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Articles 1 - 30 of 35
Full-Text Articles in Social and Behavioral Sciences
A Consistent Characteristic Function-Based Test For Conditional Independence, Liangjun Su, Halbert White
A Consistent Characteristic Function-Based Test For Conditional Independence, Liangjun Su, Halbert White
Research Collection School Of Economics
Y is conditionally independent of Z given X if Pr{f(y|X,Z)=f(y|X)}=1 for all y on its support, where f(·|·) denotes the conditional density of Y given (X,Z) or X. This paper proposes a nonparametric test of conditional independence based on the notion that two conditional distributions are equal if and only if the corresponding conditional characteristic functions are equal. We extend the test of Su and White (2005. A Hellinger-metric nonparametric test for conditional independence. Discussion Paper, Department of Economics, UCSD) in two directions: (1) our test is less sensitive to the choice of bandwidth sequences; (2) our test has power …
Long Run Covariance Matrices For Fractionally Integrated Processes, Peter C. B. Phillips, Sik Kim Chang
Long Run Covariance Matrices For Fractionally Integrated Processes, Peter C. B. Phillips, Sik Kim Chang
Research Collection School Of Economics
An asymptotic expansion is given for the autocovariance matrix of a vector of stationary long-memory processes with memory parameters d ∈ [0,½). The theory is then applied to deliver formulas for the long-run covariance matrices of multivariate time series with long memory.Phillips acknowledges partial support from a Kelly Fellowship and from the NSF under grant SES 04-142254. This may be proved directly using a Fourier integral asymptotic expansion when the spectrum of the short-memory component is analytic.
Nonstationary Discrete Choice: A Corrigendum And Addendum, Peter C. B. Phillips, Sainan Jin, Ling Hu
Nonstationary Discrete Choice: A Corrigendum And Addendum, Peter C. B. Phillips, Sainan Jin, Ling Hu
Research Collection School Of Economics
We correct the limit theory presented in an earlier paper by Hu and Phillips [2004a. Nonstationary discrete choice. Journal of Econometrics 120, 103-138] for nonstationary time series discrete choice models with multiple choices and thresholds. The new limit theory shows that, in contrast to the binary choice model with nonstationary regressors and a zero threshold where there are dual rates of convergence (n1/4 and n3/4), all parameters including the thresholds converge at the rate n3/4. The presence of nonzero thresholds therefore materially affects rates of convergence. Dual rates of convergence reappear when stationary variables are present in the system. Some …
Incidental Trends And The Power Of Panel Unit Root Tests, Hyungsik Roger Moon, Benoit Perrron, Peter C. B. Phillips
Incidental Trends And The Power Of Panel Unit Root Tests, Hyungsik Roger Moon, Benoit Perrron, Peter C. B. Phillips
Research Collection School Of Economics
The asymptotic local power of various panel unit root tests is investigated. The (Gaussian) power envelope is obtained under homogeneous and heterogeneous alternatives. The envelope is compared with the asymptotic power functions for the pooled t-test, the Ploberger and Phillips [2002. Optimal testing for unit roots in panel data. Mimeo] test, and a point optimal test in neighborhoods of unity that are of order n-1/4T-1 and n-1/2T-1, depending on whether or not incidental trends are extracted from the panel data. In the latter case, when the alternative hypothesis is homogeneous across individuals, it is shown that the point optimal test …
Global Analysis Of An Expectations Augmented Evolutionary Dynamics, Angelo Antoci, Antonio Gay, Massimiliano Landi, Pier Luigi Sacco
Global Analysis Of An Expectations Augmented Evolutionary Dynamics, Angelo Antoci, Antonio Gay, Massimiliano Landi, Pier Luigi Sacco
Research Collection School Of Economics
We consider a deterministic evolutionary model where players form expectations about future play. Players are not fully rational and have expectations that change over time in response to current payoffs and feedback from the past. We provide a complete characterization of the qualitative dynamics so induced for a two strategies population game, and relate our findings to standard evolutionary dynamics and equilibrium selection when agents have rational forward looking expectations.
Direction-Of-Change Forecasts Based On Conditional Variance, Skewness And Kurtosis Dynamics: International Evidence, Peter F. Christoffersen, Francis X. Diebold, Roberto S. Mariano, Anthony S. Tay, Yiu Kuen Tse
Direction-Of-Change Forecasts Based On Conditional Variance, Skewness And Kurtosis Dynamics: International Evidence, Peter F. Christoffersen, Francis X. Diebold, Roberto S. Mariano, Anthony S. Tay, Yiu Kuen Tse
Research Collection School Of Economics
Recent theoretical work has revealed a direct connection between asset return volatility forecastability and asset return sign forecastability. This suggests that the pervasive volatility forecastability in equity returns could, via induced sign forecastability, be used to produce direction-of change forecasts useful for market timing. We attempt to do so in an international sample of developed equity markets, with some success, as assessed by formal probability forecast scoring rules such as the Brier score. An important ingredient is our conditioning not only on conditional mean and variance information, but also conditional skewness and kurtosis information, when forming direction-of-change forecasts.
Some Empirics On Economic Growth Under Heterogeneous Technology, Peter C. B. Phillips, Donggyu Sul
Some Empirics On Economic Growth Under Heterogeneous Technology, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
A new econometric approach to testing for economic growth convergence is overviewed. The method is applicable to panel data, involves a simple regression based one-sided t-test, and can be used to form a clustering algorithm to assess the existence of growth convergence clubs. The approach allows for heterogeneous technology, utilizes some new asymptotic theory for nonlinear dynamic factor models, and is easy to implement. Some background growth theory is given which shows the form of augmented Solow regression (ASR) equations in the presence of heterogeneous technology and explains sources of potential misspecification that can arise in conventional formulations of ASR …
Improved Maximum-Likelihood Estimation For The Common Shape Parameter Of Several Weibull Populations, Zhenlin Yang, Dennis K. J. Lin
Improved Maximum-Likelihood Estimation For The Common Shape Parameter Of Several Weibull Populations, Zhenlin Yang, Dennis K. J. Lin
Research Collection School Of Economics
The biasness problem of the maximum-likelihood estimate (MLE) of the common shape parameter of several Weibull populations is examined in detail. A modified MLE (MMLE) approach is proposed. In the case of complete and Type II censored data, the bias of the MLE can be substantial. This is noticeable even when the sample size is large. Such a bias increases rapidly as the degree of censorship increases and as more populations are involved. The proposed MMLE, however, is nearly unbiased and much more efficient than the MLE, irrespective of the degree of censorship, the sample sizes, and the number of …
Avoiding Arbitrary Exclusion Restrictions Using Ratios Of Reduced-Form Estimates, Myoung-Jae Lee, Pao-Li Chang
Avoiding Arbitrary Exclusion Restrictions Using Ratios Of Reduced-Form Estimates, Myoung-Jae Lee, Pao-Li Chang
Research Collection School Of Economics
We show how to obtain coherent structural-form (SF) exclusion restrictions using the reduced-form (RF) parameter ratios. It will be shown that an over-identified SF corresponds to a group of regressors sharing the same RF ratio value; those regressors should be excluded jointly from the SF. If there is no group structure, then the SF is just-identified; in this case, however, it is no longer clear which regressor should be excluded. Hence, just-identified SF’s are more arbitrary than over-identified SF’s in terms of exclusion restrictions. This is in stark contrast to the notion that the former is less arbitrary than the …
More Efficient Estimation Of Nonparametric Panel Data Models With Random Effects, Liangjun Su, Aman Ullah
More Efficient Estimation Of Nonparametric Panel Data Models With Random Effects, Liangjun Su, Aman Ullah
Research Collection School Of Economics
We propose a class of two-step estimators for nonparametric panel data models with random effects that are more efficient than the conventional least squares estimators. We establish asymptotic normality for the proposed estimators and derive the most efficient estimator in the class.
Un-Balanced Economic Growth, Hing-Man Leung
Un-Balanced Economic Growth, Hing-Man Leung
Research Collection School Of Economics
Since the elasticity of substitution between capital and labor is not always one, and since technical progress is not always Harrod-neutral, it is desirable to have an endogenous growth model that admits all sizes of the elasticity and all known technology modes. We derive an equation to do just that, fully describing the per capita income growth rate at all times. It shows a typical economy needing hundreds if not thousands of years to reach its long term growth rate, leading to the conclusion that even the short run may be very long indeed.
Regression With Slowly Varying Regressors And Nonlinear Trends, Peter C. B. Phillips
Regression With Slowly Varying Regressors And Nonlinear Trends, Peter C. B. Phillips
Research Collection School Of Economics
Slowly varying (SV) regressors arise commonly in empirical econometric work, particularly in the form of semilogarithmic regression and log periodogram regression. These regressors are asymptotically collinear. Usual regression formulas for asymptotic standard errors are shown to remain valid, but rates of convergence are affected and the limit distribution of the regression coefficients is shown to be one dimensional. Some asymptotic representations of partial sums of SV functions and central limit theorems with SV weights are given that assist in the development of a regression theory. Multivariate regression and polynomial regression with SV functions are considered and shown to be equivalent, …
Direction-Of-Change Forecasts For Asian Equity Markets Based On Conditional Variance, Skewness And Kurtosis Dynamics: International Evidence, Peter F. Christoffersen, Francis X. Diebold, Robert S. Mariano, Anthony S. Tay, Yiu Kuen Tse
Direction-Of-Change Forecasts For Asian Equity Markets Based On Conditional Variance, Skewness And Kurtosis Dynamics: International Evidence, Peter F. Christoffersen, Francis X. Diebold, Robert S. Mariano, Anthony S. Tay, Yiu Kuen Tse
Research Collection School Of Economics
Recent theoretical work has revealed a direct connection between asset return volatility forecastability and asset return sign forecastability. This suggests that the pervasive volatility forecastability in equity returns could, via induced sign forecastability, be used to produce direction-of change forecasts useful for market timing. We attempt to do so in an international sample of developed equity markets, with some success, as assessed by formal probability forecast scoring rules such as the Brier score. An important ingredient is our conditioning not only on conditional mean and variance information, but also conditional skewness and kurtosis information, when forming direction-of-change forecasts.
A Corrected Plug-In Method For Quantile Interval Construction Through A Transformed Regression, Zhenlin Yang, Yiu Kuen Tse
A Corrected Plug-In Method For Quantile Interval Construction Through A Transformed Regression, Zhenlin Yang, Yiu Kuen Tse
Research Collection School Of Economics
We propose a corrected plug-in method for constructing confidence intervals of the conditional quantiles of an original response variable through a transformed regression with heteroscedastic errors. The interval is easy to compute. Factors affecting the magnitude of the correction are examined analytically through the special case of Box-Cox regression. Monte Carlo simulations show that the new method works well in general and is superior over the commonly used delta method and the quantile regression method. An empirical application is presented. [PUBLICATION ABSTRACT]
Estimation Of Impulse Response Functions Using Long Autoregression, Pao Li Chang, Shinichi Sakata
Estimation Of Impulse Response Functions Using Long Autoregression, Pao Li Chang, Shinichi Sakata
Research Collection School Of Economics
This article proposes an alternative methodology to estimate impulse response functions without imposing parametric restrictions. The impulse responses are estimated by regressing the series of interest on estimated innovations, which are the residuals obtained from a prior-stage ‘long autoregression.’ We establish the consistency and asymptotic normality of the proposed estimator. The proposed estimator is closely related to the estimator of Jordà (2005, American Economic Review 95, 161–182). Our large sample analysis, as a byproduct, establishes the asymptotic equivalence between Jordà's estimator and our estimator, and provides justifications for the statistical inference method used in Jordà (2005).
Indirect Inference For Dynamic Panel Models, Jun Yu
Indirect Inference For Dynamic Panel Models, Jun Yu
Research Collection School Of Economics
It is well-known that maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size (T) and large cross section sample size (N) asymptotics. The estimation bias is particularly relevant in practical applications when T is small and the autoregressive parameter is close to unity. The present paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference (Gouriéroux et al., 1993), shows unbiasedness and analyzes efficiency. The method is implemented in a simple linear dynamic panel model, but has wider …
Instrumental Variable Quantile Estimation Of Spatial Autoregressive Models, Zhenlin Yang
Instrumental Variable Quantile Estimation Of Spatial Autoregressive Models, Zhenlin Yang
Research Collection School Of Economics
We propose an instrumental variable quantile regression (IVQR) estimator for spatial autoregressive (SAR) models. Like the GMM estimators of Lin and Lee (2006) and Kelejian and Prucha (2006), the IVQR estimator is robust against heteroscedasticity. Unlike the GMM estimators, the IVQR estimator is also robust against outliers and requires weaker moment conditions. More importantly, it allows us to characterize the heterogeneous impact of variables on different points (quantiles) of a response distribution. We derive the limiting distribution of the new estimator. Simulation results show that the new estimator performs well in finite samples at various quantile points. In the special …
Financial Variables As Predictors Of Real Output Growth, Anthony S. Tay
Financial Variables As Predictors Of Real Output Growth, Anthony S. Tay
Research Collection School Of Economics
We investigate two methods for using daily stock returns to forecast, and update forecasts of, quarterly real output growth. Both methods aggregate daily returns in some manner to form a single stock market variable. We consider (i) augmenting the quarterly AR(1) model for real output growth with daily returns using a nonparametric Mixed Data Sampling (MIDAS) setting, and (ii) augmenting the quarterly AR(1) model with the most recent r -day returns as an additional predictor. We discover that adding low frequency stock returns (up to annual returns, depending on forecast horizon) to a quarterly AR(1) model improves forecasts of output …
Unit Root Log Periodogram Regression, Peter C. B. Phillips
Unit Root Log Periodogram Regression, Peter C. B. Phillips
Research Collection School Of Economics
Log periodogram (LP) regression is shown to be consistent and to have a mixed normal limit distribution when the memory parameter d=1. Gaussian errors are not required. The proof relies on a new result showing that asymptotically infinite collections of discrete Fourier transforms (dft's) of a short memory process at the fundamental frequencies in the vicinity of the origin can be treated as asymptotically independent normal variates, provided one does not include too many dft's in the collection.
Bayesian Analysis Of Dsge Models, Sungbae An, Frank Schorfheide
Bayesian Analysis Of Dsge Models, Sungbae An, Frank Schorfheide
Research Collection School Of Economics
This paper reviews Bayesian methods that have been developed in recent years to estimate and evaluate dynamic stochastic general equilibrium (DSGE) models. We consider the estimation of linearized DSGE models, the evaluation of models based on Bayesian model checking, posterior odds comparisons, and comparisons to vector autoregressions, as well as the non-linear estimation based on a second-order accurate model solution. These methods are applied to data generated from correctly specified and misspecified linearized DSGE models and a DSGE model that was solved with a second-order perturbation method.
Modelling Spatial Dependence And Social Interactions, Zhenlin Yang
Modelling Spatial Dependence And Social Interactions, Zhenlin Yang
Research Collection School Of Economics
Spatial dependence or social interaction among economic agents or social actors, such as neighbourhood effects, copycatting, and peer group effects, has recently received increased attention from regional scientists, economists, econometricians, and statisticians.
A Unified Confidence Interval For Reliability-Related Quantities Of Two-Parameter Weibull Distribution, Zhenlin Yang, Min Xie, Augustine C.M. Wong
A Unified Confidence Interval For Reliability-Related Quantities Of Two-Parameter Weibull Distribution, Zhenlin Yang, Min Xie, Augustine C.M. Wong
Research Collection School Of Economics
Statistical inference methods for the Weibull parameters and their functions usually depend on extensive tables, and hence are rather inconvenient for the practical applications. In this paper, we propose a general method for constructing confidence intervals for the Weibull parameters and their functions, which eliminates the need for the extensive tables. The method is applied to obtain confidence intervals for the scale parameter, the mean-time-to-failure, the percentile function, and the reliability function. Monte-Carlo simulation shows that these intervals possess excellent finite sample properties, having coverage probabilities very close to their nominal levels, irrespective of the sample size and the degree …
Global And Regional Sources Of Risk In Equity Markets: Evidence From Factor Models With Time-Varying Conditional Skewness, Aamir R. Hashmi, Anthony S. Tay
Global And Regional Sources Of Risk In Equity Markets: Evidence From Factor Models With Time-Varying Conditional Skewness, Aamir R. Hashmi, Anthony S. Tay
Research Collection School Of Economics
We examine the influence of global and regional factors on the conditional distribution of stock returns from six Asian markets, using factor models in which unexpected returns comprise global, regional and local shocks. The models allow for conditional heteroskedasticity and time-varying conditional skewness, and are used to measure mean, variance, and skewness spillovers. We find that incorporating time-varying conditional skewness improves the fit of our spillover models, and can alter measurements of variance spillovers. However, time-varying conditional skewness is mostly a local phenomenon; with exceptions, there is little spillover in skewness from global and regional factors.
A Simple Approach To The Parametric Estimation Of Potentially Nonstationary Diffusions, Federico Bandi, Peter C. B. Phillips
A Simple Approach To The Parametric Estimation Of Potentially Nonstationary Diffusions, Federico Bandi, Peter C. B. Phillips
Research Collection School Of Economics
A simple and robust approach is proposed for the parametric estimation of scalar homogeneous stochastic differential equations. We specify a parametric class of diffusions and estimate the parameters of interest by minimizing criteria based on the integrated squared difference between kernel estimates of the drift and diffusion functions and their parametric counterparts. The procedure does not require simulations or approximations to the true transition density and has the simplicity of standard nonlinear least-squares methods in discrete time. A complete asymptotic theory for the parametric estimates is developed. The limit theory relies on infill and long span asymptotics and is robust …
Simulation-Based Estimation Of Contingent-Claims Prices, Jun Yu
Simulation-Based Estimation Of Contingent-Claims Prices, 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. The present paper proposes instead a simulation-based method that improves the finite sample performance of the ML estimator while maintaining its good asymptotic properties. The methods are implemented and evaluated here in the Black-Scholes option pricing model and in the Vasicek bond pricing model, but have wider applicability. Monte Carlo …
Bias In Dynamic Panel Estimation With Fixed Effects, Incidental Trends And Cross Section Dependence, Peter C. B. Phillips, Donggyu Sul
Bias In Dynamic Panel Estimation With Fixed Effects, Incidental Trends And Cross Section Dependence, Peter C. B. Phillips, Donggyu Sul
Research Collection School Of Economics
Explicit asymptotic bias formulae are given for dynamic panel regression estimators as the cross section sample size N --> ∞. The results extend earlier work by Nickell [1981. Biases in dynamic models with fixed effects. Econometrica 49, 1417-1426] and later authors in several directions that are relevant for practical work, including models with unit roots, deterministic trends, predetermined and exogenous regressors, and errors that may be cross sectionally dependent. The asymptotic bias is found to be so large when incidental linear trends are fitted and the time series sample size is small that it changes the sign of the autoregressive …
Long Run Variance Estimation And Robust Regression Testing Using Sharp Origin Kernels With No Truncation, Peter C. B. Phillips, Yixiao Sun, Sainan Jin
Long Run Variance Estimation And Robust Regression Testing Using Sharp Origin Kernels With No Truncation, Peter C. B. Phillips, Yixiao Sun, Sainan Jin
Research Collection School Of Economics
A new family of kernels is suggested for use in long run variance (LRV) estimation and robust regression testing. The kernels are constructed by taking powers of the Bartlett kernel and are intended to be used with no truncation (or bandwidth) parameter. As the power parameter ([rho]) increases, the kernels become very sharp at the origin and increasingly downweight values away from the origin, thereby achieving effects similar to a bandwidth parameter. Sharp origin kernels can be used in regression testing in much the same way as conventional kernels with no truncation, as suggested in the work of Kiefer and …
Monotonicity Conditions And Inequality Imputation For Sample-Selection And Non-Response Problems, Myoung-Jae Lee
Monotonicity Conditions And Inequality Imputation For Sample-Selection And Non-Response Problems, Myoung-Jae Lee
Research Collection School Of Economics
Under a sample selection or non-response problem, where a response variable y is observed only when a condition δ = 1 is met, the identified mean E(y|δ = 1) is not equal to the desired mean E(y). But the monotonicity condition E(y|δ = 1) ≤ E(y|δ = 0) yields an informative bound E(y|δ = 1) ≤ E(y), which is enough for certain inferences. For example, in a majority voting with δ being the vote-turnout, it is enough to know if E(y) > 0.5 or not, for which E(y|δ = 1) > 0.5 is sufficient under the monotonicity. The main question is then …
Temporal Aggregation And Risk-Return Relation, Xing Jin, Leping Wang, Jun Yu
Temporal Aggregation And Risk-Return Relation, Xing Jin, Leping Wang, Jun Yu
Research Collection School Of Economics
The function form of a linear intertemporal relation between risk and return is suggested by Merton's [1973. Econometrica 41, 867–887] analytical work for instantaneous returns, whereas empirical studies have examined the nature of this relation using temporally aggregated data, i.e., daily, monthly, quarterly, or even yearly returns. Our paper carefully examines the temporal aggregation effect on the validity of the linear specification of the risk–return relation at discrete horizons, and on its implications on the reliability of the resulting inference about the risk–return relation based on different observation intervals. Surprisingly, we show that, based on the standard Heston's [1993. Review …
Limit Theory For Moderate Deviations From A Unit Root Under Weak Dependence, Peter C. B. Phillips, Tassos Magadalinos
Limit Theory For Moderate Deviations From A Unit Root Under Weak Dependence, Peter C. B. Phillips, Tassos Magadalinos
Research Collection School Of Economics
An asymptotic theory is given for autoregressive time series with weakly dependent innovations and a root of the form rho_{n} = 1+c/n^{alpha}, involving moderate deviations from unity when alpha in (0,1) and c in R are constant parameters. The limit theory combines a functional law to a diffusion on D[0,infinity) and a central limit theorem. For c > 0, the limit theory of the first order serial correlation coefficient is Cauchy and is invariant to both the distribution and the dependence structure of the innovations. To our knowledge, this is the first invariance principle of its kind for explosive processes. The …