economics
The paper develops a systematic estimation and inference procedure for quantile regression models where there may exist a common threshold effect across different quantile indices. We first propose a sup-Wald test for the existence of a threshold effect, and then study the asymptotic properties of the estimators in a threshold quantile regression model under the shrinking-threshold-effect framework. We consider several tests for the presence of a common threshold value across different quantile indices and obtain their limiting distributions. We apply our methodology to study the pricing strategy for reputation via the use of a dataset from Taobao.com. In our economic …
New Distribution Theory For The Estimation Of Structural Break Point In Mean, Liang Jiang, Xiaohu Wang, Jun Yu
New Distribution Theory For The Estimation Of Structural Break Point In Mean, Liang Jiang, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
Based on the Girsanov theorem, this paper first obtains the exact distribution of the maximum likelihood estimator of structural break point in a continuous time model. The exact distribution is asymmetric and tri-modal, indicating that the estimator is seriously biased. These two properties are also found in the finite sample distribution of the least squares estimator of structural break point in the discrete time model. The paper then builds a continuous time approximation to the discrete time model and develops an in-fill asymptotic theory for the least squares estimator. The obtained in-fill asymptotic distribution is asymmetric and tri-modal and delivers …
Semiparametric Estimation Of Partially Linear Dynamic Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang
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 θ 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 parametric and …
Meritocracy Voting: Measuring The Unmeasurable, Peter C. B. Phillips
Meritocracy Voting: Measuring The Unmeasurable, Peter C. B. Phillips
Research Collection School Of Economics
Learned societies commonly carry out selection processes to add new fellows to an existing fellowship. Criteria vary across societies but are typically based on subjective judgments concerning the merit of individuals who are nominated for fellowships. These subjective assessments may be made by existing fellows as they vote in elections to determine the new fellows or they may be decided by a selection committee of fellows and officers of the society who determine merit after reviewing nominations and written assessments. Human judgment inevitably plays a central role in these determinations and, notwithstanding its limitations, is usually regarded as being a …
Shrinkage Estimation Of Dynamic Panel Data Models With Interactive Fixed Effects, Xun Lu, Liangjun Su
Shrinkage Estimation Of Dynamic Panel Data Models With Interactive Fixed Effects, Xun Lu, Liangjun Su
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 (2009) and Moon and Weidner (2015), 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 zero. …
A Tale Of Two Option Markets: Pricing Kernels And Volatility Risk, Zhaogang Song, Dacheng Xiu
A Tale Of Two Option Markets: Pricing Kernels And Volatility Risk, Zhaogang Song, Dacheng Xiu
Research Collection Lee Kong Chian School Of Business
Using both S&P 500 option and recently introduced VIX option prices, we study pricing kernels and their dependence on multiple volatility factors. We first propose nonparametric estimates of marginal pricing kernels, conditional on the VIX and the slope of the variance swap term structure. Our estimates highlight the state-dependence nature of the pricing kernels. In particular, conditioning on volatility factors, the pricing kernel of market returns exhibit a downward sloping shape up to the extreme end of the right tail. Moreover, the volatility pricing kernel features a striking U-shape, implying that investors have high marginal utility in both high and …
Unified M-Estimation Of Fixed-Effects Spatial Dynamic Models With Short Panels, Zhenlin Yang
Unified M-Estimation Of Fixed-Effects Spatial Dynamic Models With Short Panels, Zhenlin Yang
Research Collection School Of Economics
It is well known that quasi maximum likelihood (QML) estimation of dynamic panel data (DPD) models with short panels depends on the assumptions on the initial values, and a wrong treatment of them will result in inconsistency and serious bias. The same issues apply to spatial DPD (SDPD) models with short panels. In this paper, a unified Mestimation method is proposed for estimating the fixed-effects SDPD models containing three major types of spatial effects, namely spatial lag, spatial error and space-time lag. The method is free from the specification of the distribution of the initial observations and robust against nonnormality …
Adaptive Nonparametric Regression With Conditional Heteroskedasticity, Sainan Jin, Liangjun Su, Zhijie Xiao
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 …
New Methodology For Constructing Real Estate Price Indices Applied To The Singapore Residential Market, Liang Jiang, Peter C. B. Phillips, Jun Yu
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. …
Intraday Value-At-Risk: An Asymmetric Autoregressive Conditional Duration Approach, Shouwei Liu, Yiu Kuen Tse
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.
Linear Programming-Based Estimators In Nonnegative Autoregression, Daniel P. A. Preve
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 …
Testing For Multiple Bubbles: Limit Theory Of Real-Time Detectors, Peter C. B. Phillips, Shuping Shi, Jun Yu
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 …
Supplement To Two Papers On Multiple Bubbles [Online Supplementary Materials], Peter C. B. Phillips, Shuping Shi, Jun Yu
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 …
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 …
Panel Data Models With Interactive Fixed Effects And Multiple Structural Breaks, Degui Li, Junhui Qian, Liangjun Su
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 …
Testing For Multiple Bubbles: Historical Episodes Of Exuberance And Collapse In The S&P 500, Peter C. B. Phillips, Shuping Shi, Jun Yu
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 …
A Bayesian Chi-Squared Test For Hypothesis Testing, Yong Li, Xiaobin Liu, Jun Yu
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 …
Model Selection In The Presence Of Incidental Parameters, Yeonseok Lee, Peter C. B. Phillips
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 …
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.
Poverty Decomposition By Regression: An Application To Tanzania, Tomoki Fujii
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.
Measure Of Location-Based Estimators In Simple Linear Regression, Xijia Liu, Daniel P. A. Preve
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 …
Semiparametric Estimation Of Partially Linear Dynamic Panel Data Models With Fixed Effects, Liangjun Su, Yonghui Zhang
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 …
Jackknife Model Averaging For Quantile Regressions, Xun Lu, Liangjun Su
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 …
Bias In The Estimation Of Mean Reversion In Continuous-Time Levy Processes, Yong Bao, Aman Ullah, Yun Wang, Jun Yu
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.
Memorial To Edmond Malinvaud, Peter C. B. Phillips
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
Halbert White Jr. Memorial Jfec Lecture: Pitfalls And Possibilities In Predictive Regression, Peter C. B. Phillips
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.
Automated Estimation Of Vector Error Correction Models, Zhipeng Liao, Peter C. B. Phillips
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 …
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 …
Limit Theory For Vars With Mixed Roots Near Unity, Peter C. B. Phillips, Ji Hyung Lee
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.