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Articles 1 - 30 of 38
Full-Text Articles in Economics
On Bias In The Estimation Of Structural Break Points, Liang Jiang, Xiaohu Wang, Jun Yu
On Bias In The Estimation Of Structural Break Points, Liang Jiang, Xiaohu Wang, Jun Yu
Research Collection School Of Economics
Based on the Girsanov theorem, this paper obtains the exact Önite sample distribution of the maximum likelihood estimator of structural break points in a continuous time model. The exact Önite sample theory suggests that, in empirically realistic situations, there is a strong Önite sample bias in the estimator of structural break points. This property is shared by least squares estimator of both the absolute structural break point and the fractional structural break point in discrete time models. A simulation-based method based on the indirect estimation approach is proposed to reduce the bias both in continuous time and discrete time models. …
Structural Change Estimation In Time Series Regressions With Endogenous Variables, Junhui Qian, Liangjun Su
Structural Change Estimation In Time Series Regressions With Endogenous Variables, Junhui Qian, Liangjun Su
Research Collection School Of Economics
We propose to apply the group fused Lasso to estimate time series models with endogenous regressors and an unknown number of breaks. It can correctly determine the number of breaks and estimate the break dates asymptotically. Simulations and applications are given.
Testing The Martingale Hypothesis, Peter C. B. Phillips, Sainan Jin
Testing The Martingale Hypothesis, Peter C. B. Phillips, Sainan Jin
Research Collection School Of Economics
We propose new tests of the martingale hypothesis based on generalized versions of the Kolmogorov–Smirnov and Cramér–von Mises tests. The tests are distribution-free and allow for a weak drift in the null model. The methods do not require either smoothing parameters or bootstrap resampling for their implementation and so are well suited to practical work. The article develops limit theory for the tests under the null and shows that the tests are consistent against a wide class of nonlinear, nonmartingale processes. Simulations show that the tests have good finite sample properties in comparison with other tests particularly under conditional heteroscedasticity …
Norming Rates And Limit Theory For Some Time-Varying Coefficient Autoregressions, Offer Lieberman, Peter C. B. Phillips
Norming Rates And Limit Theory For Some Time-Varying Coefficient Autoregressions, Offer Lieberman, Peter C. B. Phillips
Research Collection School Of Economics
A time-varying autoregression is considered with a similarity-based coefficient and possible drift. It is shown that the random-walk model has a natural interpretation as the leading term in a small-sigma expansion of a similarity model with an exponential similarity function as its AR coefficient. Consistency of the quasi-maximum likelihood estimator of the parameters in this model is established, the behaviours of the score and Hessian functions are analysed and test statistics are suggested. A complete list is provided of the normalization rates required for the consistency proof and for the score and Hessian function standardization. A large family of unit …
A New Hedonic Regression For Real Estate Prices Applied To The Singapore Residential Market, Jiang Liang, Peter C. B. Phillips, Jun Yu
A New Hedonic Regression For Real Estate Prices Applied To The Singapore Residential Market, Jiang Liang, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
This paper develops a new hedonic method for constructing a real estate price index that utilizes all transaction price information that encompasses both single-sale and repeat-sale properties. The new method is less prone to specification errors than standard hedonic methods and uses all available data. Like the Case-Shiller repeat-sales method, the new method has the advantage of being computationally efficient. In an empirical analysis of the methodology, we fit the model to all transaction prices for private residential property holdings in Singapore between Q1 1995 and Q2 2014, covering several periods of major price fluctuation and changes in government macro …
Bound Estimator Of Hiv Prevalence: Application To Malawi, Tomoki Fujii, Denis H. Y. Leung
Bound Estimator Of Hiv Prevalence: Application To Malawi, Tomoki Fujii, Denis H. Y. Leung
Research Collection School Of Economics
To find lower and upper bounds of HIV prevalence in Malawi under mild and intuitive assumptions to assess the importance of the refusal issue in the estimation of HIV prevalence. Methods: We derive bounds based on the following two key assumptions: (i) Among those who have never taken an HIV test before, those who refuse to take an HIV test (hereafter “refusers”) have at least as much risk to be HIV positive as those who participate in the HIV test, and (ii) among the refusers, those who have a prior testing experience are at least as likely to be HIV …
Additive Nonparametric Regression In The Presence Of Endogenous Regressors, Deniz Ozabaci, Daniel J. Henderson, Liangjun Su
Additive Nonparametric Regression In The Presence Of Endogenous Regressors, Deniz Ozabaci, Daniel J. Henderson, Liangjun Su
Research Collection School Of Economics
In this article we consider nonparametric estimation of a structural equation model under full additivity constraint. We propose estimators for both the conditional mean and gradient which are consistent, asymptotically normal, oracle efficient, and free from the curse of dimensionality. Monte Carlo simulations support the asymptotic developments. We employ a partially linear extension of our model to study the relationship between child care and cognitive outcomes. Some of our (average) results are consistent with the literature (e.g., negative returns to child care when mothers have higher levels of education). However, as our estimators allow for heterogeneity both across and within …
Intraday Periodicity Adjustments Of Transaction Duration And Their Effects On High-Frequency Volatility Estimation, Yiu Kuen Tse, Yingjie Dong
Intraday Periodicity Adjustments Of Transaction Duration And Their Effects On High-Frequency Volatility Estimation, Yiu Kuen Tse, Yingjie Dong
Research Collection School Of Economics
We study two methods of adjusting for intraday periodicity of high-frequency financial data: the well-known Duration Adjustment (DA) method and the recently proposed Time Transformation (TT) method (Wu (2012)). We examine the effects of these adjustments on the estimation of intraday volatility using the Autoregressive Conditional Duration-Integrated Conditional Variance (ACD-ICV) method of Tse and Yang (2012). We find that daily volatility estimates are not sensitive to intraday periodicity adjustment. However, intraday volatility is found to have a weaker U-shaped volatility smile and a biased trough if intraday periodicity adjustment is not applied. In addition, adjustment taking account of trades with …
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 application of this model. Contrary to the …
Initial-Condition Free Estimation Of Fixed Effects Dynamic Panel Data Models, Zhenlin Yang
Initial-Condition Free Estimation Of Fixed Effects Dynamic Panel Data Models, Zhenlin Yang
Research Collection School Of Economics
It is well known that (quasi) MLE of dynamic panel data (DPD) models with short panels depends on the assumptions on the initial values; ignoring them or a wrong treatment of them will result in inconsistency or serious bias. This paper introduces a initial-condition free method for estimating the fixed-effects DPD models, through as simple modification of the quasi-score. An outer-product-of-gradients (OPG) method is also proposed for robust inference. The MLE of Hsiao, Pesaran and Tahmiscioglu (2002, Journal of Econometrics), where the initial observations are modeled, is extended to quasi MLE and an OPG method is proposed for robust inference. …
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Testing Conditional Independence Via Empirical Likelihood, Liangjun Su, Halbert White
Research Collection School Of Economics
We construct two classes of smoothed empirical likelihood ratio tests for the conditional independence hypothesis by writing the null hypothesis as an infinite collection of conditional moment restrictions indexed by a nuisance parameter. One class is based on the CDF; another is based on smoother functions. We show that the test statistics are asymptotically normal under the null hypothesis and a sequence of Pitman local alternatives. We also show that the tests possess an asymptotic optimality property in terms of average power. Simulations suggest that the tests are well behaved in finite samples. Applications to some economic and financial time …
Modified Qml Estimation Of Spatial Autoregressive Models With Unknown Heteroskedasticity And Nonnormality, Shew Fan Liu, Zhenlin Yang
Modified Qml Estimation Of Spatial Autoregressive Models With Unknown Heteroskedasticity And Nonnormality, Shew Fan Liu, Zhenlin Yang
Research Collection School Of Economics
In the presence of heteroskedasticity, Lin and Lee (2010) show that the quasi maximum likelihood (QML) estimators of spatial autoregressive models (SAR) can be inconsistent as a ‘necessary’ condition for consistency can be violated, and thus propose robust GMM estimators for the model. In this paper, we first show that this condition may hold in many practical situations and when it does the regular QML estimators can be consistent.In cases where this condition is violated, we propose a modified QML estimation method robust against heteroskedasticity of unknown form. In both cases, asymptotic distributions of the estimators are derived, and methods …
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 …
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 …
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. …
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 …
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 …
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 …
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 …
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 …
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.
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 …
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.
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.
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.
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 …