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Articles 451 - 480 of 771
Full-Text Articles in Econometrics
Variable Selection In Nonparametric And Semiparametric Regression Models, Liangjun Su, Yonghui Zhang
Variable Selection In Nonparametric And Semiparametric Regression Models, Liangjun Su, Yonghui Zhang
Research Collection School Of Economics
This chapter reviews the literature on variable selection in nonparametric and semiparametric regression models via shrinkage. We highlight recent developments on simultaneous variable selection and estimation through the methods of least absolute shrinkage and selection operator (Lasso), smoothly clipped absolute deviation (SCAD) or their variants, but restrict our attention to nonparametric and semiparametric regression models. In particular, we consider variable selection in additive models, partially linear models, functional/varying coefficient models, single index models, general nonparametric regression models, and semiparametric/nonparametric quantile regression models.
Non-Normality And Heteroscedasticity Robust Lm Tests Of Spatial Dependence, Badi H. Baltagi, Zhenlin Yang
Non-Normality And Heteroscedasticity Robust Lm Tests Of Spatial Dependence, Badi H. Baltagi, Zhenlin Yang
Research Collection School Of Economics
The standard LM tests for spatial dependence in linear and panel regressions are derived under the normality and homoskedasticity assumptions of the regression disturbances. Hence, they may not be robust against non-normality or heteroskedasticity of the disturbances. Following Born and Breitung (2011), we introduce general methods to modify the standard LM tests so that they become robust against heteroskedasticity and non-normality. The idea behind the robustification is to decompose the concentrated score function into a sum of uncorrelated terms so that the outer product of gradient (OPG) can be used to estimate its variance. We also provide methods for improving …
Optimal Design Of P-Value Consistent Step-Up Procedures For Multiple Comparisons With A Control In Direction-Mixed Families, Koon Shing Kwong, Siu Hung Cheung
Optimal Design Of P-Value Consistent Step-Up Procedures For Multiple Comparisons With A Control In Direction-Mixed Families, Koon Shing Kwong, Siu Hung Cheung
Research Collection School Of Economics
It is common in clinical studies for several treatments to be compared to a control. Most of the related statistical techniques have been developed to accommodate inferential families in which all hypotheses are either one- or two-sided such that the familywise error rate is controlled at a specified level. Several multiple testing procedures were recently proposed to perform multiple comparisons with a control in direction-mixed families that contain a mixture of one- and two-sided inferences. Of these procedures, the p-value consistent step-up procedure is found to be superior in terms of its power and p-value consistent property. In this paper, …
Singapore Inflation Expectations: Expecting The Unexpected, Aurobindo Ghosh, Jun Yu
Singapore Inflation Expectations: Expecting The Unexpected, Aurobindo Ghosh, Jun Yu
Research Collection School Of Economics
The study of inflation expectations of Singapore house-holds is a multi-disciplinary industry-relevant research that comes out of a partnership between Singapore Management University (SMU) and MasterCard. The research team for this MasterCard-SKBI Singapore Index of Inflation Expectations (SInDEx) project applied rigorous methods using current internet-based marketing survey tools for data-collection and advanced econometric techniques to analyse the data. The updates from the quarterly waves are keenly followed by policymakers, market watchers and the media because of the enormous importance of cost of living to individuals and businesses alike.
Estimation Of High-Frequency Volatility: An Autoregressive Conditional Duration Approach, Yiu Kuen Tse, Thomas Tao Yang
Estimation Of High-Frequency Volatility: An Autoregressive Conditional Duration Approach, Yiu Kuen Tse, Thomas Tao Yang
Research Collection School Of Economics
We propose a method to estimate the intraday volatility of a stock by integrating the instantaneous conditional return variance per unit time obtained from the autoregressive conditional duration (ACD) model, called the ACD-ICV method. We compare the daily volatility estimated using the ACD-ICV method against several versions of the realized volatility (RV) method, including the bipower variation RV with subsampling, the realized kernel estimate, and the duration-based RV. Our Monte Carlo results show that the ACD-ICV method has lower root mean-squared error than the RV methods in almost all cases considered. This article has online supplementary material.
Optimal Estimation Under Nonstandard Conditions, Werner Ploberger, Peter C. B. Phillips
Optimal Estimation Under Nonstandard Conditions, Werner Ploberger, Peter C. B. Phillips
Research Collection School Of Economics
We analyze optimality properties of maximum likelihood (ML) and other estimators when the problem does not necessarily fall within the locally asymptotically normal (LAN) class, therefore covering cases that are excluded from conventional LAN theory such as unit root nonstationary time series. The classical Hajek-Le Cam optimality theory is adapted to cover this situation. We show that the expectation of certain monotone "bowl-shaped" functions of the squared estimation error are minimized by the ML estimator in locally asymptotically quadratic situations, which often occur in nonstationary time series analysis when the LAN property fails. Moreover, we demonstrate a direct connection between …
Detecting Bubbles In Hong Kong Residential Property Market, Matthew S. Yiu, Jun Yu, Lu Jin
Detecting Bubbles In Hong Kong Residential Property Market, Matthew S. Yiu, Jun Yu, Lu Jin
Research Collection School Of Economics
This study uses a newly developed bubble detection method (Phillips, Shi and Yu, 2011) to identify real estate bubbles in the Hong Kong residential property market. Our empirical results reveal several positive bubbles in the Hong Kong residential property market, including one in 1995, a stronger one in 1997, another one in 2004, and a more recent one in 2008. In addition, the method identifies two negative bubbles in the data, one in 2000 and the other one in 2001. These empirical results continue to be valid for the mass segment and the luxury segment. However, the method finds a …
Mean And Autocovariance Function Estimation Near The Boundary Of Stationarity, Liudas Giraitis, Peter C. B. Phillips
Mean And Autocovariance Function Estimation Near The Boundary Of Stationarity, Liudas Giraitis, Peter C. B. Phillips
Research Collection School Of Economics
We analyze the applicability of standard normal asymptotic theory for linear process models near the boundary of stationarity. Limit results are given for estimation of the mean, autocovariance and autocorrelation functions within the broad region of stationarity that includes near boundary cases which vary with the sample size. The rate of consistency and the validity of the normal asymptotic approximation for the corresponding estimators is determined both by the sample size n and a parameter measuring the proximity of the model to the unit root boundary. (C) 2012 Elsevier B.V. All rights reserved.
Robust Deviance Information Criterion For Latent Variable Models, Yong Li, Tao Zeng, Jun Yu
Robust Deviance Information Criterion For Latent Variable Models, Yong Li, Tao Zeng, Jun Yu
Research Collection School Of Economics
It is shown in this paper that the data augmentation technique undermines the theoretical underpinnings of the deviance information criterion (DIC), a widely used information criterion for Bayesian model comparison, although it facilitates parameter estimation for latent variable models via Markov chain Monte Carlo (MCMC) simulation. Data augmentation makes the likelihood function non-regular and hence invalidates the standard asymptotic arguments. A new information criterion, robust DIC (RDIC), is proposed for Bayesian comparison of latent variable models. RDIC is shown to be a good approximation to DIC without data augmentation. While the later quantity is difficult to compute, the expectation { …
Recent Advances In Nonstationary Time Series: A Festschrift In Honor Of Peter C. B. Phillips, Robert S. Mariano, Zhijie Xiao, Jun Yu
Recent Advances In Nonstationary Time Series: A Festschrift In Honor Of Peter C. B. Phillips, Robert S. Mariano, Zhijie Xiao, Jun Yu
Research Collection School Of Economics
On July 14–15, 2008, the School of Economics and the Sim Kee Boon Institute for Financial Economics at Singapore Management University (SMU) co-hosted a conference honoring the contribution of Peter Phillips to econometrics and statistics, in celebration of his 60th birthday. In total, 51 papers were presented by his colleagues and former students, who deeply appreciate and respect Peter as a true scholar and a good friend. These papers mainly cover two areas of Peter’s current research interests—nonstationary time series analysis, and panel, nonlinear and nonparametric models. On the basis of this conference, we have taken the opportunity to edit …
Statistical Tests For Multiple Forecast Comparison, Roberto Mariano, Daniel P. A. Preve
Statistical Tests For Multiple Forecast Comparison, Roberto Mariano, Daniel P. A. Preve
Research Collection School Of Economics
We consider a multivariate version of the Diebold–Mariano test for equal predictive ability of three or more forecasting models. The Wald-type test, S, which has a null distribution that is asymptotically chi-squared, is shown to be generally invariant with respect to the ordering of the models being compared. Finite-sample corrections for the test are also developed. Monte Carlo simulations indicate that S has reasonable size properties in large samples but tends to be oversized in moderate samples. The finite-sample correction succeeds in correcting for size, but only partially. For the size-adjusted tests, power increases with sample size, as expected. It …
Sieve Estimation Of Panel Data Models With Cross Section Dependence, Liangjun Su, Sainan Jin
Sieve Estimation Of Panel Data Models With Cross Section Dependence, Liangjun Su, Sainan Jin
Research Collection School Of Economics
In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006) common correlated effect (CCE) estimator to our semiparametric framework. Asymptotic normal distributions for the proposed estimators are derived and asymptotic variance estimators are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.
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 finite discrete samples and in large in-fill 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 …
Nonlinear Cointegrating Regression Under Weak Identification, Xiaoxia Shi, Peter C. B. Phillips
Nonlinear Cointegrating Regression Under Weak Identification, Xiaoxia Shi, Peter C. B. Phillips
Research Collection School Of Economics
An asymptotic theory is developed for a weakly identified cointegrating regression model in which the regressor is a nonlinear transformation of an integrated process. Weak identification arises from the presence of a loading coefficient for the nonlinear function that may be close to zero. In that case, standard nonlinear cointegrating limit theory does not provide good approximations to the finite-sample distributions of nonlinear least squares estimators, resulting in potentially misleading inference. A new local limit theory is developed that approximates the finite-sample distributions of the estimators uniformly well irrespective of the strength of the identification. An important technical component of …
Dynamic Misspecification In Nonparametric Cointegrating Regression, Ioannis Kasparis, Peter C. B. Phillips
Dynamic Misspecification In Nonparametric Cointegrating Regression, Ioannis Kasparis, Peter C. B. Phillips
Research Collection School Of Economics
Linear cointegration is known to have the important property of invariance under temporal translation. The same property is shown not to apply for nonlinear cointegration. The limit properties of the Nadaraya-Watson (NW) estimator for cointegrating regression under misspecified lag structure are derived, showing the NW estimator to be inconsistent, in general, with a "pseudo-true function" limit that is a local average of the true regression function. In this respect nonlinear cointegrating regression differs importantly from conventional linear cointegration which is invariant to time translation. When centred on the pseudo-true function and appropriately scaled, the NW estimator still has a mixed …
Semiparametric Gmm Estimation Of Spatial Autoregressive Models, Liangjun Su
Semiparametric Gmm Estimation Of Spatial Autoregressive Models, Liangjun Su
Research Collection School Of Economics
We propose semiparametric GMM estimation of semiparametric spatial autoregressive (SAR) models under weak moment conditions. In comparison with the quasi-maximum-likelihood-based semiparametric estimator of Su and Jin (2010), we allow for both heteroscedasticity and spatial dependence in the error terms. We derive the limiting distributions of our estimators for both the parametric and nonparametric components in the model and demonstrate the estimator of the parametric component has the usual -asymptotics. When the error term also follows an SAR process, we propose an estimator for the parameter in the SAR error process and derive the joint asymptotic distribution for both spatial parameters. …
A Semiparametric Stochastic Volatility Model, Jun Yu
A Semiparametric Stochastic Volatility Model, Jun Yu
Research Collection School Of Economics
In this paper the correlation structure in the classical leverage stochastic volatility (SV) model is generalized based on a linear spline. In the new model the correlation between the return and volatility innovations is time varying and depends nonparametrically on the type of news arrived to the market. Theoretical properties of the proposed model are examined. The model estimation and comparison are conducted by Bayesian methods. The performance of the estimates are examined in simulations. The new model is fitted to daily and weekly US data and compared with the classical SV and GARCH models in terms of their in-sample …
On The Intraday Periodicity Duration Adjustment Of High-Frequency Data, Wu Zhengxiao
On The Intraday Periodicity Duration Adjustment Of High-Frequency Data, Wu Zhengxiao
Research Collection School Of Economics
In the last decade, intensive studies on modeling high frequency financial data at the transaction level have been conducted. In the analysis of high-frequency duration data, it is often the first step to remove the intraday periodicity. Currently the most popular adjustment procedure is the cubic spline procedure proposed by Engle and Russell (1998). In this article, we first carry out a simulation study and show that the performance of the cubic spline procedure is not entirely satisfactory. Then we define periodicity point processes rigorously and prove a time change theorem. A new intraday periodic adjustment procedure is then proposed …
Pricing For Goodwill: A Threshold Quantile Regression Approach, Heng Ju, Liangjun Su, Pai Xu
Pricing For Goodwill: A Threshold Quantile Regression Approach, Heng Ju, Liangjun Su, Pai Xu
Research Collection School Of Economics
In the absence of other effective trust systems, an agent's reputation status becomes a critical factor in online transactions. A higher reputation category may give sellers an advantage in competition on online trading platforms. It is also possible that such reputation benefits provide sufficient incentives for sellers to adjust their pricing behavior. We here propose a simple economic model in which an online seller maximizes the sum of the profit from current sales and the possible future gain from a targeted higher reputation level. We show that the model can predict a jump in optimal pricing behavior. We adopt a …
The Et Interview: A Conversation With Professor Eric Ghysels, Peter C. B. Phillips, Jun Yu
The Et Interview: A Conversation With Professor Eric Ghysels, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
Eric Ghysels is the Bernstein Distinguished Professor of Economics and Professor of Finance at University of North Carolina at Chapel Hill. In 2008, Eric Ghysels and Robert Engle (2003 Nobel co-Laureate in Economic Science with Clive Granger) founded the Society for Financial Econometrics (SoFiE), establishing a global network of academics and practitioners dedicated to the fast-growing field of financial econometrics. In June 2010, Eric visited the Centre for Financial Econometrics (CoFiE) and the Sim Kee Boon Institute (SKBI) of Financial Economics at Singapore Management University. During his visit we conversed with him about SoFiE and the growing toolroom of financial …
Testing For Common Trends In Semi-Parametric Panel Data Models With Fixed Effects, Yonghui Zhang, Liangjun Su, Peter C. B. Phillips
Testing For Common Trends In Semi-Parametric Panel Data Models With Fixed Effects, Yonghui Zhang, Liangjun Su, Peter C. B. Phillips
Research Collection School Of Economics
This paper proposes a non-parametric test for common trends in semi-parametric panel data models with fixed effects based on a measure of non-parametric goodness-of-fit (R2). We first estimate the model under the null hypothesis of common trends by the method of profile least squares, and obtain the augmented residual which consistently estimates the sum of the fixed effect and the disturbance under the null. Then we run a local linear regression of the augmented residuals on a time trend and calculate the non-parametric R2 for each cross-section unit. The proposed test statistic is obtained by averaging all cross-sectional non-parametric R2s, …
Bayesian Hypothesis Testing In Latent Variable Models, Yong Li, Jun Yu
Bayesian Hypothesis Testing In Latent Variable Models, Yong Li, Jun Yu
Research Collection School Of Economics
Hypothesis testing using Bayes factors (BFs) is known not to be well defined under the improper prior. In the context of latent variable models, an additional problem with BFs is that they are difficult to compute. In this paper, a new Bayesian method, based on the decision theory and the EM algorithm, is introduced to test a point hypothesis in latent variable models. The new statistic is a by-product of the Bayesian MCMC output and, hence, easy to compute. It is shown that the new statistic is appropriately defined under improper priors because the method employs a continuous loss function. …
Bayesian Learning Of Impacts Of Self-Exciting Jumps In Returns And Volatility, Andras Fulop, Junye Li, Jun Yu
Bayesian Learning Of Impacts Of Self-Exciting Jumps In Returns And Volatility, Andras Fulop, Junye Li, Jun Yu
Research Collection School Of Economics
The paper proposes a new class of continuous-time asset pricing models where negative jumps play a crucial role. Whenever there is a negative jump in asset returns, it is simultaneously passed on to diffusion variance and the jump intensity, generating self-exciting co-jumps of prices and volatility and jump clustering. To properly deal with parameter uncertainty and in-sample over-fitting, a Bayesian learning approach combined with an efficient particle filter is employed. It not only allows for comparison of both nested and non-nested models, but also generates all quantities necessary for sequential model analysis. Empirical investigation using S&P 500 index returns shows …
Conditional Independence Specification Testing For Dependent Processes With Local Polynomial Quantile Regression, Liangjun Su, Halbert L. White
Conditional Independence Specification Testing For Dependent Processes With Local Polynomial Quantile Regression, Liangjun Su, Halbert L. White
Research Collection School Of Economics
We provide straightforward new nonparametric methods for testing conditional independence using local polynomial quantile regression, allowing weakly dependent data. Inspired by Hausman's (1978) specification testing ideas, our methods essentially compare two collections of estimators that converge to the same limits under correct specification (conditional independence) and that diverge under the alternative. To establish the properties of our estimators, we generalize the existing nonparametric quantile literature not only by allowing for dependent heterogeneous data but also by establishing a weak consistency rate for the local Bahadur representation that is uniform in both the conditioning variables and the quantile index. We also …
Folklore Theorems, Implicit Maps, And Indirect Inference, Peter C. B. Phillips
Folklore Theorems, Implicit Maps, And Indirect Inference, Peter C. B. Phillips
Research Collection School Of Economics
The delta method and continuous mapping theorem are among the most extensively used tools in asymptotic derivations in econometrics. Extensions of these methods are provided for sequences of functions that are commonly encountered in applications and where the usual methods sometimes fail. Important examples of failure arise in the use of simulation-based estimation methods such as indirect inference. The paper explores the application of these methods to the indirect inference estimator (IIE) in first order autoregressive estimation. The IIE uses a binding function that is sample size dependent. Its limit theory relies on a sequence-based delta method in the stationary …
A Simple And Robust Method Of Inference For Spatial Lag Dependence, Zhenlin Yang, Yan Shen
A Simple And Robust Method Of Inference For Spatial Lag Dependence, Zhenlin Yang, Yan Shen
Research Collection School Of Economics
A simple and reliable method of inference for the spatial parameter in spatial autoregressive models is introduced, based on a statistic obtained by centering and rescaling the numerator of the concentrated Gaussian score function. The resulted tests and confidence intervals are robust against the distributional misspecifications and are insensitive to the spatial layouts and the error standard deviation. In contrast, the standard methods based on Gaussian score and information matrix may lead to inconsistent inference when errors are non normal, and can be quite sensitive to the spatial layouts and the error standard deviation even when errors are normally distributed. …
Score Tests For Inverse Gaussian Mixtures, A. F. Desmond, Zhenlin Yang
Score Tests For Inverse Gaussian Mixtures, A. F. Desmond, Zhenlin Yang
Research Collection School Of Economics
The mixed inverse Gaussian given by Whitmore (Scand. J. Statist., 13, 1986, 211–220) provides a convenient way for testing the goodness-of-fit of a pure inverse Gaussian distribution. The test is a one-sided score test with the null hypothesis being the pure inverse Gaussian (i.e. the mixing parameter is zero) and the alternative a mixture. We devise a simple score test and study its finite sample properties. Monte Carlo results show that it compares favourably with the smooth test of Ducharme (Test, 10, 2001, 271-290). In practical applications, when the pure inverse Gaussian distribution is rejected, one is interested in making …
Uniform Asymptotic Normality In Stationary And Unit Root Autoregression, Chirok Han, Peter C. B. Phillips, Donggyu Sul
Uniform Asymptotic Normality In Stationary And Unit Root Autoregression, Chirok Han, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
While differencing transformations can eliminate nonstationarity, they typically reduce signal strength and correspondingly reduce rates of convergence in unit root autoregressions. The present paper shows that aggregating moment conditions that are formulated in differences provides an orderly mechanism for preserving information and signal strength in autoregressions with some very desirable properties. In first order autoregression, a partially aggregated estimator based on moment conditions in differences is shown to have a limiting normal distribution that holds uniformly in the autoregressive coefficient rho, including stationary and unit root cases. The rate of convergence is root n when vertical bar rho vertical bar < 1 and the limit distribution is the same as the Gaussian maximum likelihood estimator (MLE), but when rho = 1 the rate of convergence to the normal distribution is within a slowly varying factor of n. A fully aggregated estimator (FAE) is shown to have the same limit behavior in the stationary case and to have nonstandard limit distributions in unit root and near integrated cases, which reduce both the bias and the variance of the MLE. This result shows that it is possible to improve on the asymptotic behavior of the MLE without using an artificial shrinkage technique or otherwise accelerating convergence at unity at the cost of performance in the neighborhood of unity. Confidence intervals constructed from the FAE using local asymptotic theory around unity also lead to improvements over the MLE.
Power Maximization And Size Control Of Heteroscedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Power Maximization And Size Control Of Heteroscedasticity 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 (ρ). We show that the nonstandard fixed-ρ limit distributions of the t-statistic provide more accurate approximations to the finite sample distributions than the conventional large-ρ 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-ρ asymptotics. As a further contribution, we propose a new practical procedure for selecting the test-optimal power parameter that addresses the central …
Singapore Consumer’S Inflation Expectations And Creation Of Singapore Index Of Inflation Expectations, Aurobindo Ghosh, Jun Yu
Singapore Consumer’S Inflation Expectations And Creation Of Singapore Index Of Inflation Expectations, Aurobindo Ghosh, Jun Yu
Research Collection School Of Economics
The aim of this report is to highlight a broad spectrum of issues that brings about the measurement of the disagreement and the uncertainity and the formation of inflation expectations among economic agents in Singapore.