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Articles 391 - 420 of 771
Full-Text Articles in Econometrics
Unit Roots In Life: A Graduate Student Story, Peter C. B. Phillips
Unit Roots In Life: A Graduate Student Story, Peter C. B. Phillips
Research Collection School Of Economics
What follows is a graduate student story. It draws on the first part of the speech I gave that evening at the NZESG conference dinner. It mixes personal reflections with recollections of the extraordinary New Zealanders who shaped my thinking as a graduate student and beginning researcher-people who have had an enduring impact on my work and career as an econometrician. The story traces out these human initial conditions and unit roots that figure in my early life of teaching and research.
Econometric Analysis Of Continuous Time Models: A Survey Of Peter Phillips' Work And Some New Results, Jun Yu
Research Collection School Of Economics
Econometric analysis of continuous time models has drawn the attention of Peter Phillips for 40 years, resulting in many important publications by him. In these publications he has dealt with a wide range of continuous time models and the associated econometric problems. He has investigated problems from univariate equations to systems of equations, from asymptotic theory to finite sample issues, from parametric models to nonparametric models, from identification problems to estimation and inference problems, from stationary models to nonstationary and nearly nonstationary models. This paper provides an overview of Peter Phillips' contributions in the continuous time econometrics literature. We review …
Robustify Financial Time Series Forecasting With Bagging, Sainan Jin, Liangjun Su, Aman Ullah
Robustify Financial Time Series Forecasting With Bagging, Sainan Jin, Liangjun Su, Aman Ullah
Research Collection School Of Economics
In this paper we propose a revised version of (bagging) bootstrap aggregating as a forecast combination method for the out-of-sample forecasts in time series models. The revised version explicitly takes into account the dependence in time series data and can be used to justify the validity of bagging in the reduction of mean squared forecast error when compared with the unbagged forecasts. Monte Carlo simulations show that the new method works quite well and outperforms the traditional one-step-ahead linear forecast as well as the nonparametric forecast in general, especially when the in-sample estimation period is small. We also find that …
Specification Test For Panel Data Models With Interactive Fixed Effects, Liangjun Su, Sainan Jin, Yonghui Zhang
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 a large dimensional panel data model with interactive fixed effects. Both lagged dependent variables and conditional heteroskedasticity of unknown form are allowed in the model. We estimate the model under the null hypothesis of linearity to obtain the restricted residuals which are then used to construct the test statistic. We show that after being appropriately centered and standardized, the test statistic is asymptotically normally distributed under both the null hypothesis and a sequence of Pitman local alternatives by using the concept of conditional strong mixing that was recently …
Minimum Investment Requirements, Financial Market Globalization, And Symmetry Breaking, Haiping Zhang
Minimum Investment Requirements, Financial Market Globalization, And Symmetry Breaking, Haiping Zhang
Research Collection School Of Economics
We incorporate wealth heterogeneity and the minimum investment requirements in the model of Matsuyama (2004, Econometrica) and provide a complete characterization of symmetry breaking. In particular, we identify the extensive margin of investment as a key channel through which the interest rate may respond positively to capital accumulation, or equivalently, the interest rate can be higher in the rich than in the poor countries. Then, financial market globalization may lead to “uphill” capital flows from the poor to the rich countries, which widens the initial cross-country income gap and leads to income divergence among inherently identical countries, a phenomenon that …
Shrinkage Estimation Of Regression Models With Multiple Structural Changes, Junhui Qian, Liangjun Su
Shrinkage Estimation Of Regression Models With Multiple Structural Changes, Junhui Qian, Liangjun Su
Research Collection School Of Economics
In this paper we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso (least absolute shrinkage and selection operator). We show that with probability tending to one our method can correctly determine the unknown number of breaks and the estimated break dates are sufficiently close to the true break dates. We obtain estimates of the regression coefficients via post Lasso and establish the asymptotic distributions of the estimates of both break ratios and regression coefficients. We also propose and validate a data-driven method to determine the tuning parameter. Monte Carlo …
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 the problem of frequentist model averaging for quantile regression (QR) when all the M models under investigation are potentially misspecified and the number of parameters in some or all models is diverging with the sample size n. To allow for the dependence between the error terms and the 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 that the jackknife selected weight vector is asymptotically optimal in terms of minimizing the out-of-sample final prediction error among the given …
A Combined Approach To The Inference Of Conditional Factor Models, Yan Li, Liangjun Su, Yuewu Xu
A Combined Approach To The Inference Of Conditional Factor Models, Yan Li, Liangjun Su, Yuewu Xu
Research Collection School Of Economics
This paper develops a new methodology for estimating and testing conditional factor models in finance. We propose a two-stage procedure that naturally unifies the two existing approaches in the finance literature -- the parametric approach and the nonparametric approach. Our combined approach possesses important advantages over both methods. Using our two-stage combined estimator, we derive new test statistics for investigating key hypotheses in the context of conditional factor models. Our tests can be performed on a single asset or jointly across multiple assets. We further propose a novel test to directly check whether the parametric model used in our first …
A Flexible And Automated Likelihood Based Framework For Inference In Stochastic Volatility Models, Hans J. Skaug, Jun Yu
A Flexible And Automated Likelihood Based Framework For Inference In Stochastic Volatility Models, Hans J. Skaug, Jun Yu
Research Collection School Of Economics
The Laplace approximation is used to perform maximum likelihood estimation of univariate and multivariate stochastic volatility (SV) models. It is shown that the implementation of the Laplace approximation is greatly simplified by the use of a numerical technique known as automatic differentiation (AD). Several algorithms are proposed and compared with some existing maximum likelihood methods using both simulated data and actual data. It is found that the new methods match the statistical efficiency of the existing methods while significantly reducing the coding effort. Also proposed are simple methods for obtaining the filtered, smoothed and predictive values for the latent variable. …
Nonlinearity Induced Weak Instrumentation, Ioannis Kasparis, Peter C. B. Phillips, Tassos Magdalinos
Nonlinearity Induced Weak Instrumentation, Ioannis Kasparis, Peter C. B. Phillips, Tassos Magdalinos
Research Collection School Of Economics
In regressions involving integrable functions we examine the limit properties of instrumental variable (IV) estimators that utilise integrable transformations of lagged regressors as instruments. The regressors can be either I(0) or nearly integrated (NI) processes. We show that this kind of nonlinearity in the regression function can significantly affect the relevance of the instruments. In particular, such instruments become weak when the signal of the regressor is strong, as it is in the NI case. Instruments based on integrable functions of lagged NI regressors display long range dependence and so remain relevant even at long lags, continuing to contribute to …
Assessing Market Failures In Export Pioneering Activities: A Structural Estimation Approach, Shang-Jin Wei, Ziru Wei, Jianhuan Xu
Assessing Market Failures In Export Pioneering Activities: A Structural Estimation Approach, Shang-Jin Wei, Ziru Wei, Jianhuan Xu
Research Collection School Of Economics
The paper provides a first structural-estimation-based assessment of an influential hypothesis that export pioneers are too few relative to social optimum due to knowledge spillover in new market explorations. Such market failure requires two inequalities to hold simultaneously: the discovery cost is greater than any individual firm’s expected profit but Smaller than the sum of all potential exporters’ expected profits. Neither has to hold in the data. We estimate the structural parameters based on the customs data of Chinese electronics exports. While we find positive discover cost and spillovers, "missing pioneers" are nonetheless a low probability event.
Bayesian Analysis Of Bubbles In Asset Prices, Andras Fulop, Jun Yu
Bayesian Analysis Of Bubbles In Asset Prices, Andras Fulop, Jun Yu
Research Collection School Of Economics
We develop a new asset price model where the dynamic structure of the asset price, after the fundamental value is removed, is subject to two different regimes. One regime reflects the normal period where the asset price divided by the dividend is assumed to follow a mean-reverting process around a stochastic long run mean. This latter is allowed to account for possible smooth structural change. The second regime reflects the bubble period with explosive behavior. Stochastic switches between two regimes and non-constant probabilities of exit from the bubble regime are both allowed. A Bayesian learning approach is employed to jointly …
Specification Sensitivity In Right‐Tailed Unit Root Testing For Explosive Behaviour, Peter C. B. Phillips, Shuping Shi, Jun Yu
Specification Sensitivity In Right‐Tailed Unit Root Testing For Explosive Behaviour, Peter C. B. Phillips, Shuping Shi, Jun Yu
Research Collection School Of Economics
This article aims to provide some empirical guidelines for the practical implementation of right-tailed unit root tests, focusing on the recursive right-tailed ADF test of Phillips et al. (2011b). We analyze and compare the limit theory of the recursive test under different hypotheses and model specifications. The size and power properties of the test under various scenarios are examined and some recommendations for empirical practice are given. Some new results on the consistent estimation of localizing drift exponents are obtained, which are useful in assessing model specification. Empirical applications to stock markets illustrate these specification issues and reveal their practical …
Deviance Information Criterion For Comparing Var Models, Tao Zeng, Yong Li, Jun Yu
Deviance Information Criterion For Comparing Var Models, Tao Zeng, Yong Li, Jun Yu
Research Collection School Of Economics
Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li et al. (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.
A Bayesian Chi-Squared Test For Hypothesis Testing, Yong Li, Xiao-Bin Liu, Jun Yu
A Bayesian Chi-Squared Test For Hypothesis Testing, Yong Li, Xiao-Bin Liu, Jun Yu
Research Collection School Of Economics
A new Bayesian test statistic is proposed to test a point null hypothesis based on 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 appeal in practical applications. First, it is well-defined under improper prior distributions. Second, it avoids Jeffrey-Lindley’s paradox. Third, it is relatively easy to compute, even for models with latent variables. Finally, it is pivotal and its threshold value can be easily obtained from the asymptotic chi-squared distribution. The method is illustrated using some real examples in economics and finance.
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
Vector Autoregression (VAR) has been a standard empirical tool used in macroeconomics and finance. In this paper we discuss how to compare alternative VAR models after they are estimated by Bayesian MCMC methods. In particular we apply a robust version of deviance information criterion (RDIC) recently developed in Li et al. (2014b) to determine the best candidate model. RDIC is a better information criterion than the widely used deviance information criterion (DIC) when latent variables are involved in candidate models. Empirical analysis using US data shows that the optimal model selected by RDIC can be different from that by DIC.
Self-Exciting Jumps, Learning, And Asset Pricing Implications, Andras Fulop, Junye Li, Jun Yu
Self-Exciting Jumps, Learning, And Asset Pricing Implications, Andras Fulop, Junye Li, Jun Yu
Research Collection School Of Economics
The paper proposes a self-exciting asset pricing model that takes into account cojumps between prices and volatility and self-exciting jump clustering. We employ a dence of self-exciting jump clustering since the 1987 market crash, and its importance Bayesian learning approach to implement real time sequential analysis. We find evidence of self-exciting jump clustering since the 1987 market crash, and its importance becomes more obvious at the onset of the 2008 global financial crisis. It is found that learning affects the tail behaviors of the return distributions and has important implications for risk management, volatility forecasting and option pricing.
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 location-scale model subject to endogenous selection, in the absence of an instrument or a large support regressor. Identification relies on the independence between the covariates and selection, for arbitrarily large values of the outcome. In this context, we propose a simple estimator, which combines extremal quantile regressions with minimum distance. We establish the asymptotic normality of this estimator 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 …
On Confidence Intervals For Autoregressive Roots And Predictive Regression, Peter C. B. Phillips
On Confidence Intervals For Autoregressive Roots And Predictive Regression, Peter C. B. Phillips
Research Collection School Of Economics
Local to unity limit theory is used in applications to construct confidence intervals (CIs) for autoregressive roots through inversion of a unit root test (Stock (1991)). Such CIs are asymptotically valid when the true model has an autoregressive root that is local to unity (rho = 1 + c/n), but are shown here to be invalid at the limits of the domain of definition of the localizing coefficient c because of a failure in tightness and the escape of probability mass. Failure at the boundary implies that these CIs have zero asymptotic coverage probability in the stationary case and vicinities …
Maximum Likelihood Estimation Of Partially Observed Diffusion Models, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Maximum Likelihood Estimation Of Partially Observed Diffusion Models, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Research Collection School Of Economics
This paper develops a maximum likelihood (ML) method to estimate partially observed diffusion models based on data sampled at discrete times. The method combines two techniques recently proposed in the literature in two separate steps. In the first step, the closed form approach of Aït-Sahalia (2008) is used to obtain a highly accurate approximation to the joint transition probability density of the latent and the observed states. In the second step, the efficient importance sampling technique of Richard and Zhang (2007) is used to integrate out the latent states, thereby yielding the likelihood function. Using both simulated and real data, …
X-Differencing And Dynamic Panel Model Estimation, Chirok Han, Peter C. B. Phillips, Donggyu Sul
X-Differencing And Dynamic Panel Model Estimation, Chirok Han, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
This paper introduces a new estimation method for dynamic panel models with fixed effects and AR(p) idiosyncratic errors. The proposed estimator uses a novel form of systematic differencing, called X-differencing, that eliminates fixed effects and retains information and signal strength in cases where there is a root at or near unity. The resulting "panel fully aggregated" estimator (PFAE) is obtained by pooled least squares on the system of X-differenced equations. The method is simple to implement, consistent for all parameter values, including unit root cases, and has strong asymptotic and finite sample performance characteristics that dominate other procedures, such as …
Specification Testing For Transformation Models, Arthur Lewbel, Xun Lu, Liangjun Su
Specification Testing For Transformation Models, Arthur Lewbel, Xun Lu, Liangjun Su
Research Collection School Of Economics
Consider a nonseparable model Y=R(X,U) where Y and X are observed, while U is unobserved and conditionally independent of X. This paper provides the first nonparametric test of whether R takes the form of a transformation model, meaning that Y is monotonic in the sum of a function of X plus a function of U. Transformation models of this form are commonly assumed in economics, including, e.g., standard specifications of duration models and hedonic pricing models. Our test statistic is asymptotically normal under local alternatives and consistent against nonparametric alternatives. Monte Carlo experiments show that our test performs well in …
A New Approach To Bayesian Hypothesis Testing, Yong Li, Tao Zeng, Jun Yu
A New Approach To Bayesian Hypothesis Testing, Yong Li, Tao Zeng, Jun Yu
Research Collection School Of Economics
In this paper a new Bayesian approach is proposed to test a point null hypothesis based on the deviance in a decision-theoretical framework. The proposed test statistic may be regarded as the Bayesian version of the likelihood ratio test and appeals in practical applications with three desirable properties. First, it is immune to Jeffreys’ concern about the use of improper priors. Second, it avoids Jeffreys–Lindley’s paradox, Third, it is easy to compute and its threshold value is easily derived, facilitating the implementation in practice. The method is illustrated using some real examples in economics and finance. It is found that …
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Nonparametric Testing For Anomaly Effects In Empirical Asset Pricing Models, Sainan Jin, Liangjun Su, Yonghui Zhang
Research Collection School Of Economics
In this paper we propose a class of nonparametric tests for anomaly effects in empirical asset pricing models in the framework of nonparametric panel data models with interactive fixed effects. Our approach has two prominent features: one is the adoption of nonparametric component to capture the anomaly effects of some asset-specific characteristics, and the other is the flexible treatment of both observed/constructed and unobserved common factors. By estimating the unknown factors, betas, and nonparametric function simultaneously, our setup is robust to misspecification of functional form and common factors and avoids the well-known “error-in-variable” (EIV) problem associated with the commonly used …
Optimal Estimation Of Cointegrated Systems With Irrelevant Instruments, Peter C. B. Phillips
Optimal Estimation Of Cointegrated Systems With Irrelevant Instruments, Peter C. B. Phillips
Research Collection School Of Economics
It has been known since Phillips and Hansen (1990) that cointegrated systems can be consistently estimated using stochastic trend instruments that are independent of the system variables. A similar phenomenon occurs with deterministically trending instruments. The present work shows that such "irrelevant" deterministic trend instruments may be systematically used to produce asymptotically efficient estimates of a cointegrated system. The approach is convenient in practice, involves only linear instrumental variables estimation, and is a straightforward one step procedure with no loss of degrees of freedom in estimation. Simulations reveal that the procedure works well in practice both in terms of point …
Predictive Regression Under Various Degrees Of Persistence And Robust Long-Horizon Regression, Peter C. B. Phillips, Ji Hyung Lee
Predictive Regression Under Various Degrees Of Persistence And Robust Long-Horizon Regression, Peter C. B. Phillips, Ji Hyung Lee
Research Collection School Of Economics
The paper proposes a novel inference procedure for long-horizon predictive regression with persistent regressors, allowing the autoregressive roots to lie in a wide vicinity of unity. The invalidity of conventional tests when regressors are persistent has led to a large literature dealing with inference in predictive regressions with local to unity regressors. Magdalinos and Phillips (2009b) recently developed a new framework of extended IV procedures (IVX) that enables robtist chi-square testing for a wider class of persistent regressors. We extend this robust procedure to an even wider parameter space in the vicinity of unity and apply the methods to long-horizon …
Testing Homogeneity In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Qihui Chen
Testing Homogeneity In Panel Data Models With Interactive Fixed Effects, Liangjun Su, Qihui Chen
Research Collection School Of Economics
This paper proposes a residual-based LM test for slope homogeneity in large dimensional panel data models with interactive fixed effects. We first run the panel regression under the null to obtain the restricted residuals, and then use them to construct our LM test statistic. We show that after being appropriately centered and scaled, our test statistic is asymptotically normally distributed under the null and a sequence of Pitman local alternatives. The asymptotic distributional theories are established under fairly general conditions which allow for both lagged dependent variables and conditional heteroskedasticity of unknown form by relying on the concept of conditional …
Identifying Latent Structures In Panel Data, Liangjun Su, Zhentao Shi, Peter C. B. Phillips
Identifying Latent Structures In Panel Data, Liangjun Su, Zhentao Shi, Peter C. B. Phillips
Research Collection School Of Economics
This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized regression techniques. We focus on linear models where the slope parameters are heterogeneous across groups but homogenous within a group and the group membership is unknown. Two approaches are considered -- penalized least squares (PLS) for models without endogenous regressors, and penalized GMM (PGMM) for models with endogeneity. In both cases we develop a new variant of Lasso called classifier-Lasso (C-Lasso) that serves to shrink individual coefficients to the unknown group-specific coefficients. C-Lasso achieves simultaneous classification and consistent estimation in a single …
Quasi-Maximum Likelihood Estimation For Spatial Panel Data Regressions, Zhenlin Yang
Quasi-Maximum Likelihood Estimation For Spatial Panel Data Regressions, Zhenlin Yang
Research Collection School Of Economics
This article considers quasi-maximum likelihood estimations (QMLE) for two spatial panel data regression models: mixed effects model with spatial errors and transformed mixed effects model (where response and covariates are transformed) with spatial errors. One aim of transformation is to normalize the data, thus the transformed models are more robust with respect to the normality assumption compared with the standard ones. QMLE method provides additional protection against violation of normality assumption. Asymptotic properties of the QMLEs are investigated. Numerical illustrations are provided.
Estimation Of Time-Varying Adjusted Probability Of Informed Trading And Probability Of Symmetric Order-Flow Shock, Daniel Preve, Yiu Kuen Tse
Estimation Of Time-Varying Adjusted Probability Of Informed Trading And Probability Of Symmetric Order-Flow Shock, Daniel Preve, Yiu Kuen Tse
Research Collection School Of Economics
Recently Duarte and Young (2009) study the probability of informed trading (PIN) proposed by Easley et al. (2002) and decompose it into two parts: the adjusted PIN (APIN) as a measure of asymmetric information and the probability of symmetric order-flow shock (PSOS) as a measure of illiquidity. They provide some cross-section estimates of these measures using daily data over annual periods. In this paper we propose a method to estimate daily APIN and PSOS by extending the method in Tay et al. (2009) using high-frequency transaction data. Our empirical results show that while PIN is positively contemporaneously correlated with variance, …