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Articles 511 - 540 of 771
Full-Text Articles in Econometrics
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 …
Nonparametric Tests For Poolability In Panel Data Models With Cross Section Dependence, Sainan Jin, Liangjun Su
Nonparametric Tests For Poolability In Panel Data Models With Cross Section Dependence, Sainan Jin, Liangjun Su
Research Collection School Of Economics
In this paper we propose a nonparametric test for poolability in large dimensional semiparametric panel data models with cross-section dependence based on the sieve estimation technique. To construct the test statistic, we only need to estimate the model under the alternative. We establish the asymptotic normal distributions of our test statistic under the null hypothesis of poolability and a sequence of local alternatives, and prove the consistency of our test. We also suggest a bootstrap method as an alternative way to obtain the critical values and justify its validity. A small set of Monte Carlo simulations indicate the test performs …
Simulated Maximum Likelihood Estimation Of Continuous Time Stochastic Volatility Models, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Simulated Maximum Likelihood Estimation Of Continuous Time Stochastic Volatility Models, Tore Selland Kleppe, Jun Yu, Hans J. Skaug
Research Collection School Of Economics
In this chapter 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 does 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.
Testing Linearity In Cointegrating Relations With An Application To Purchasing Power Parity, Seong Hyun Hong, Peter C. B. Phillips
Testing Linearity In Cointegrating Relations With An Application To Purchasing Power Parity, Seong Hyun Hong, Peter C. B. Phillips
Research Collection School Of Economics
This article shows that when applied to nonstationary time series, the conventional Regression Error Specification Test (RESET) leads to severe size distortion and its asymptotic distribution involves a mixture of noncentral chi(2) distributions. Nonstationarity introduces bias terms in the limit distribution, and appropriate corrections for the bias are presented leading to a modified RESET test that has a central chi(2) limit distribution. In simulations, this modified test is shown to have power not only against nonlinear cointegration but also against the absence of cointegration. In an empirical illustration, the linear purchasing power parity (PPP) specification is tested using five Organization …
Functional Coefficient Estimation With Both Categorical And Continuous Data, Liangjun Su, Ye Chen, Aman Ullah
Functional Coefficient Estimation With Both Categorical And Continuous Data, Liangjun Su, Ye Chen, Aman Ullah
Research Collection School Of Economics
We propose a local linear functional coefficient estimator that admits a mix of discrete and continuous data for stationary time series. Under weak conditions our estimator is asymptotically normally distributed. A small set of simulation studies is carried out to illustrate the finite sample performance of our estimator. As an application, we estimate a wage determination function that explicitly allows the return to education to depend on other variables. We find evidence of the complex interacting patterns among the regressors in the wage equation, such as increasing returns to education when experience is very low, high return to education for …
Estimation Of High-Frequency Volatility: An Autoregressive Conditional Duration Models Approach, Yiu Kuen Tse, Tao Yang
Estimation Of High-Frequency Volatility: An Autoregressive Conditional Duration Models Approach, Yiu Kuen Tse, 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) models. We compare the daily volatilities estimated using the ACD models against several versions of the realized volatility (RV) method, including the bipower variation realized volatility with subsampling, the realized kernel estimate and the duration-based realized volatility. The ACD volatility estimates correlate highly with and perform very well against the RV estimates. Our Monte Carlo results show that our method has lower root mean-squared error than the RV methods in most …
Numerical Analysis Of Non-Constant Pure Rate Of Time Preference: A Model Of Climate Policy, Tomoki Fujii, Larry Karp
Numerical Analysis Of Non-Constant Pure Rate Of Time Preference: A Model Of Climate Policy, Tomoki Fujii, Larry Karp
Research Collection School Of Economics
When current decisions affect welfare in the far-distant future, as with climate change, the use of a declining pure rate of time preference (PRTP) provides potentially important modeling flexibility. The difficulty of analyzing models with non-constant PRTP limits their application. We describe and provide software (available online) to implement an algorithm to numerically obtain a Markov perfect equilibrium for an optimal control problem with non-constant PRTP. We apply this software to a simplified version of the numerical climate change model used in the Stern Review. For our calibration, the policy recommendations are less sensitive to the PRTP than widely believed.
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 …
Forecasting Realized Volatility Using A Nonnegative Semiparametric Model, Daniel P. A. Preve, Anders Eriksson, Jun Yu
Forecasting Realized Volatility Using A Nonnegative Semiparametric Model, Daniel P. A. Preve, Anders 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 …
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 …
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. …
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 …
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 …
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.
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 …
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 …
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 …