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Articles 541 - 570 of 771
Full-Text Articles in Econometrics
A Semi-Parametric Two-Stage Projection Type Estimator Of Multivalued Treatment Effects, Aurobindo Ghosh
A Semi-Parametric Two-Stage Projection Type Estimator Of Multivalued Treatment Effects, Aurobindo Ghosh
Research Collection School Of Economics
One of the most well documented regularities in evaluation literature like returns to schooling(or funding for programs) is that several factors come together to confound the measurement of its effect. First, in observational studies the true return is often individual specific, and so it is almost impossible to use a traditional treatment effect models with randomly assigned treatment and control groups. This endogeneity in the model further exacerbates our inability to conduct such trials. Second, the problem is not a classical treatment effect measurement problem where we have discrete or more often binary treatments. Hence, techniques like measuring the Local …
Testing Structural Change In Conditional Distributions Via Quantile Regressions, Liangjun Su, Zhijie Xiao
Testing Structural Change In Conditional Distributions Via Quantile Regressions, Liangjun Su, Zhijie Xiao
Research Collection School Of Economics
We propose tests for structural change in conditional distributions via quantile regressions. To avoid misspecification on the conditioning relationship, we construct the tests based on the residuals from local polynomial quantile regressions. In particular, the tests are based upon the cumulative sums of generalized residuals from quantile regressions and have power against local alternatives at rate n−1/2. We derive the limiting distributions for our tests under the null hypothesis of no structural change and a sequence of local alternatives. The proposed tests apply to a wide range of dynamic models, including time series regressions with m.d.s. errors, as well as …
Simulation-Based Estimation Of Contingent-Claims Prices, Peter C. B. Phillips, Jun Yu
Simulation-Based Estimation Of Contingent-Claims Prices, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
A new methodology is proposed to estimate theoretical prices of financial contingent claims whose values are dependent on some other underlying financial assets. In the literature, the preferred choice of estimator is usually maximum likelihood (ML). ML has strong asymptotic justification but is not necessarily the best method in finite samples. This paper proposes a simulation-based method. When it is used in connection with ML, it can improve the finite-sample performance of the ML estimator while maintaining its good asymptotic properties. The method is implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond and …
Power Maximization And Size Control In Heteroskedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Power Maximization And Size Control In Heteroskedasticity And Autocorrelation Robust Tests With Exponentiated Kernels, Yixiao Sun, Peter C. B. Phillips, Sainan Jin
Research Collection School Of Economics
Using the power kernels of Phillips, Sun and Jin (2006, 2007), we examine the large sample asymptotic properties of the t-test for different choices of power parameter (rho). We show that the nonstandard fixed-rho limit distributions of the t-statistic provide more accurate approximations to the finite sample distributions than the conventional large-rho limit distribution. We prove that the second-order corrected critical value based on an asymptotic expansion of the nonstandard limit distribution is also second-order correct under the large-rho asymptotics. As a further contribution, we propose a new practical procedure for selecting the test-optimal power parameter that addresses the central …
Efficient Parameter Estimation In Longitudinal Data Analysis Using A Hybrid Gee Method, Denis H. Y. Leung, You Gan Wang, Min Zhu
Efficient Parameter Estimation In Longitudinal Data Analysis Using A Hybrid Gee Method, Denis H. Y. Leung, You Gan Wang, Min Zhu
Research Collection School Of Economics
The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. …
Econometric Theory And Practice, Peter C. B. Phillips
Econometric Theory And Practice, Peter C. B. Phillips
Research Collection School Of Economics
Econometrics has been evolving as a discipline over the last decade in a way that has successfully brought theory and practice much closer together. Many of the developments are associated with laptop computing, the increasing availability of electronic databases, and the convenience of modern econometric software and matrix programming languages. The changes that have occurred affect us at every level as teachers, researchers, practitioners, readers, reviewers, and authors. No journal can stand still in the face of such changes. This editorial speaks to these changes and the way they impact our subject, our authors, and our readership.
Econometric Theory And Practice, Peter C. B. Phillips
Econometric Theory And Practice, Peter C. B. Phillips
Research Collection School Of Economics
Econometrics has been evolving as a discipline over the last decade in a way that has successfully brought theory and practice much closer together. Many of the developments are associated with laptop computing, the increasing availability of electronic databases, and the convenience of modern econometric software and matrix programming languages. The changes that have occurred affect us at every level as teachers, researchers, practitioners, readers, reviewers, and authors. No journal can stand still in the face of such changes. This editorial speaks to these changes and the way they impact our subject, our authors, and our readership.
Discrete Choice Modeling With Nonstationary Panels Applied To Exchange Rate Regime Choice, Sainan Jin
Discrete Choice Modeling With Nonstationary Panels Applied To Exchange Rate Regime Choice, Sainan Jin
Research Collection School Of Economics
This paper develops a regression limit theory for discrete choice nonstationary panels with large cross section (N) and time series (T) dimensions. Some results emerging from this theory are directly applicable in the wider context of M-estimation. This includes an extension of work by Wooldridge [Wooldridge, J.M., 1994. Estimation and Inference for Dependent Processes. In: Engle, R.F., McFadden, D.L. (Eds.). Handbook of Econometrics, vol. 4, North-Holland, Amsterdam] on the limit theory of local extremum estimators to multi-indexed processes in nonlinear nonstationary panel data models. It is shown that the maximum likelihood (ML) estimator is consistent without an incidental parameters problem …
A Two-Stage Realized Volatility Approach To Estimation Of Diffusion Processes With Discrete Data, Peter C. B. Phillips, Jun Yu
A Two-Stage Realized Volatility Approach To Estimation Of Diffusion Processes With Discrete Data, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
This paper motivates and introduces a two-stage method of estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as developed in [Jacod, J., 1994. Limit of random measures associated with the increments of a Brownian semiartingal. Working paper, Laboratoire de Probabilities, Universite Pierre et Marie Curie, Paris] and [Barndorff-Nielsen, O., Shephard, N., 2002. Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society. Series B, 64, 253-280], to provide a regression model for estimating the parameters …
Nonparametric Structural Estimation Via Continuous Location Shifts In An Endogenous Regressor, Peter C. B. Phillips, Liangjun Su
Nonparametric Structural Estimation Via Continuous Location Shifts In An Endogenous Regressor, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
Recent work by Wang and Phillips (2009b, c) has shown that ill posed inverse problems do not arise in nonstationary nonparametric regression and there is no need for nonparametric instrumental variable estimation. Instead, simple Nadaraya Watson nonparametric estimation of a (possibly nonlinear) cointegrating regression equation is consistent with a limiting (mixed) normal distribution irrespective of the endogeneity in the regressor, near integration as well as integration in the regressor, and serial dependence in the regression equation. The present paper shows that some closely related results apply in the case of structural nonparametric regression with independent data when there are continuous …
A Paradox Of Inconsistent Parametric And Consistent Nonparametric Regression, Peter C. B. Phillips, Liangjun Su
A Paradox Of Inconsistent Parametric And Consistent Nonparametric Regression, Peter C. B. Phillips, Liangjun Su
Research Collection School Of Economics
This paper explores a paradox discovered in recent work by Phillips and Su (2009). That paper gave an example in which nonparametric regression is consistent whereas parametric regression is inconsistent even when the true regression functional form is known and used in regression. This appears to be a paradox, as knowing the true functional form should not in general be detrimental in regression. In the present case, local regression methods turn out to have a distinct advantage because of endogeneity in the regressor. The paradox arises because additional correct information is not necessarily advantageous when information is incomplete. In the …
Using High-Frequency Transaction Data To Estimate The Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Using High-Frequency Transaction Data To Estimate The Probability Of Informed Trading, Anthony S. Tay, Christopher Ting, Yiu Kuen Tse, Mitchell Warachka
Research Collection School Of Economics
This paper applies the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot (2003) to estimate the probability of informed trading (PIN) using irregularly spaced transaction data. We model trade direction (buy versus sell orders) and the duration between trades jointly. Unlike the Easley, Hvidkjaer, and O'Hara (2002) approach, which uses the aggregate numbers of daily buy and sell orders to estimate PIN, our methodology allows for interactions between consecutive buy-sell orders and accounts for the duration between trades and the volume of trade. We extend the Easley–Hvidkjaer–O'Hara framework by allowing the probabilities of good news and bad news …
Limit Theory For Dating The Origination And Collapse Of Mildly Explosive Periods In Time Series Data, Jun Yu, Peter C. B. Phillips
Limit Theory For Dating The Origination And Collapse Of Mildly Explosive Periods In Time Series Data, Jun Yu, Peter C. B. Phillips
Research Collection School Of Economics
Some limit theory is developed for estimators suggested in Phillips, Wu and Yu (2009) for dating bubble pheonoma in time series data. The models involve mildly explosive autoregressions and the tests rely on right sided recursive unit root tests. The estimates locate the origination and collapse dates of bubbles involving mildly explosive episodes set within longer periods where the data evolve as a stochastic trend. The dating estimators are shown to be consistent under mild regularity conditions on the process. Some simulation evidence on the performance of the estimators is reported. The proposed method works well in finite samples and …
Limit Theory For Cointegrated Systems With Moderately Integrated And Moderately Explosive Regressors, Tassos Magdalinos, Peter C. B. Phillips
Limit Theory For Cointegrated Systems With Moderately Integrated And Moderately Explosive Regressors, Tassos Magdalinos, Peter C. B. Phillips
Research Collection School Of Economics
An asymptotic theory is developed for multivariate regression in cointegrated systems whose variables are moderately integrated or moderately explosive in the sense that they have autoregressive roots of the form rho(ni) = 1 + c(i)/n(alpha), involving moderate deviations from unity when alpha is an element of (0, 1) and c(i) is an element of R are constant parameters. When the data are moderately integrated in the stationary direction (with c(i) < 0), it is shown that least squares regression is consistent and asymptotically normal but suffers from significant bias, related to simultaneous equations bias. In the moderately explosive case (where c(i) > 0) the limit theory is mixed normal with Cauchy-type tail behavior, and the rate of convergence is explosive, as in the case of a moderately explosive scalar autoregression (Phillips and …
Testing Conditional Uncorrelatedness, Liangjun Su, Aman Ullah
Testing Conditional Uncorrelatedness, Liangjun Su, Aman Ullah
Research Collection School Of Economics
We propose a nonparametric test for conditional uncorrelatedness in multiple-equation models such as seemingly unrelated regressions (SURs), multivariate volatility models, and vector autoregressions (VARs). Under the null hypothesis of conditional uncorrelatedness, the test statistic converges to the standard normal distribution asymptotically. We also study the local power property of the test. Simulation shows that the test behaves quite well in finite samples.
Semiparametric Cointegrating Rank Selection, Xu Cheng, Peter C. B. Phillips
Semiparametric Cointegrating Rank Selection, Xu Cheng, Peter C. B. Phillips
Research Collection School Of Economics
Some convenient limit properties of usual information criteria are given for cointegrating rank selection. Allowing for a non-parametric short memory component and using a reduced rank regression with only a single lag, standard information criteria are shown to be weakly consistent in the choice of cointegrating rank provided the penalty coefficient C(n) -> infinity and C(n)/n -> 0 as n -> 8. The limit distribution of the AIC criterion, which is inconsistent, is also obtained. The analysis provides a general limit theory for semiparametric reduced rank regression under weakly dependent errors. The method does not require the specification of a …
Asymptotics And Bootstrap For Transformed Panel Data Regressions, Liangjun Su, Zhenlin Yang
Asymptotics And Bootstrap For Transformed Panel Data Regressions, Liangjun Su, Zhenlin Yang
Research Collection School Of Economics
This paper investigates the asymptotic properties of quasi-maximum likelihood estimators for transformed random effects models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoscedasticity, and simple model structure. We develop a quasi maximum likelihood-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the parameter estimates, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance matrix. Monte Carlo results reveal that these estimates perform well in finite samples, and that the gains by using bootstrap procedure for inference …
Semiparametric Prevalence Estimation From A Two-Phase Survey, Denis H. Y. Leung, J Qin
Semiparametric Prevalence Estimation From A Two-Phase Survey, Denis H. Y. Leung, J Qin
Research Collection School Of Economics
This paper studies a semi-parametric method for estimating the prevalence of a binary outcome using a two-phase survey. The motivation for a two-phase survey is, due to time, money and ethical considerations, it is impossible to carry out comprehensive evaluation on all subjects in a large random sample of the population. Rather, a relatively inexpensive "screening test" is given to all subjects in the random sample and only individuals more likely to have a positive outcome (cases) will be selected for a further "gold standard" test to verify the outcome. Therefore, individuals with verified outcome form a non-random sample from …
A Robust Lm Test For Spatial Error Components, Zhenlin Yang
A Robust Lm Test For Spatial Error Components, Zhenlin Yang
Research Collection School Of Economics
This paper presents a modified LM test of spatial error components, which is shown to be robust against distributional misspecifications and spatial layouts. The proposed test differs from the LM test of Anselin (2001) by a term in the denominators of the test statistics. This term disappears when either the errors are normal, or the variance of the diagonal elements of the product of spatial weights matrix and its transpose is zero or approaches to zero as sample size goes large. When neither is true, as is often the case in practice, the effect of this term can be significant …
A Centered Index Of Spatial Concentration: Axiomatic Approach With An Application To Population And Capital Cities, Filipe R. Campante, Quoc-Anh Do
A Centered Index Of Spatial Concentration: Axiomatic Approach With An Application To Population And Capital Cities, Filipe R. Campante, Quoc-Anh Do
Research Collection School Of Economics
We construct an axiomatic index of spatial concentration around a center or capital point of interest, a concept with wide applicability from urban economics, economic geography and trade, to political economy and industrial organization. We propose basic axioms (decomposability and monotonicity) and refinement axioms (order preservation, convexity, and local monotonicity) for how the index should respond to changes in the underlying distribution. We obtain a unique class of functions satisfying all these properties, defined over any n-dimensional Euclidian space: the sum of a decreasing, isoelastic function of individual distances to the capital point of interest, with specific boundaries for the …
Maximum Likelihood And Gaussian Estimation Of Continuous Time Models In Finance, Peter C. B. Phillips, Jun Yu
Maximum Likelihood And Gaussian Estimation Of Continuous Time Models In Finance, Peter C. B. Phillips, Jun Yu
Research Collection School Of Economics
This paper overviews maximum likelihood and Gaussian methods of estimating continuous time models used in finance. Since the exact likelihood can be constructed only in special cases, much attention has been devoted to the development of methods designed to approximate the likelihood. These approaches range from crude Euler-type approximations and higher order stochastic Taylor series expansions to more complex polynomial-based expansions and infill approximations to the likelihood based on a continuous time data record. The methods are discussed, their properties are outlined and their relative finite sample performance compared in a simulation experiment with the nonlinear CIR diffusion model, which …
Future Fiscal And Budgetary Shocks, Hian Teck Hoon, Edmund S. Phelps
Future Fiscal And Budgetary Shocks, Hian Teck Hoon, Edmund S. Phelps
Research Collection School Of Economics
We study the effects of future tax and budgetary shocks in a non-monetary and possibly non-Ricardian economy. An (unanticipated) temporary labor tax cut to be effective on a given future date—a delayed “debt bomb”—causes at once a drop in the (unit) value placed on the firms' business asset, the customer, with the result that share prices, the hourly wage, and employment drop in tandem. This paradox of reduced activity through announcement of future “stimulus” does not hinge on an upward jump of long interest rates. A future tax-rate cut lacking a “sunset” provision has the same negative effects.
Testing For Parameter Stability In Quantile Regression Models, Liangjun Su, Zhijie Xiao
Testing For Parameter Stability In Quantile Regression Models, Liangjun Su, Zhijie Xiao
Research Collection School Of Economics
We propose a test for structural change of conditional distribution in dynamic regression models. The test is constructed based on time series regression quantile estimates and complements conventional parameter instability tests in least-square type regression models. Asymptotic distribution for our test under the null hypothesis is derived.
Improving Semiparametric Estimation By Using Surrogate Data, Song Xi Chen, Leung, Denis H. Y., Jin Qin
Improving Semiparametric Estimation By Using Surrogate Data, Song Xi Chen, Leung, Denis H. Y., Jin Qin
Research Collection School Of Economics
The paper considers estimating a parameter beta that defines an estimating function U(y, x, beta) for an outcome variable y and its covariate x when the outcome is missing in some of the observations. We assume that, in addition to the outcome and the covariate, a surrogate outcome is available in every observation. The efficiency of existing estimators for beta depends critically on correctly specifying the conditional expectation of U given the surrogate and the covariate. When the conditional expectation is not correctly specified, which is the most likely scenario in practice, the efficiency of estimation can be severely compromised …
A Nonparametric Hellinger Metric Test For Conditional Independence, Liangjun Su, Halbert White
A Nonparametric Hellinger Metric Test For Conditional Independence, Liangjun Su, Halbert White
Research Collection School Of Economics
We propose a nonparametric test of conditional independence based on the weighted Hellinger distance between the two conditional densities, f(y|x,z) and f(y|x), which is identically zero under the null. We use the functional delta method to expand the test statistic around the population value and establish asymptotic normality under β-mixing conditions. We show that the test is consistent and has power against alternatives at distance n−1/2h−d/4. The cases for which not all random variables of interest are continuously valued or observable are also discussed. Monte Carlo simulation results indicate that the test behaves reasonably well in …
On The Evaluation Of The Joint Distribution Of Order Statistics, Koon Shing Kwong, Yiu Man Chan
On The Evaluation Of The Joint Distribution Of Order Statistics, Koon Shing Kwong, Yiu Man Chan
Research Collection School Of Economics
Dunnett and Tamhane [Dunnett, C.W., Tamhane, A.C., 1992. A step-up multiple test procedure. J. Amer. Statist. Assoc. 87, 162-170.] proposed a step-up procedure for comparing k treatments with a control and showed that the step-up procedure is more powerful than its counterpart single step and step-down procedures. Since then, several modified step-up procedures have been suggested to deal with different testing environments. In order to establish those step-up procedures, it is necessary to derive approaches for evaluating the joint distribution of the order statistics. In some cases, experimenters may have difficulty in applying those step-up procedures in multiple hypothesis testing …
Limit Theory For Explosively Cointegrated Systems, Peter C. B. Phillips, Tassos Magdalinos
Limit Theory For Explosively Cointegrated Systems, Peter C. B. Phillips, Tassos Magdalinos
Research Collection School Of Economics
A limit theory is developed for multivariate regression in an explosive cointegrated system. The asymptotic behavior of the least squares estimator of the cointegrating coefficients is found to depend upon the precise relationship between the explosive regressors. When the eigenvalues of the autoregressive matrix Θ are distinct, the centered least squares estimator has an exponential Θn rate of convergence and a mixed normal limit distribution. No central limit theory is applicable here, and Gaussian innovations are assumed. On the other hand, when some regressors exhibit common explosive behavior, a different mixed normal limiting distribution is derived with rate of convergence …
Regression Asymptotics Using Martingale Convergence Methods, Rustam Ibragimov, Peter C. B. Phillips
Regression Asymptotics Using Martingale Convergence Methods, Rustam Ibragimov, Peter C. B. Phillips
Research Collection School Of Economics
Weak convergence of partial sums and multilinear forms in independent random variables and linear processes and their nonlinear analogues to stochastic integrals now plays a major role in nonstationary time series and has been central to the development of unit root econometrics. The present paper develops a new and conceptually simple method for obtaining such forms of convergence. The method relies on the fact that the econometric quantities of interest involve discrete time martingales or semimartingales and shows how in the limit these quantities become continuous martingales and semimartingales. The limit theory itself uses very general convergence results for semimartingales …
Nonparametric Prewhitening Estimators For Conditional Quantiles, Liangjun Su, Aman Ullah
Nonparametric Prewhitening Estimators For Conditional Quantiles, Liangjun Su, Aman Ullah
Research Collection School Of Economics
We define a nonparametric prewhitening method for estimating conditional quantiles based on local linear quantile regression. We characterize the bias, variance and asymptotic normality of the proposed estimator. Under weak conditions our estimator can achieve bias reduction and have the same variance as the local linear quantile estimators. A small set of Monte Carlo simulations is carried out to illustrate the performance of our estimators. An application to US gross domestic product data demonstrates the usefulness of our methodology.
A Semiparametric Stochastic Volatility Model, Jun Yu
A Semiparametric Stochastic Volatility Model, Jun Yu
Research Collection School Of Economics
This paper examines how volatility responds to return news in the context of stochastic volatility (SV) using a nonparametric method. The correlation structure in the classical leverage SV model is generalized based on a linear spline. In the new model the correlation between the return innovation and volatility innovation is dependent on the type of news arrived to the market. Theoretical properties of the proposed model are examined. A simulation-based maximum likelihood method is developed to estimate the new model. Simulations show that the estimation method provides reliable parameter estimates. The new model is fitted to daily and weekly data …