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Articles 541 - 570 of 828
Full-Text Articles in Econometrics
Infinite Density At The Median And The Typical Shape Of Stock Return Distributions, Chirok Han, Jin Seo Cho, Peter C. B. Phillips
Infinite Density At The Median And The Typical Shape Of Stock Return Distributions, Chirok Han, Jin Seo Cho, Peter C. B. Phillips
Research Collection School Of Economics
Statistics are developed to test for the presence of an asymptotic discontinuity (or infinite density or peakedness) in a probability density at the median. The approach makes use of work by Knight (1998) on L(1) estimation asymptotics in conjunction with nonparametric kernel density estimation methods. The size and power of the tests are assessed, and conditions under which the tests have good performance are explored in simulations. The new methods are applied to stock returns of leading companies across major U.S. industry groups. The results confirm the presence of infinite density at the median as a new significant empirical evidence …
Corrigendum To "A Gaussian Approach For Continuous Time Models Of The Short Term Interest Rate", Peter C. B. Phillips, Jun Yu
Corrigendum To "A Gaussian Approach For Continuous Time Models Of The Short Term Interest Rate", Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
An error is corrected in Yu and Phillips (2001) (Econometrics Journal, 4, 210-224) where a time transformation was used to induce Gaussian disturbances in the discrete time equivalent model. It is shown that the error process in this model is not a martingale and the Dambis, Dubins-Schwarz (DDS) theorem is not directly applicable. However, a detrended error process is a martingale, the DDS theorem is applicable, and the corresponding stopping time correctly induces Gaussianity. We show that the two stopping time sequences differ by O(a2), where a is the pre-specified normalized timing constant.
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. An empirical application to the …
Nonparametric And Semiparametric Panel Econometric Models: Estimation And Testing, Liangjun Su, Aman Ullah
Nonparametric And Semiparametric Panel Econometric Models: Estimation And Testing, Liangjun Su, Aman Ullah
Research Collection School Of Economics
This paper gives a selective review on the recent developments of nonparametric and semiparametric panel data models. We focus on the conventional panel data models with one-way error component structure, partially linear panel data models, varying coefficient panel data models, nonparametric panel data models with multi-factor error structure, and nonseparable nonparametric panel data models. For each area, we discuss the basic models and ideas of estimation, and comment on the asymptotic properties of different estimators and specification tests. Much theoretical and empirical research is needed in this emerging area
Testing Heterogeneity In Panel Data Models With Interactive Fixed Effects, Qihui Chen
Testing Heterogeneity In Panel Data Models With Interactive Fixed Effects, Qihui Chen
Dissertations and Theses Collection (Open Access)
This paper proposes a test for the slope homogeneity in large dimensional panel data models with interactive fixed effects based on a measure of goodness-of-fit (R2). We first obtain, for each cross-sectional unit, the R2 from the time series regression of residuals on the constant and observable regressors and then construct the test statistic R2 as an equally weighted average of the cross-sectional R2's. R̄2 is close to 0 under the null hypothesis of homogenous slopes and deviates away from 0 otherwise. We show that after being appropriately centered and scaled, R2 …
Estimation And Forecasting Of Dynamic Conditional Covariance: A Semiparametric Multivariate Model, Xiangdong Long, Liangjun Su, Aman Ullah
Estimation And Forecasting Of Dynamic Conditional Covariance: A Semiparametric Multivariate Model, Xiangdong Long, Liangjun Su, Aman Ullah
Research Collection School Of Economics
We propose a semiparametric conditional covariance (SCC) estimator that combines the first-stage parametric conditional covariance (PCC) estimator with the second-stage nonparametric correction estimator in a multiplicative way. We prove the asymptotic normality of our SCC estimator, propose a nonparametric test for the correct specification of PCC models, and study its asymptotic properties. We evaluate the finite sample performance of our test and SCC estimator and compare the latter with that of PCC estimator, purely nonparametric estimator, and Hafner, Dijk, and Franses’s (2006) estimator in terms of mean squared error and Value-at-Risk losses via simulations and real data analyses.
Model Selection In Validation Sampling Data: An Asymptotic Likelihood-Based Lasso Approach, Chenlei Leng, Denis H. Y. Leung
Model Selection In Validation Sampling Data: An Asymptotic Likelihood-Based Lasso Approach, Chenlei Leng, Denis H. Y. Leung
Research Collection School Of Economics
We propose an asymptotic likelihood-based LASSO approach for model selection in regression analysis when data are subject to validation sampling. The method makes use of an initial estimator of the regression coefficients and their asymptotic covariance matrix to form an asymptotic likelihood. This ``working'' objective function facilitates the formulation of the LASSO and the implementation of a fast algorithm. Our method circumvents the need to use a likelihood set-up that requires full distributional assumptions about the data. We show that the resulting estimator is consistent in model selection and that the method has lower prediction errors than a model that …
Testing Structural Change In Partially Linear Models, Liangjun Su, Halbert White
Testing Structural Change In Partially Linear Models, Liangjun Su, Halbert White
Research Collection School Of Economics
We consider two tests of structural change for partially linear time-series models. The first tests for structural change in the parametric component, based on the cumulative sums of gradients from a single semiparametric regression. The second tests for structural change in the parametric and nonparametric components simultaneously, based on the cumulative sums of weighted residuals from the same semiparametric regression. We derive the limiting distributions of both tests under the null hypothesis of no structural change and for sequences of local alternatives. We show that the tests are generally not asymptotically pivotal under the null but may be free of …
Need Singapore Fear Floating? A Dsge-Var Approach, Hwee Kwan Chow, Paul D. Mcnelis
Need Singapore Fear Floating? A Dsge-Var Approach, Hwee Kwan Chow, Paul D. Mcnelis
Research Collection School Of Economics
This paper uses a DSGE-VAR model to examine the managed exchange-rate system at work in Singapore and asks if the country has any reason to fear floating the exchange rate with a Taylor rule inflation-targeting mechanism that uses the short term interest rate instead of the exchange rate as the benchmark monetary policy instrument. Our simulation results show that the use of a more flexible exchange rate system will reduce volatility in inflation and investment but consumption volatility will increase. Overall, there are neither signi cant welfare gains or losses in the regime shift. Given the highly open and trade …
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 Lee Kong Chian School Of Business
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 finite 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 devian information criterion …
Smoothing Local-To-Moderate Unit Root Theory, Peter C. B. Phillips, Tassos Magdalinos, Liudas Giraitis
Smoothing Local-To-Moderate Unit Root Theory, Peter C. B. Phillips, Tassos Magdalinos, Liudas Giraitis
Research Collection School Of Economics
A limit theory is established for autoregressive time series that smooths the transition between local and moderate deviations from unity and provides a transitional form that links conventional unit root distributions and the standard normal. Edgeworth expansions of the limit theory are given. These expansions show that the limit theory that holds for values of the autoregressive coefficient that are closer to stationarity than local (i.e. deviations of the form rho = 1 + c/n, where n is the sample size and c < 0) holds up to the second order. Similar expansions around the limiting Cauchy density are provided for the mildly explosive case. (C) 2010 Elsevier B.V. All rights reserved.
Estimating The Garch Diffusion: Simulated Maximum Likelihood In Continuous Time, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Estimating The Garch Diffusion: Simulated Maximum Likelihood In Continuous Time, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Research Collection School Of Economics
A new algorithm is developed to provide a simulated maximum likelihood estimation of the GARCH diffusion model of Nelson (1990) based on return data only. The method combines two accurate approximation procedures, namely, the polynomial expansion of Ait-Sahalia (2008) to approximate the transition probability density of return and volatility, and the Efficient Importance Sampler (EIS) of Richard and Zhang (2007) to integrate out the volatility. The first and second order terms in the polynomial expansion are used to generate a base-line importance density for an EIS algorithm. The higher order terms are included when evaluating the importance weights. Monte Carlo …
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 to suffer from several problems in the context of latent variable models. The first problem is computational. Another problem is that BFs are not well defined under the improper prior. In this paper, a new Bayesian method, based on 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 …
A Conversation With Eric Ghysels, Peter C. B. Phillips, Jun Yu
A Conversation With 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 …
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Research Collection School Of Economics
This paper overviews some recent advances on simulatio n-based methods of estimating time series models and asset pricing models that are widely used in finance. The simulation based methods have proven to be particularly useful when the likelihood function and moments do not have tractable forms and hence the maximum likelihood method (MLE) and the generalized method of moments (GMM) are difficult to use. They can also be useful for improving the finite sample performance of the traditional methods when financial time series are highly persistent and when the quantity of interest is a highly nonlinear function of system parameters.The …
Standardized Lm Tests For Spatial Error Dependence In Linear Or Panel Regressions, Badi H. Baltagi, Zhenlin Yang
Standardized Lm Tests For Spatial Error Dependence In Linear Or Panel Regressions, Badi H. Baltagi, Zhenlin Yang
Research Collection School Of Economics
The robustness of the LM tests for spatial error dependence of Burridge (1980) for the linear regression model and Anselin (1988) for the panel regression model are examined. While both tests are asymptotic ally robust against distributional misspecification, their finite sample behavior can be sensitive to the spatial layout. To overcome this shortcoming, standardized LM tests are suggested. Monte Carlo results show that the new tests possess good finite sample properties. An important observation made throughout this study is that the LM tests for spatial dependence need to be both mean- and variance-adjusted for good finite sample performance to be …
A Study Of Price Evolution In Online Toy Market, Zhenlin Yang, Lydia L Gan, Fang-Fang Tang
A Study Of Price Evolution In Online Toy Market, Zhenlin Yang, Lydia L Gan, Fang-Fang Tang
Research Collection School Of Economics
We study and contrast pricing and price evolution of online only (Dotcom) and online branch of multi-channel retailers (OBMCRs) based on two panel data sets collected from online toy markets. Panel data regression analyses reveal several interesting empirical results: over time, OBMCRs and Dotcoms charge similar prices on average but Dotcoms significantly increase their shipping costs that eventually drive the overall average price of Dotcoms higher than that of OBMCRs. Price dispersions of both types of retailers are persistent. The price dispersion of OBMCRs is higher than that of Dotcoms at the beginning and does not change much over time, …
Bias In Estimating Multivariate And Univariate Diffusions, Xiaohu Wang, Peter C. B. Phillips, Jun Yu
Bias In Estimating Multivariate And Univariate Diffusions, Xiaohu Wang, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
Multivariate continuous time models are now widely used in economics and finance. Empirical applications typically rely on some process of discretization so that the system may be estimated with discrete data. This paper introduces a framework for discretizing linear multivariate continuous time systems that includes the commonly used Euler and trapezoidal approximations as special cases and leads to a general class of estimators for the mean reversion matrix. Asymptotic distributions and bias formulae are obtained for estimates of the mean reversion parameter. Explicit expressions are given for the discretization bias and its relationship to estimation bias in both multivariate and …
Measurement And High Finance, Peter C. B. Phillips, Jun Yu, Eric Ghysels
Measurement And High Finance, Peter C. B. Phillips, Jun Yu, Eric Ghysels
Research Collection School Of Economics
Turbulence in the world of banking and finance over the last two years has riveted media attention on the financial industry, exposing practices, products and risks in the industry to widespread public scrutiny. Questions continue to be asked about the management and regulation of an industry whose performance is now seen to affect the world’s financial health and its prospects as much as it does national savings and individual retirement funds.
Asymptotic Distributions Of The Least Squares Estimator For Diffusion Processes, Qiankun Zhou, Jun Yu
Asymptotic Distributions Of The Least Squares Estimator For Diffusion Processes, Qiankun Zhou, Jun Yu
Research Collection School Of Economics
The asymptotic distributions of the least squares estimator of the mean reversion parameter (κ) are developed in a general class of diffusion models under three sampling schemes, namely, ongspan, in-fill and the combination of long-span and in-fill. The models have an affine structure in the drift function, but allow for nonlinearity in the diffusion function. The limiting distributions are quite different under the alternative sampling schemes. In particular, the in-fill limiting distribution is non-standard and depends on the initial condition and the time span whereas the other two are Gaussian. Moreover, while the other two distributions are discontinuous at κ …
A New Bayesian Unit Root Test In Stochastic Volatility Models, Yong Li, Jun Yu
A New Bayesian Unit Root Test In Stochastic Volatility Models, Yong Li, Jun Yu
Research Collection School Of Economics
A new posterior odds analysis is proposed to test for a unit root in volatility dynamics in the context of stochastic volatility models. This analysis extends the Bayesian unit root test of So and Li (1999, Journal of Business Economic Statistics) in two important ways. First, a numerically more stable algorithm is introduced to compute the Bayes factor, taking into account the special structure of the competing models. Owing to its numerical stability, the algorithm overcomes the problem of diverged “size” in the marginal likelihood approach. Second, to improve the “power” of the unit root test, a mixed prior specification …
Bias-Corrected Estimation For Spatial Autocorrelation, Zhenlin Yang
Bias-Corrected Estimation For Spatial Autocorrelation, Zhenlin Yang
Research Collection School Of Economics
The biasedness issue arising from the maximum likelihood estimation of the spatial autoregressive model (SAR) is further investigated under a broader set-up than that in Bao and Ullah (2007a). A major difficulty in analytically evaluating the expectations of ratios of quadratic forms is overcome by a simple bootstrap procedure. With that, the corrections on bias and variance of the spatial estimator can easily be made up to third-order, and once this is done, the estimators of other model parameters become nearly unbiased. Compared with the analytical approach, the new approach is much simpler, and can easily be extended to other …
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 previous termanext term modified previous termLM test of spatial error components,next term which is shown to be previous termrobustnext term against distributional misspecifications and previous termspatialnext term layouts. The proposed previous termtestnext term differs from the previous termLM testnext term of Anselin (2001) by previous termanext term term in the denominators of the previous termtestnext term statistics. This term disappears when either the previous termerrorsnext term are normal, or the variance of the diagonal elements of the product of previous termspatialnext term weights matrix and its transpose is zero or approaches to zero as sample size goes …
Bimodal T-Ratios: The Impact Of Thick Tails On Inference, Carlo V. Fioro, Vassilis A. Hajivassiliou, Peter C. B. Phillips
Bimodal T-Ratios: The Impact Of Thick Tails On Inference, Carlo V. Fioro, Vassilis A. Hajivassiliou, Peter C. B. Phillips
Research Collection School Of Economics
This paper studies the distribution of the classical t-ratio with data generated from distributions with no finite moments and shows how classical testing is affected by bimodality. A key condition in generating bimodality is independence of the observations in the underlying data-generating process (DGP). The paper highlights the strikingly different implications of lack of correlation versus statistical independence in DGPs with infinite moments and shows how standard inference can be invalidated in such cases, thereby pointing to the need for adapting estimation and inference procedures to the special problems induced by thick-tailed (TT) distributions. The paper presents theoretical results for …
Nonparametric Testing For Asymmetric Information, Liangjun Su, Martin Spindler
Nonparametric Testing For Asymmetric Information, Liangjun Su, Martin Spindler
Research Collection School Of Economics
Asymmetric information is an important phenomenon in many markets and in particular in insurance markets. Testing for asymmetric information has become a very important issue in the literature in the last two decades. Almost all testing procedures that are used in empirical studies are parametric, which may yield misleading conclusions in the case of misspecification of either functional or distributional relationships among the variables of interest. Motivated by the literature on testing conditional independence, we propose a new nonparametric test for asymmetric information which is applicable in a variety of situations. We demonstrate the test works reasonably well through Monte …
Profile Quasi-Maximum Likelihood Estimation Of Partially Linear Spatial Autoregressive Models, Liangjun Su, Sainan Jin
Profile Quasi-Maximum Likelihood Estimation Of Partially Linear Spatial Autoregressive Models, Liangjun Su, Sainan Jin
Research Collection School Of Economics
We propose profile quasi-maximum likelihood estimation of spatial autoregressive models that are partially linear. The rate of convergence of the spatial parameter estimator depends on some general features of the spatial weight matrix of the model. The estimators of other finite-dimensional parameters in the model have the regular √n-rate of convergence and the estimator of the nonparametric component is consistent but with different restrictions on the choice of bandwidth parameter associated with different natures of the spatial weights. Monte Carlo simulations verify our theory and indicate that our estimators perform reasonably well in finite samples.
Lad Asymptotics Under Conditional Heteroskedasticity With Possibly Infinite Error Densities, Jin Seo Cho, Chirok Han, Peter C. B. Phillips
Lad Asymptotics Under Conditional Heteroskedasticity With Possibly Infinite Error Densities, Jin Seo Cho, Chirok Han, Peter C. B. Phillips
Research Collection School Of Economics
Least absolute deviations (LAD) estimation of linear time series models is considered under conditional heteroskedasticity and serial correlation. The limit theory of the LAD estimator is obtained without assuming the finite density condition for the errors that is required in standard LAD asymptotics. The results are particularly useful in application of LAD estimation to financial time series data.
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Simulation-Based Estimation Methods For Financial Time Series Models, Jun Yu
Research Collection School Of Economics
This paper overviews some recent advances on simulation-based methods of estimating time series models and asset pricing models that are widely used in finance. The simulation based methods have proven to be particularly useful when the likelihood function and moments do not have tractable forms and hence the maximum likelihood method (MLE) and the generalized method of moments (GMM) are difficult to use. They can also be useful for improving the finite sample performance of the traditional methods when financial time series are highly persistent and when the quantity of interest is a highly nonlinear function of system parameters. The …
Semiparametric Estimator Of Time Series Conditional Variance, Santosh Mishra, Liangjun Su, Aman Ullah
Semiparametric Estimator Of Time Series Conditional Variance, Santosh Mishra, Liangjun Su, Aman Ullah
Research Collection School Of Economics
We propose a new combined semiparametric estimator, which incorporates the parametric and nonparametric estimators of the conditional variance in a multiplicative way. We derive the asymptotic bias, variance, and normality of the combined estimator under general conditions. We show that under correct parametric specification, our estimator can do as well as the parametric estimator in terms of convergence rates; whereas under parametric misspecification our estimator can still be consistent. It also improves over the nonparametric estimator of Ziegelmann (2002) in terms of bias reduction. The superiority of our estimator is verfied by Monte Carlo simulations and empirical data analysis.
Forecasting Realized Volatility Using A Nonnegative Semiparametric Time Series Model, A. Eriksson, D. Preve, Jun Yu
Forecasting Realized Volatility Using A Nonnegative Semiparametric Time Series Model, A. Eriksson, D. Preve, Jun Yu
Research Collection School Of Economics
This paper introduces a parsimonious and yet flexible nonnegative semiparametric model to forecast financial volatility. The new model extends the linear nonnegative autoregressive model of Barndorff-Nielsen & Shephard (2001) and Nielsen & Shephard (2003) by way of a power transformation. It is semiparametric in the sense that the distributional form of its error component is left unspecified. The statistical properties of the model are discussed and a novel estimation method is proposed. Asymptotic properties are established for the new estimation method. Simulation studies validate the new estimation method. The out-of-sample performance of the proposed model is evaluated against a number …