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Articles 1 - 30 of 32
Full-Text Articles in Social and Behavioral Sciences
Empirical Likelihood In Missing Data Problems, Jing Qin, Biao Zhang, Denis H. Y. Leung
Empirical Likelihood In Missing Data Problems, Jing Qin, Biao Zhang, Denis H. Y. Leung
Research Collection School Of Economics
Missing data is a ubiquitous problem in medical and social sciences. It is well known that inferences based only on the complete data may not only lose efficiency, but may also lead to biased results if the data is not missing completely at random (MCAR). The inverse-probability weighting method proposed by Horvitz and Thompson (1952) is a popular alternative when the data is not MCAR. The Horvitz–Thompson method, however, is sensitive to the inverse weights and may suffer from loss of efficiency. In this paper, we propose a unified empirical likelihood approach to missing data problems and explore the use …
Dating The Timeline Of Financial Bubbles During The Subprime Crisis, Peter C. B. Phillips, Jun Yu
Dating The Timeline Of Financial Bubbles During The Subprime Crisis, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
A new recursive regression methodology is introduced to analyze the bubble characteristics of various financial time series during the subprime crisis. The methods modify a technique proposed in Phillips, Wu, and Yu (2011) and provide a technology for identifying bubble behavior with consistent dating of their origination and collapse. The tests serve as an early warning diagnostic of bubble activity and a new procedure is introduced for testing bubble migration across markets. Three relevant financial series are investigated, including a financial asset price (a house price index), a commodity price (the crude oil price), and one bond price (the spread …
Forecasting Realized Volatility Using A Nonnegative Semiparametric Model, D. Preve, A. Eriksson, Jun Yu
Forecasting Realized Volatility Using A Nonnegative Semiparametric Model, D. Preve, A. Eriksson, Jun Yu
Research Collection School Of Economics
This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen and Shephard (2001) and Nielsen and Shephard (2003) by way of a Box-Cox transformation. It is semiparametric in the sense that the dependency structure and the distributional form of its error component are left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Its out-of-sample performance is evaluated against a number of standard methods, using data on S&P 500 monthly realized volatilities. The competing models include the exponential …
Econometric Analysis Of Continuous Time Models: A Survey Of Peter Philip’S Work And Some New Results, Jun Yu
Econometric Analysis Of Continuous Time Models: A Survey Of Peter Philip’S 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 nearly 40 years, resulting in many important publications by him. In these publications he has dealt with a wide range of continuous time models and econometric problems, from univariate equations to systems of equations, from asymptotic theory to nite sample issues, from parametric models to nonparametric models, from identication 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 the problems that have been …
Bias In The Estimation Of The Mean Reversion Parameter In Continuous Time Models, Jun Yu
Bias In The Estimation Of The Mean Reversion Parameter In Continuous Time Models, Jun Yu
Research Collection School Of Economics
It is well known that for continuous time models with a linear drift standard estimation methods yield biased estimators for the mean reversion parameter both in nite discrete samples and in large in-…ll samples. In this paper, we obtain two expressions to approximate the bias of the least squares/maximum likelihood estimator of the mean reversion parameter in the Ornstein-Uhlenbeck process with a known long run mean when discretely sampled data are available. The first expression mimics the bias formula of Marriott and Pope (1954) for the discrete time model. Simulations show that this expression does not work satisfactorily when the …
Economic Transition And Growth, Peter C. B. Phillips, Donggyu Sul
Economic Transition And Growth, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
Some extensions of neoclassical growth models are discussed that allow for cross-section heterogeneity among economies and evolution in rates of technological progress over time. The models offer a spectrum of transitional behavior among economies that includes convergence to a common steady-state path as well as various forms of transitional divergence and convergence. Mechanisms for modeling such transitions, measuring them econometrically, assessing group behavior and selecting subgroups are developed in the paper. Some econometric issues with the commonly used augmented Solow regressions are pointed out, including problems of endogeneity and omitted variable bias which arise under conditions of transitional heterogeneity. Alternative …
Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu
Bayesian Analysis Of Structural Credit Risk Models With Microstructure Noises, Shirley J. Huang, Jun Yu
Research Collection School Of Economics
In this paper a Markov chain Monte Carlo (MCMC) technique is developed for the Bayesian analysis of structural credit risk models with microstructure noises. The technique is based on the general Bayesian approach with posterior computations performed by Gibbs sampling. Simulations from the Markov chain, whose stationary distribution converges to the posterior distribution, enable exact ¯nite sample inferences of model parameters. The exact inferences can easily be extended to latent state variables and any nonlinear transformation of state variables and parameters, facilitating practical credit risk applications. In addition, the comparison of alternative models can be based on deviance information criterion …
Explosive Behavior In The 1990s Nasdaq: When Did Exuberance Escalate Asset Values?, Peter C. B. Phillips, Yangru Wu, Jun Yu
Explosive Behavior In The 1990s Nasdaq: When Did Exuberance Escalate Asset Values?, Peter C. B. Phillips, Yangru Wu, Jun Yu
Research Collection School Of Economics
A recursive test procedure is suggested that provides a mechanism for testing explosive behavior, date-stamping the origination and collapse of economic exuberance, and providing valid confidence intervals for explosive growth rates. The method involves the recursive implementation of a right-side unit root test and a sup test, both of which are easy to use in practical applications, and some new limit theory for mildly explosive processes. The test procedure is shown to have discriminatory power in detecting periodically collapsing bubbles, thereby overcoming a weakness in earlier applications of unit root tests for economic bubbles. Some asymptotic properties of the Evans …
Stimulated Maximum Likelihood Estimation Of Continuous Time Stochastic Volatility Models, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Stimulated Maximum Likelihood Estimation Of Continuous Time Stochastic Volatility Models, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Research Collection School Of Economics
In this paper we develop and implement a method for maximum simulated likelihood estimation of the continuous time stochastic volatility model with the constant elasticity of volatility. The approach do not require observations on option prices nor volatility. To integrate out latent volatility from the joint density of return and volatility, a modified efficient importance sampling technique is used after the continuous time model is approximated using the Euler-Maruyama scheme. The Monte Carlo studies show that the method works well and the empirical applications illustrate usefulness of the method. Empirical results provide strong evidence against the Heston model.
Automated Likelihood Based Inference For Stochastic Volatility Models, H. Skaug, Jun Yu
Automated Likelihood Based Inference For Stochastic Volatility Models, H. 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. …
A Semi-Parametric Two-Stage Projection Type Estimator Of Multivalued Treatment Effects, Aurobindo Ghosh
A Semi-Parametric Two-Stage Projection Type Estimator Of Multivalued Treatment Effects, Aurobindo Ghosh
Research Collection School Of Economics
One of the most well documented regularities in evaluation literature like returns to schooling(or funding for programs) is that several factors come together to confound the measurement of its effect. First, in observational studies the true return is often individual specific, and so it is almost impossible to use a traditional treatment effect models with randomly assigned treatment and control groups. This endogeneity in the model further exacerbates our inability to conduct such trials. Second, the problem is not a classical treatment effect measurement problem where we have discrete or more often binary treatments. Hence, techniques like measuring the Local …
A Nonparametric Goodness-Of-Fit-Based Test For Conditional Heteroskedasticity, Liangjun Su, Aman Ullah
A Nonparametric Goodness-Of-Fit-Based Test For Conditional Heteroskedasticity, Liangjun Su, Aman Ullah
Research Collection School Of Economics
In this paper we propose a nonparametric test for conditional heteroskedasticity based on a new measure of nonparametric goodness-of-fit (R2). In analogy with the ANOVA tools for classical linear regression models, the nonparametric R2 is obtained for the local polynomial regression of the residuals from a parametric regression on some covariates. It is close to 0 under the null hypothesis of conditional homoskedasticity and stays away from 0 otherwise. Unlike most popular parametric tests in the literature, the new test does not require the correct specification of parametric conditional heteroskedasticity form and thus is able to detect all kinds of …
Econometric Forecasting And High-Frequency Data Analysis, Yiu Kuen Tse, Yiu Kuen Tse
Econometric Forecasting And High-Frequency Data Analysis, Yiu Kuen Tse, Yiu Kuen Tse
Research Collection School of Economics
This important book consists of surveys of high-frequency financial data analysis and econometric forecasting, written by pioneers in these areas including Nobel laureate Lawrence Klein. Some of the chapters were presented as tutorials to an audience in the Econometric Forecasting and High-Frequency Data Analysis Workshop at the Institute for Mathematical Science, National University of Singapore in May 2006. They will be of interest to researchers working in macroeconometrics as well as financial econometrics. Moreover, readers will find these chapters useful as a guide to the literature as well as suggestions for future research.
Testing Structural Change In Conditional Distributions Via Quantile Regressions, Liangjun Su, Zhijie Xiao
Testing Structural Change In Conditional Distributions Via Quantile Regressions, Liangjun Su, Zhijie Xiao
Research Collection School Of Economics
We propose tests for structural change in conditional distributions via quantile regressions. To avoid misspecification on the conditioning relationship, we construct the tests based on the residuals from local polynomial quantile regressions. In particular, the tests are based upon the cumulative sums of generalized residuals from quantile regressions and have power against local alternatives at rate n−1/2. We derive the limiting distributions for our tests under the null hypothesis of no structural change and a sequence of local alternatives. The proposed tests apply to a wide range of dynamic models, including time series regressions with m.d.s. errors, as well as …
Simulation-Based Estimation Of Contingent-Claims Prices, Peter C. B. Phillips, Jun Yu
Simulation-Based Estimation Of Contingent-Claims Prices, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
A new methodology is proposed to estimate theoretical prices of financial contingent claims whose values are dependent on some other underlying financial assets. In the literature, the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is not necessarily the best method in finite samples. This paper proposes a simulation-based method. When it is used in connection with ML, it can improve the finite-sample performance of the ML estimator while maintaining its good asymptotic properties. The method is implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond and …
Power Maximization And Size Control In Heteroskedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Power Maximization And Size Control In Heteroskedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Research Collection School Of Economics
Using the power kernels of Phillips, Sun and Jin (2006, 2007), we examine the large sample asymptotic properties of the t-test for different choices of power parameter (rho). We show that the nonstandard fixed-rho limit distributions of the t-statistic provide more accurate approximations to the finite sample distributions than the conventional large-rho limit distribution. We prove that the second-order corrected critical value based on an asymptotic expansion of the nonstandard limit distribution is also second-order correct under the large-rho asymptotics. As a further contribution, we propose a new practical procedure for selecting the test-optimal power parameter that addresses the central …
Efficient Parameter Estimation In Longitudinal Data Analysis Using A Hybrid Gee Method, Denis H. Y. Leung, You Gan Wang, Min Zhu
Efficient Parameter Estimation In Longitudinal Data Analysis Using A Hybrid Gee Method, Denis H. Y. Leung, You Gan Wang, Min Zhu
Research Collection School Of Economics
The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. …
Discrete Choice Modeling With Nonstationary Panels Applied To Exchange Rate Regime Choice, Sainan Jin
Discrete Choice Modeling With Nonstationary Panels Applied To Exchange Rate Regime Choice, Sainan Jin
Research Collection School Of Economics
This paper develops a regression limit theory for discrete choice nonstationary panels with large cross section (N) and time series (T) dimensions. Some results emerging from this theory are directly applicable in the wider context of M-estimation. This includes an extension of work by Wooldridge [Wooldridge, J.M., 1994. Estimation and Inference for Dependent Processes. In: Engle, R.F., McFadden, D.L. (Eds.). Handbook of Econometrics, vol. 4, North-Holland, Amsterdam] on the limit theory of local extremum estimators to multi-indexed processes in nonlinear nonstationary panel data models. It is shown that the maximum likelihood (ML) estimator is consistent without an incidental parameters problem …
Econometric Theory And Practice, Peter C. B. Phillips
Econometric Theory And Practice, Peter C. B. Phillips
Research Collection School Of Economics
Econometrics has been evolving as a discipline over the last decade in a way that has successfully brought theory and practice much closer together. Many of the developments are associated with laptop computing, the increasing availability of electronic databases, and the convenience of modern econometric software and matrix programming languages. The changes that have occurred affect us at every level as teachers, researchers, practitioners, readers, reviewers, and authors. No journal can stand still in the face of such changes. This editorial speaks to these changes and the way they impact our subject, our authors, and our readership.
Econometric Theory And Practice, Peter C. B. Phillips
Econometric Theory And Practice, Peter C. B. Phillips
Research Collection School Of Economics
Econometrics has been evolving as a discipline over the last decade in a way that has successfully brought theory and practice much closer together. Many of the developments are associated with laptop computing, the increasing availability of electronic databases, and the convenience of modern econometric software and matrix programming languages. The changes that have occurred affect us at every level as teachers, researchers, practitioners, readers, reviewers, and authors. No journal can stand still in the face of such changes. This editorial speaks to these changes and the way they impact our subject, our authors, and our readership.
A Two-Stage Realized Volatility Approach To Estimation Of Diffusion Processes With Discrete Data, Peter C. B. Phillips, Jun Yu
A Two-Stage Realized Volatility Approach To Estimation Of Diffusion Processes With Discrete Data, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
This paper motivates and introduces a two-stage method of estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as developed in [Jacod, J., 1994. Limit of random measures associated with the increments of a Brownian semiartingal. Working paper, Laboratoire de Probabilities, Universite Pierre et Marie Curie, Paris] and [Barndorff-Nielsen, O., Shephard, N., 2002. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society. Series B, 64, 253-280], to provide a regression model for estimating the parameters …
Nonparametric Structural Estimation Via Continuous Location Shifts In An Endogenous Regressor, Peter C. B. Phillips, Liangjun Su
Nonparametric Structural Estimation Via Continuous Location Shifts In An Endogenous Regressor, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
Recent work by Wang and Phillips (2009b, c) has shown that ill posed inverse problems do not arise in nonstationary nonparametric regression and there is no need for nonparametric instrumental variable estimation. Instead, simple Nadaraya Watson nonparametric estimation of a (possibly nonlinear) cointegrating regression equation is consistent with a limiting (mixed) normal distribution irrespective of the endogeneity in the regressor, near integration as well as integration in the regressor, and serial dependence in the regression equation. The present paper shows that some closely related results apply in the case of structural nonparametric regression with independent data when there are continuous …
Using High-Frequency Transaction Data To Estimate The Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Using High-Frequency Transaction Data To Estimate The Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Research Collection School Of Economics
This paper applies the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot (2003) to estimate the probability of informed trading (PIN) using irregularly spaced transaction data. We model trade direction (buy versus sell orders) and the duration between trades jointly. Unlike the Easley, Hvidkjaer, and O'Hara (2002) approach, which uses the aggregate numbers of daily buy and sell orders to estimate PIN, our methodology allows for interactions between consecutive buy-sell orders and accounts for the duration between trades and the volume of trade. We extend the Easley–Hvidkjaer–O'Hara framework by allowing the probabilities of good news and bad news …
A Paradox Of Inconsistent Parametric And Consistent Nonparametric Regression, Peter C. B. Phillips, Liangjun Su
A Paradox Of Inconsistent Parametric And Consistent Nonparametric Regression, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
This paper explores a paradox discovered in recent work by Phillips and Su (2009). That paper gave an example in which nonparametric regression is consistent whereas parametric regression is inconsistent even when the true regression functional form is known and used in regression. This appears to be a paradox, as knowing the true functional form should not in general be detrimental in regression. In the present case, local regression methods turn out to have a distinct advantage because of endogeneity in the regressor. The paradox arises because additional correct information is not necessarily advantageous when information is incomplete. In the …
Limit Theory For Dating The Origination And Collapse Of Mildly Explosive Periods In Time Series Data, Jun Yu, Peter C. B. Phillips
Limit Theory For Dating The Origination And Collapse Of Mildly Explosive Periods In Time Series Data, Jun Yu, Peter C. B. Phillips
Research Collection School Of Economics
Some limit theory is developed for estimators suggested in Phillips, Wu and Yu (2009) for dating bubble pheonoma in time series data. The models involve mildly explosive autoregressions and the tests rely on right sided recursive unit root tests. The estimates locate the origination and collapse dates of bubbles involving mildly explosive episodes set within longer periods where the data evolve as a stochastic trend. The dating estimators are shown to be consistent under mild regularity conditions on the process. Some simulation evidence on the performance of the estimators is reported. The proposed method works well in finite samples and …
Limit Theory For Cointegrated Systems With Moderately Integrated And Moderately Explosive Regressors, Tassos Magdalinos, Peter C. B. Phillips
Limit Theory For Cointegrated Systems With Moderately Integrated And Moderately Explosive Regressors, Tassos Magdalinos, Peter C. B. Phillips
Research Collection School Of Economics
An asymptotic theory is developed for multivariate regression in cointegrated systems whose variables are moderately integrated or moderately explosive in the sense that they have autoregressive roots of the form rho(ni) = 1 + c(i)/n(alpha), involving moderate deviations from unity when alpha is an element of (0, 1) and c(i) is an element of R are constant parameters. When the data are moderately integrated in the stationary direction (with c(i) < 0), it is shown that least squares regression is consistent and asymptotically normal but suffers from significant bias, related to simultaneous equations bias. In the moderately explosive case (where c(i) > 0) the limit theory is mixed normal with Cauchy-type tail behavior, and the rate of convergence is explosive, as in the case of a moderately explosive scalar autoregression (Phillips and …
Testing Conditional Uncorrelatedness, Liangjun Su, Aman Ullah
Testing Conditional Uncorrelatedness, Liangjun Su, Aman Ullah
Research Collection School Of Economics
We propose a nonparametric test for conditional uncorrelatedness in multiple-equation models such as seemingly unrelated regressions (SURs), multivariate volatility models, and vector autoregressions (VARs). Under the null hypothesis of conditional uncorrelatedness, the test statistic converges to the standard normal distribution asymptotically. We also study the local power property of the test. Simulation shows that the test behaves quite well in finite samples.
Semiparametric Cointegrating Rank Selection, Xu Cheng, Peter C. B. Phillips
Semiparametric Cointegrating Rank Selection, Xu Cheng, Peter C. B. Phillips
Research Collection School Of Economics
Some convenient limit properties of usual information criteria are given for cointegrating rank selection. Allowing for a non-parametric short memory component and using a reduced rank regression with only a single lag, standard information criteria are shown to be weakly consistent in the choice of cointegrating rank provided the penalty coefficient C(n) -> infinity and C(n)/n -> 0 as n -> 8. The limit distribution of the AIC criterion, which is inconsistent, is also obtained. The analysis provides a general limit theory for semiparametric reduced rank regression under weakly dependent errors. The method does not require the specification of a …
A Centered Index Of Spatial Concentration: Axiomatic Approach With An Application To Population And Capital Cities, Filipe R. Campante, Quoc-Anh Do
A Centered Index Of Spatial Concentration: Axiomatic Approach With An Application To Population And Capital Cities, Filipe R. Campante, Quoc-Anh Do
Research Collection School Of Economics
We construct an axiomatic index of spatial concentration around a center or capital point of interest, a concept with wide applicability from urban economics, economic geography and trade, to political economy and industrial organization. We propose basic axioms (decomposability and monotonicity) and refinement axioms (order preservation, convexity, and local monotonicity) for how the index should respond to changes in the underlying distribution. We obtain a unique class of functions satisfying all these properties, defined over any n-dimensional Euclidian space: the sum of a decreasing, isoelastic function of individual distances to the capital point of interest, with specific boundaries for the …
A Robust Lm Test For Spatial Error Components, Zhenlin Yang
A Robust Lm Test For Spatial Error Components, Zhenlin Yang
Research Collection School Of Economics
This paper presents a modified LM test of spatial error components, which is shown to be robust against distributional misspecifications and spatial layouts. The proposed test differs from the LM test of Anselin (2001) by a term in the denominators of the test statistics. This term disappears when either the errors are normal, or the variance of the diagonal elements of the product of spatial weights matrix and its transpose is zero or approaches to zero as sample size goes large. When neither is true, as is often the case in practice, the effect of this term can be significant …