This paper proves that the fully modified vector autoregression (FM-VAR) estimator has second-order bias effects when some roots are local to unity. These bias effects are shown to result in potentially severe size distortions in FM-VAR testing when the hypothesis involves near unit root variables. In addition, the paper reveals that with the FM-VAR method near unit roots become estimated as exact unit roots with convergence speed faster than the order of the sample size. Also this result implies problems for FM-VAR testing, as such “hyperconsistent” estimates give rise to degenerate limit distributions under the null hypothesis.I am grateful to Pentti Saikkonen, Jim Stock, Markku Lanne, Jukka Nyblom, and three referees for very helpful comments on earlier drafts. This paper is a part of the research program of the Research Unit on Economic Structures and Growth (RUESG) at the Department of Economics at the University of Helsinki. Financial support from the ASLA Fulbright, the Yrjö Jahnsson Foundation, and the Finnish Cultural Foundation is gratefully acknowledged. The usual disclaimer applies.

The paper presents the asymptotic theory of the efficient method of moments when the model of interest is not correctly specified. The paper assumes a sequence of independent and identically distributed observations and a global misspecification. It is found that the limiting distribution of the estimator is still asymptotically normal, but it suffers a strong impact in the covariance matrix. A consistent estimator of this covariance matrix is provided. The large sample distribution on the estimated moment function is also obtained. These results are used to discuss the situation when the moment conditions hold but the model is misspecified. It also is shown that the overidentifying restrictions test has asymptotic power one whenever the limit moment function is different from zero. It is also proved that the bootstrap distributions converge almost surely to the previously mentioned distributions and hence they could be used as an alternative to draw inferences under misspecification. Interestingly, it is also shown that bootstrap can be reliably applied even if the number of bootstrap replications is very small.The authors express their gratitude to Professor A.R. Gallant for his support and for helpful discussions about this topic. They also thank Professor Donald Andrews for his revision and for the suggestion to read the work by Hall and Inoue (2003). They thank Hall and Inoue for sending them their technical report. Finally the authors thank two anonymous referees for their helpful comments, which led to a significant improvement of the paper. The views expressed in this paper are our sole responsibility. Víctor Aguirre-Torres acknowledges partial support from the Asociación Mexicana de la Cultura A.C. Manuel A. Domínguez acknowledges funding from CONACYT, research project J38076-D, and partial support from Asociación Mexicana de la Cultura A.C.

This paper adopts a two-step technique to estimate structural vector error correction models and provides the asymptotic distribution of the impulse response functions of such a system. The method combines two popular tools in econometrics, namely, vector autoregressive cointegration analysis in the first step and structural vector autoregression analysis in the second. The proposed structural model structure is very general in the sense that all just-identifying or overidentifying schemes that can be expressed as linear restrictions on either the contemporaneous or long-run impact of the structural shocks are allowed for. The long-run restrictions complicate the derivation of the asymptotic distribution of the structural parameter estimates as these restrictions are a function of the reduced form parameters. Consequently, the asymptotic distribution involves an extra partial derivative.Useful comments by Peter Boswijk, Günter Coenen, Neil Ericsson, Sören Johansen, Klaus Neusser, Franz Palm, Paolo Paruolo, Peter van Els, Anders Warne, and ESEM 1998 participants are gratefully acknowledged. The paper also significantly benefited from suggestions by the co-editor Pentti Saikkonen and two anonymous referees.

We suggest a sequential monitoring scheme to detect changes in the parameters of a GARCH(p,q) sequence. The procedure is based on quasi-likelihood scores and does not use model residuals. Unlike for linear regression models, the squared residuals of nonlinear time series models such as generalized autoregressive conditional heteroskedasticity (GARCH) do not satisfy a functional central limit theorem with a Wiener process as a limit, so its boundary crossing probabilities cannot be used. Our procedure nevertheless has an asymptotically controlled size, and, moreover, the conditions on the boundary function are very simple; it can be chosen as a constant. We establish the asymptotic properties of our monitoring scheme under both the null of no change in parameters and the alternative of a change in parameters and investigate its finite-sample behavior by means of a small simulation study.This research was partially supported by NSF grant INT-0223262 and NATO grant PST.CLG.977607. The work of the first author was supported by the Hungarian National Foundation for Scientific Research, grants T 29621, 37886; the work of the second author was supported by NSERC Canada.

The constant conditional correlation general autoregressive conditional heteroskedasticity (GARCH) model is among the most commonly applied multivariate GARCH models and serves as a benchmark against which other models can be compared. In this paper we consider an extension to this model and examine its fourth-moment structure. The extension, first defined by Jeantheau (1998, Econometric Theory 14, 70–86), is motivated by the result found and discussed in this paper that the squared observations from the extended model have a rich autocorrelation structure. This means that already the first-order model is capable of reproducing a whole variety of autocorrelation structures observed in financial return series. These autocorrelations are derived for the first- and the second-order constant conditional correlation GARCH model. The usefulness of the theoretical results of the paper is demonstrated by reconsidering an empirical example that appeared in the original paper on the constant conditional correlation GARCH model.This research has been supported by the Swedish Research Council of Humanities and Social Sciences and the Tore Browaldh's Foundation. A part of this work was carried out while the second author was visiting the School of Finance and Economics, University of Technology, Sydney, whose kind hospitality is gratefully acknowledged. The paper has been presented at the Econometric Society European Meeting, Venice, August 2002. We thank participants for comments and two anonymous referees for their remarks. Any errors and shortcomings in the paper remain our own responsibility.

Recent literature shows that embedding fractionally integrated time series models with spectral poles at the long-run and/or seasonal frequencies in autoregressive frameworks leads to estimators and test statistics with nonstandard limiting distributions. However, we demonstrate that when embedding such models in a general I(d) framework the resulting estimators and tests regain desirable properties from standard statistical analysis. We show the existence of a local time domain maximum likelihood estimator and its asymptotic normality—and under Gaussianity asymptotic efficiency. The Wald, likelihood ratio, and Lagrange multiplier tests are asymptotically equivalent and chi-squared distributed under local alternatives. With independent and identically distributed Gaussian errors and a scalar parameter, we show that the tests in addition achieve the asymptotic Gaussian power envelope of all invariant unbiased tests; i.e., they are asymptotically uniformly most powerful invariant unbiased against local alternatives. In a Monte Carlo study we document the finite sample superiority of the likelihood ratio test.I am grateful to Bent Jesper Christensen, Niels Haldrup, Pentti Saikkonen (the co-editor), and two anonymous referees for many useful comments and suggestions that significantly improved this paper. This work was done while the author was at the University of Aarhus, Denmark.

We define the Bernstein copula and study its statistical properties in terms of both distributions and densities. We also develop a theory of approximation for multivariate distributions in terms of Bernstein copulas. Rates of consistency when the Bernstein copula density is estimated empirically are given. In order of magnitude, this estimator has variance equal to the square root of the variance of common nonparametric estimators, e.g., kernel smoothers, but it is biased as a histogram estimator.We would thank Mark Salmon for interesting us in the copula function and Peter Phillips, an associate editor, and the referees for many valuable comments. All remaining errors are our sole responsibility.

Several estimation procedures such as the efficient method of moments (EMM) of Gallant and Tauchen (1996, Econometric Theory 12, 657–681) and indirect inference procedure of Gouriéroux, Monfort, and Renault (1993, Journal of Applied Econometrics 8, S85–S118) involve two models, an auxiliary one and a model of interest. The role played by both models poses challenges and provides new opportunities for hypothesis testing beyond the usual Wald-, Lagrange multiplier–, and likelihood ratio–type tests. In this paper we present and derive the asymptotic distribution theory for various classes of tests for structural change. Some procedures are extensions of standard tests, whereas others are specific to the dual model setup and exploit its unique features.The first author gratefully acknowledges financial support from Fonds pour la Formation de Chercheurs et l'aide à la Recherche (FCAR). The second author acknowledges the financial support of the Natural Sciences and Engineering Research Council of Canada through a grant to NCM2 (Network for Computing and Mathematical Modeling). We also thank Alastair Hall and Éric Renault for comments on an earlier draft of the paper.

Existing simulation-based estimation methods are either general purpose but asymptotically inefficient or asymptotically efficient but only suitable for restricted classes of models. This paper studies a simulated maximum likelihood method that rests on estimating the likelihood nonparametrically on a simulated sample. We prove that this method, which can be used on very general models, is consistent and asymptotically efficient for static models. We then propose an extension to dynamic models and give some Monte-Carlo simulation results on a dynamic Tobit model.We thank Jean-Pierre Florens, Arnoldo Frigessi, Christian Gouriéroux, Jim Heckman, Guy Laroque, Oliver Linton, Nour Meddahi, Alain Monfort, Eric Renault, Christian Robert, Neil Shephard, and two referees for their comments. Remaining errors and imperfections are ours. Parts of this paper were written while Bernard Salanié was visiting the University of Chicago, which he thanks for its hospitality.

The rank-based unit root tests proposed by Hasan and Koenker (1997, Econometrica 65, 133–161) have power equal to size for normal innovations. Unit root tests based on M-estimators exhibit the same behavior. The problem occurs because the test statistics are transformed to obtain computationally convenient critical values. I describe a convenient way to compute critical values without transforming the test statistics. The resulting tests are almost as powerful as least squares–based tests for normal errors and much more powerful for thicker tailed distributions.I thank Thomas Rothenberg for many useful comments. This paper is based on my dissertation, which he supervised. I also thank Jack Porter, Jim Powell, Richard Stanton, and Jim Stock for good advice. Comments of Bruce Hansen, the editor, and two anonymous referees improved the exposition.

Consistency and asymptotic normality are established for the highly applied quasi-maximum likelihood estimator in the GARCH(1,1) model. Contrary to existing literature we allow the parameters to be in the region where no stationary version of the process exists. This has the important implication that the likelihood-based estimator for the GARCH parameters is consistent and asymptotically normal in the entire parameter region including both stationary and explosive behavior. In particular, there is no “knife edge result like the unit root case” as hypothesized in Lumsdaine (1996, Econometrica 64, 575–596).Anders Rahbek is grateful for support from the Danish Social Sciences Research Council, the Centre for Analytical Finance (CAF), and the EU network DYNSTOCH. Both authors thank the two anonymous referees and the editor for highly valuable and detailed comments that have, we believe, led to a much improved version of the paper, both in terms of the econometric theory and of the presentation.

This paper proposes a semiparametric approach by introducing a smooth scale function into the standard generalized autoregressive conditional heteroskedastic (GARCH) model so that conditional heteroskedasticity (CH) and scale change in financial returns can be modeled simultaneously. An estimation procedure combining kernel estimation of the scale function and maximum likelihood estimation of the GARCH parameters is proposed. Asymptotic properties of the estimators are investigated in detail. It is shown that asymptotically normal, -consistent parameter estimation is available. A data-driven algorithm is developed for practical implementation. Finite sample performance of the proposal is studied through simulation. The proposal is applied to model CH and scale change in the daily S&P 500 and DAX 100 returns. It is shown that both series have simultaneously significant scale change and CH.We are very grateful to the co-editor and two referees for their helpful comments and suggestions, which led to a substantial improvement of this paper. The paper was finished under the advice of Professor Jan Beran, Department of Mathematics and Statistics, University of Konstanz, Germany, and was financially supported by the Center of Finance and Econometrics (CoFE), University of Konstanz. We thank colleagues in CoFE, especially Professor Winfried Pohlmeier, for their interesting questions at a talk of the author. It was these questions that motivated the author to write this paper. Our special thanks go to Dr. Erik Lüders, Department of Finance and Insurance, Laval University, and Stern School of Business, New York University, for his helpful suggestions.

We derive the asymptotic distribution of the estimate of the cointegration rank of a multivariate model when Akaike's information criterion is used. It is shown that the use of this criterion is ill-advised given that the estimate is severely upward biased even asymptotically.I thank the editor, Professor Peter Phillips, and three anonymous referees for comments and suggestions that improved this paper significantly. All remaining errors are my own.

We present a goodness-of-fit test for time series models based on the discrete spectral average estimator. Unlike current tests of goodness of fit, the asymptotic distribution of our test statistic allows the null hypothesis to be either a short- or long-range dependence model. Our test is in the frequency domain, is easy to compute, and does not require the calculation of residuals from the fitted model. This is especially advantageous when the fitted model is not a finite-order autoregressive model. The test statistic is a frequency domain analogue of the test by Hong (1996, Econometrica 64, 837–864), which is a generalization of the Box and Pierce (1970, Journal of the American Statistical Association 65, 1509–1526) test statistic. A simulation study shows that our test has power comparable to that of Hong's test and superior to that of another frequency domain test by Milhoj (1981, Biometrika 68, 177–187).

We consider models for financial data by Lévy processes, including hyperbolic, normal inverse Gaussian, and Carr, Geman, Madan, and Yor (CGMY) processes. They are given by their Lévy triplet (μ(θ),σ2,eθxg(x)ν(dx)), where μ denotes the drift, σ2 the diffusion, and eθxg(x)ν(dx) the Lévy measure, and the unknown parameter θ models the skewness of the process. We provide local asymptotic normality results and construct efficient estimators for the skewness parameter θ taking into account different discrete sampling schemes.I thank Prof. Dr. L. Rüschendorf for his steady encouragement, the referees for helpful comments, and the German National Scholarship Foundation for financial support.

We establish the necessary and sufficient conditions for covariance stationarity of ARCH(∞), for both the levels and the squares. The result applies to any form of the conditional variance coefficients. This includes GARCH(p,q) and also specifications with hyperbolically decaying coefficients, such as the autoregressive coefficients of the autoregressive fractionally integrated moving average model. The covariance stationarity condition for the levels rules out long memory in the squares.I thank Peter M. Robinson for useful comments on previous versions of the paper. Also, I am grateful to the co-editor (Bruce E. Hansen) and an anonymous referee whose suggestions greatly improved the paper.

Professor David F. Hendry is interviewed by Neil R. Ericsson.

We examine properties of residual-based tests for the null of no cointegration for dynamic panels in which both the short-run dynamics and the long-run slope coefficients are permitted to be heterogeneous across individual members of the panel. The tests also allow for individual heterogeneous fixed effects and trend terms, and we consider both pooled within dimension tests and group mean between dimension tests. We derive limiting distributions for these and show that they are normal and free of nuisance parameters. We also provide Monte Carlo evidence to demonstrate their small sample size and power performance, and we illustrate their use in testing purchasing power parity for the post–Bretton Woods period.I thank Rich Clarida, Bob Cumby, Mahmoud El-Gamal, Heejoon Kang, Chiwha Kao, Andy Levin, Klaus Neusser, Masao Ogaki, David Papell, Pierre Perron, Abdel Senhadji, Jean-Pierre Urbain, Alan Taylor, and three anonymous referees for helpful comments on various earlier versions of this paper. The paper has also benefited from presentations at the 1994 North American Econometric Society Summer Meetings in Quebec City, the 1994 European Econometric Society Summer Meetings in Maastricht, and workshop seminars at the Board of Governors of the Federal Reserve, INSEE-CREST Paris, IUPUI, Ohio State, Purdue, Queens University Belfast, Rice University–University of Houston, and Southern Methodist University. Finally, I thank the following students who provided assistance in the earlier stages of the project: Younghan Kim, Rasmus Ruffer, and Lining Wan.

This paper investigates the asymptotic properties of the t-statistic in spurious regressions when the bandwidth in the estimation of the heteroskedasticity and autocorrelation consistent (HAC) standard error is set proportional to the sample size. Using autocovariances of large lags, the so-defined HAC estimator is capable of capturing the high persistence of the regressor and regression residuals. It is shown that the resulting t-statistic converges to a nondegenerate limiting distribution for all cases of spurious regressions considered in the literature. This finding sheds some new light on the nature of spurious regressions.I am very grateful to Bruce Hansen and two anonymous referees for helpful comments and suggestions. All remaining errors and omissions are mine alone.

This paper presents results on the issue of weak convergence of a TVP-GQARCH-M(1,1) process. These suggest that the weak limit of this endogenous volatility model is an exogenous (stochastic) volatility continuous time process. Under the appropriate assumptions, we derive the invariant distributions at which the process converges in various cases. They reveal an approximate distributional relation between the GARCH or the asymmetric GQARCH variance processes and the continuous time Ornstein–Uhlenbeck models with respect to appropriate nonnegative Levy processes.I am grateful to Ritsa Panagiotou, Phyllis Alexander, Antonis Demos, Bruce Hansen, Nicholas Magginas, Nour Meddahi, Enrique Sentana, Paolo Zaffaroni, two anonymous referees, and the seminar participants at the Department of International and European Economic Studies for their valuable comments.