In this paper we discuss weak dependence and mixing properties of some popular models. We also develop some of their econometric applications. Autoregressive models, autoregressive conditional heteroskedasticity (ARCH) models, and bilinear models are widely used in econometrics. More generally, stationary Markov modeling is often used. Bernoulli shifts also generate many useful stationary sequences, such as autoregressive moving average (ARMA) or ARCH(∞) processes. For Volterra processes, mixing properties obtain given additional regularity assumptions on the distribution of the innovations.

We recall associated probability limit theorems and investigate the nonparametric estimation of those sequences.We first thank the editor for the huge amount of additional editorial work provided for this review paper. The efficiency of the numerous referees was especially useful. The error pointed out in Hall and Horowitz (1996) was the origin of the present paper, and we thank the referees for asking for a more detailed treatment of a correct proof for this paper in Section 2.3. Also we thank Marc Henry and Rafal Wojakowski for a very careful rereading of the paper. An anonymous referee has been particularly helpful in the process of revision of the paper. The authors thank him for his numerous suggestions of improvement, including important results on negatively associated sequences and a thorough update in standard English.

Weak convergence results for sample averages of nonlinear functions of (discrete-time) stochastic processes satisfying a functional central limit theorem (e.g., integrated processes) are given. These results substantially extend recent work by Park and Phillips (1999, Econometric Theory 15, 269–298) and de Jong (2002, working paper), in that a much wider class of functions is covered. For example, some of the results hold for the class of all locally integrable functions, thus avoiding any of the various regularity conditions imposed on the functions in Park and Phillips (1999) or de Jong (2002).I thank Robert de Jong for drawing my attention to this problem and Hannes Leeb for helpful comments. This paper was presented at the Econometric Society European Meeting 2002 in Venice.

In establishing the Problems and Solutions (P&S) series of Econometric Theory, the primary objective was pedagogical—to provide an intellectual resource for students, instructors, and researchers through the publication of student exercises and research-level problems in econometrics. Over the past 20 years, the P&S series has successfully served this goal. Large numbers of worked exercises and problems have now been published, and the series has promoted interaction between problem solvers, problem posers, and the ET readership, thereby helping to bring the wider econometric community together in a common purpose.

This paper develops semiparametric kernel-based estimators of risk-specific hazard functions for competing risks data. Both discrete and continuous failure times are considered. The maintained assumption is that the hazard function depends on explanatory variables only through an index. In contrast to existing semiparametric estimators, proportional hazards is not assumed. The new estimators are asymptotically normally distributed. The estimator of index coefficients is root-n consistent. The estimator of hazard functions achieves the one-dimensional rate of convergence. Thus the index assumption eliminates the “curse of dimensionality.” The estimators perform well in Monte Carlo experiments.I thank Denise Doiron for stimulating my interest in this research project and Catherine de Fontenay, Hans Christian Kongsted, Lars Muus, seminar participants, and two anonymous referees for comments on an earlier version of the paper. I gratefully acknowledge the hospitality of the University of Aarhus and the University of Copenhagen, where part of this research was undertaken.

This paper uses the concept of dual likelihood to develop some higher order asymptotic theory for the empirical likelihood ratio test for parameters defined implicitly by a set of estimating equations. The resulting theory is likelihood based in the sense that it relies on methods developed for ordinary parametric likelihood models to obtain valid Edgeworth expansions for the maximum dual likelihood estimator and for the dual/empirical likelihood ratio statistic. In particular, the theory relies on certain Bartlett-type identities that can be used to produce a simple proof of the existence of a Bartlett correction for the dual/empirical likelihood ratio. The paper also shows that a bootstrap version of the dual/empirical likelihood ratio achieves the same higher order accuracy as the Bartlett-corrected dual/empirical likelihood ratio.This paper is based on Chapter 2 of my Ph.D. dissertation at the University of Southampton. Partial financial support under E.S.R.C. grant R00429634019 is gratefully acknowledged. I thank my supervisor, Grant Hillier, for many stimulating conversations and Peter Phillips, Andrew Chesher, and Jan Podivisnky for some useful suggestions. In addition, I am very grateful to the co-editor Donald Andrews and two referees for many valuable comments that have improved noticeably the original draft. All remaining errors are my own responsibility.

Threshold models (sample splitting models) have wide application in economics. Existing estimation methods are confined to regression models, which require that all right-hand-side variables are exogenous. This paper considers a model with endogenous variables but an exogenous threshold variable. We develop a two-stage least squares estimator of the threshold parameter and a generalized method of moments estimator of the slope parameters. We show that these estimators are consistent, and we derive the asymptotic distribution of the estimators. The threshold estimate has the same distribution as for the regression case (Hansen, 2000, Econometrica 68, 575–603), with a different scale. The slope parameter estimates are asymptotically normal with conventional covariance matrices. We investigate our distribution theory with a Monte Carlo simulation that indicates the applicability of the methods.We thank the two referees and co-editor for constructive comments. Hansen thanks the National Science Foundation for financial support. Caner thanks University of Pittsburgh Central Research Development Fund for financial support.

Elliott, Rothenberg, and Stock (1996, Econometrica 64, 813–836) derive a class of point-optimal unit root tests in a time series model with Gaussian errors. Other authors have proposed “robust” tests that are not optimal for any model but perform well when the error distribution has thick tails. I derive a class of point-optimal tests for models with non-Gaussian errors. When the true error distribution is known and has thick tails, the point-optimal tests are generally more powerful than the tests of Elliott et al. (1996) and also than the robust tests. However, when the true error distribution is unknown and asymmetric, the point-optimal tests can behave very badly. Thus there is a trade-off between robustness to unknown error distributions and optimality with respect to the trend coefficients.This paper could not have been written without the encouragement of Thomas Rothenberg. This is based on my dissertation, which he supervised. I also thank Don Andrews, Jack Porter, Jim Stock, and seminar participants at the University of Pennsylvania, the University of Toronto, the University of Montreal, Princeton University, and the meetings of the Econometric Society in UCLA. Comments of three anonymous referees greatly improved the exposition of the paper. I owe special thanks to Gary Chamberlain for helping me to understand these results.

The maximum likelihood estimator (MLE) of the fractional difference parameter in the Gaussian ARFIMA(0,d,0) model is well known to be asymptotically N(0,6/π2). This paper develops asymptotic expansions to the distribution of this statistic under the assumption of a known unit variance. The correction term for the density is shown to be independent of d, so that the MLE is second-order pivotal for d. This feature of the MLE is unusual, at least in time series contexts. Simulations show that the normal approximation is poor and that the expansions can make a significant improvement in accuracy provided the correction terms are computed without further asymptotic approximation.This paper was commenced and revised while Lieberman was visiting the Cowles Foundation during 2000–2002. Lieberman thanks the Cowles Foundation for support and hospitality during this visit. Phillips thanks the NSF for support under grants SBR 97-30295 and SES 0092509.

We propose an optimal test procedure for testing the marginal density functions of a class of nonlinear diffusion processes. The proposed test is not only an optimal one but also avoids undersmoothing. An adaptive test is constructed, and its asymptotic properties are investigated. To show the asymptotic properties, we establish some general results for moment inequalities and asymptotic distributions for strictly stationary processes under the α-mixing condition. These results are applicable to some other estimation and testing of strictly stationary processes with the α-mixing condition. An example of implementation is given to demonstrate that the proposed model specification procedure is applicable to economic and financial model specification and can be implemented in practice. To ensure the applicability and implementation, we propose a computer-intensive simulation scheme for the choice of a suitable bandwidth involved in the kernel estimation and also a simulated critical value for the proposed adaptive test. Our finite sample studies support both the proposed theory and the simulation procedure.The authors thank the co-editor and three anonymous referees for their constructive comments and suggestions. The first author also thanks Song Xi Chen for some constructive suggestions, in particular the suggestion on using the local linear form instead of the Nadaraya–Watson kernel form in equation (2.6), and Yongmiao Hong for sending a working paper. The authors acknowledge comments from seminar participants at the International Chinese Statistical Association Meeting in Hong Kong in July 2001, the Western Australian Branch Meeting of the Statistical Society of Australia in September 2001, the University of Western Australia, and Monash University. Thanks also go to the Australian Research Council for its financial support.

In this paper we derive representations for the limiting distributions of the regression-based seasonal unit root test statistics of Hylleberg, Engle, Granger, and Yoo (1990, Journal of Econometrics 44, 215–238) and Beaulieu and Miron (1993, Journal of Econometrics 55, 305–328), inter alia, when the underlying process displays near seasonal integration. Our results generalize those presented in previous studies by allowing for an arbitrary seasonal periodicity (including the nonseasonal case), a wide range of possible assumptions on the initial conditions, a range of (seasonal) deterministic mean effects, and finite autoregressive behavior in the driving shocks. We use these representations to simulate the asymptotic local power functions of the seasonal unit root tests, demonstrating a significant dependence on serial correlation nuisance parameters in the case of the pairs of t-statistics, but not the associated F-statistic, for unit roots at the seasonal harmonic frequencies. Monte Carlo simulation results are presented that suggest that the local limiting distribution theory provides a good approximation to the finite-sample behavior of the statistics. Our results lend further weight to the advice of previous authors that inference on the unit root hypothesis at the seasonal harmonic frequencies should be based on the F-statistic, rather than on the associated pairs of t-ratios.We are grateful to Bruce Hansen and two anonymous referees for their helpful comments and suggestions on earlier versions of this paper.

We introduce a nonparametric regression estimator that is consistent in the presence of measurement error in the explanatory variable when one repeated observation of the mismeasured regressor is available. The approach taken relies on a useful property of the Fourier transform, namely, its ability to convert complicated integral equations into simple algebraic equations. The proposed estimator is shown to be asymptotically normal, and its rate of convergence in probability is derived as a function of the smoothness of the densities and conditional expectations involved. The resulting rates are often comparable to kernel deconvolution estimators, which provide consistent estimation under the much stronger assumption that the density of the measurement error is known. The finite-sample properties of the estimator are investigated through Monte Carlo experiments.This work was made possible in part through financial support from the National Science Foundation via grant SES-0214068. The author is grateful to the referees and the co-editor for their helpful comments.

Semiparametric generalized additive models are a powerful tool in quantitative econometrics. With response Y, covariates X,T, the considered model is E(Y |X;T) = G{XTβ + α + m1(T1) + ··· + md(Td)}. Here, G is a known link, α and β are unknown parameters, and m1,…,md are unknown (smooth) functions of possibly higher dimensional covariates T1,…,Td. Estimates of m1,…,md, α, and β are presented, and asymptotic distributions are given for both the nonparametric and the parametric part. The main focus of the paper is application of bootstrap methods. It is shown how bootstrap can be used for bias correction, hypothesis testing (e.g., component-wise analysis), and the construction of uniform confidence bands. Further, bootstrap tests for model specification and parametrization are given, in particular for testing additivity and link function specification. The practical performance of the methods is illustrated in a simulation study.This research was supported by the Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 373 “Quantifikation und Simulation ökonomischer Prozesse,” Humboldt-Universität zu Berlin, DFG project MA 1026/6-2, the Spanish “Dirección General de Enseñanza Superior,” no. BEC2001-1270, and the grant “Nonparametric methods in finance and insurance” from the Danish Social Science Research Council. We thank Marlene Müller, Oliver Linton, and two anonymous referees for helpful discussion.

Two new stationarity tests are proposed. Both tests can be viewed as generalizations of existing stationarity tests and dominate these in terms of local asymptotic power. Improvements are achieved by accommodating stationary covariates. A Monte Carlo investigation of the small sample properties of the tests is conducted, and an empirical illustration from international finance is provided.This paper has benefited from the comments of Pentti Saikkonen (the co-editor), two anonymous referees, and seminar participants at University of Aarhus, Indiana University, Purdue University, Stanford University, UC Riverside, the 2001 Nordic Econometric Meeting, and the 2001 NBER Summer Institute. A MATLAB program that implements the tests proposed in this paper is available at http://elsa.Berkeley.EDU/users/mjansson.

We investigate a new separable nonparametric model for time series, which includes many autoregressive conditional heteroskedastic (ARCH) models and autoregressive (AR) models already discussed in the literature. We also propose a new estimation procedure called LIVE, or local instrumental variable estimation, that is based on a localization of the classical instrumental variable method. Our method has considerable computational advantages over the competing marginal integration or projection method. We also consider a more efficient two-step likelihood-based procedure and show that this yields both asymptotic and finite-sample performance gains.This paper is based on Chapter 2 of the first author's Ph.D. dissertation from Yale University. We thank Wolfgang Härdle, Joel Horowitz, Peter Phillips, and Dag Tjøstheim for helpful discussions. We are also grateful to Donald Andrews and two anonymous referees for valuable comments. The second author thanks the National Science Foundation and the ESRC for financial support.

This paper proposes some tests for fitting a regression model with a long memory covariate process and with errors that form either a martingale difference sequence or a long memory moving average process, independent of the covariate. The tests are based on a partial sum process of the residuals from the fitted regression. The asymptotic null distribution of this process is discussed in some detail under each set of these assumptions. The proposed tests are shown to have known asymptotic null distributions in the case of martingale difference errors and also in the case of fitting a polynomial of a known degree through the origin when the errors have long memory. The theory is then illustrated with some examples based on the forward premium anomaly where a squared interest rate differential proxies a time dependent risk premium. The paper also shows that the proposed test statistic converges weakly to nonstandard distributions in some cases.The authors gratefully acknowledge the helpful comments of the co-editor Don Andrews and two anonymous referees. The research of the first two authors was partly supported by NSF grant DMS 00-71619.

This paper considers testing for normality for correlated data. The proposed test procedure employs the skewness-kurtosis test statistic, but studentized by standard error estimators that are consistent under serial dependence of the observations. The standard error estimators are sample versions of the asymptotic quantities that do not incorporate any downweighting, and, hence, no smoothing parameter is needed. Therefore, the main feature of our proposed test is its simplicity, because it does not require the selection of any user-chosen parameter such as a smoothing number or the order of an approximating model.We are very grateful to Don Andrews and two referees for useful comments and suggestions. We are especially thankful to a referee who provided a FORTRAN code. Lobato acknowledges financial support from Asociación Mexicana de Cultura and from Consejo Nacional de Ciencia y Tecnologìa (CONACYT) under project grant 41893-S. Velasco acknowledges financial support from Spanish Dirección General de Enseñanza Superior, BEC 2001-1270.

This paper studies the smooth transition regression model where regressors are I(1) and errors are I(0). The regressors and errors are assumed to be dependent both serially and contemporaneously. Using the triangular array asymptotics, the nonlinear least squares estimator is shown to be consistent, and its asymptotic distribution is derived. It is found that the asymptotic distribution involves a bias under the regressor-error dependence, which implies that the nonlinear least squares estimator is inefficient and unsuitable for use in hypothesis testing. Thus, this paper proposes a Gauss–Newton type estimator that uses the nonlinear least squares estimator as an initial estimator and is based on regressions augmented by leads and lags. Using leads and lags enables the Gauss–Newton estimator to eliminate the bias and have a mixture normal distribution in the limit, which makes it more efficient than the nonlinear least squares estimator and suitable for use in hypothesis testing. Simulation results indicate that the results obtained from the triangular array asymptotics provide reasonable approximations for the finite-sample properties of the estimators and t-tests when sample sizes are moderately large. The cointegrating smooth transition regression model is applied to the Korean and Indonesian data from the Asian currency crisis of 1997. The estimation results partially support the interest Laffer curve hypothesis. But overall the effects of interest rate on spot exchange rate are shown to be quite negligible in both nations.This paper was partly written while the first author was visiting the Institute of Statistics and Econometrics at Humboldt University, Berlin. This author acknowledges financial support from the Alexander von Humboldt Foundation under a Humboldt Research Award and from the Yrjö Jahnsson Foundation. The second author wrote this paper while visiting the Cowles Foundation for Research in Economics, Yale University. This author thanks the faculty and staff of the Cowles Foundation, especially Don Andrews, John Geanakoplos, David Pearce, Peter Phillips, and Nora Wiedenbach, for their support and hospitality. The second author was financially supported for the research in this paper by Kookmin University. The authors thank Don Andrews, Helmut Lütkepohl, Peter Phillips, Bruce Hansen, and two referees for their valuable comments on this paper. Part of the data studied in this paper was provided by Chi-Young Song, whom we thank.

This paper shows that nonparametric identification of latent competing risks models is possible without the usual conditional independence and exclusion restrictions.The authors thank Jinyong Hahn, James Heckman, and Shinichi Sakata for useful discussions on the subject matter of this paper. Any errors are the fault of the authors. Research funding for Rilstone was provided by the Social Sciences and Humanities Research Council of Canada.

In this paper we derive the necessary and sufficient conditions for the t-ratio to be Student's t distributed. In particular, it is demonstrated for a special case that under conditions of nonnormality characterized by elliptical symmetry, the t-ratio remains Student's t distributed provided that the random vector forming the t-ratio has a diagonal covariance structure. Our results also show that the findings of Magnus (2002, in A. Ullah, A.T.K. Wan, & A. Chaturvedi (eds.), Handbook of Applied Econometrics and Statistical Inference, 277–285) on the sensitivity of the t-ratio remain invariant in the elliptically symmetric distribution setting. Extension to the linear model is considered. Exact results giving finite sample justification for the t-statistic under nonnormal error terms are derived. Furthermore, the distribution of the F-ratio assuming elliptical errors is examined. Our results reject the argument by Zaman (1996, Statistical Foundations for Econometric Techniques) that nonnormality of disturbances has in general no effect on the F-statistic.The authors thank Anurag Banerjee, Judith Clarke, Kazuhiro Ohtani, and Guohua Zou for their helpful suggestions and advice during the course of this work. In particular, we are very grateful to Judith Clarke for bringing to our attention the unpublished work of D.H. Thomas and to Guohua Zou for his careful reading of an earlier version of this paper. Thanks also go to the co-editor Benedikt Pötscher and two anonymous referees for their valuable comments. The second author gratefully acknowledges financial support from the Hong Kong Research Grant Council.

In this paper we investigate the impact that starting values have upon the double differencing tests of Hasza and Fuller (1979, Annals of Statistics 7, 1106–1120) and Sen and Dickey (1987, Journal of Business and Economic Statistics 5, 463–473), based on ordinary least squares (OLS) and simple symmetric least squares (SSLS) estimation, respectively. We demonstrate that, contrary to what is observed for conventional unit root tests, when based on data that have been demeaned, either directly or by the inclusion of a constant in the test regression, these tests are not exact similar to the starting values of the process, except where they are fixed and equal. We show that such test statistics can also fail to be asymptotically pivotal, contrary to claims made previously in the literature. We demonstrate that OLS tests based on data that have been detrended, either directly or by the inclusion of a constant and linear time trend in the test regression, do provide exact similar inference. In the context of the SSLS-based approach, we demonstrate that one obtains exact similar tests only where direct detrending is used. We highlight another error that appears in the literature, demonstrating that the two demeaned OLS-based test statistics do not coincide, even asymptotically, and that the same holds for the OLS- and SSLS-based test statistics for detrended data. We use Monte Carlo methods to quantify the finite-sample dependence of these tests on the starting values. These results suggest that the SSLS-based tests are considerably more sensitive to the nature of the starting values than are the OLS-based tests.We are grateful to Pentti Saikkonen and two anonymous referees for their helpful and constructive comments on an earlier version of this paper. We are especially grateful to one of the referees for the suggestion implemented in Remark 3.1 of the paper.