This paper considers the properties of systems likelihood procedures for cointegrated systems when the I(2) variables are present. Two alternative methods are proposed: one is based on the full system likelihood, whereas another is based on the subsystem likelihood. By eliminating all unit roots in the system by the use of prior information concerning the presence of unit roots, these procedures yield estimates whose asymptotic distributions are mixed normal, free from nuisance parameters, and median-unbiased. Both methods are extensions of a full system maximum likelihood procedure by Phillips (1991a) to I(2) models. Three cases of cointegration with I(2) variables are considered in order to cover a wide variety of cointegration relationships. A triangular ECM representation and the two ML estimates are derived for each case, and the asymptotics are discussed as well. The asymptotic efficiency concerning the two estimates are considered.

This paper develops the asymptotic theory for least absolute deviation estimation of a shift in linear regressions. Rates of convergence and asymptotic distributions for the estimated regression parameters and the estimated shift point are derived. The asymptotic theory is developed both for fixed magnitude of shift and for shift with magnitude converging to zero as the sample size increases. Asymptotic distributions are also obtained for trending regressors and for dependent disturbances. The analysis is carried out in the framework of partial structural change, allowing some parameters not to be influenced by the shift. Efficiency relative to least-squares estimation is also discussed. Monte Carlo analysis is performed to assess how informative the asymptotic distributions are.

We define, in a dynamic framework, the notions of binding functions, images, reflecting sets, indirect identification, indirect information, and encompassing. We study the properties of the notion of encompassing when the true distribution does not necessarily belong to one of the two competing models of interest. In this context we propose various test procedures of the encompassing hypothesis. Some of these procedures are based on simulations, and some of them are linked with the notion of indirect estimation (in particular, the GET and simulated GET procedures). As a by-product, we get an asymptotic theory of the tests of non-nested hypotheses in the stationary dynamic case.

One of the more obvious empirical characteristics of macroeconomic time series is their tendency to grow, or trend, over time. Dealing with this trendnonstationarity in models of multiple time series has been a major agenda of econometric research for much of the last decade and has produced an enormous literature. Equally, the goal of developing a general asymptotic theory of inference for stochastic processes has been a long-standing concern of probabilists and statisticians. Finally, understanding and modeling trend processes and cyclical activity lie at the nerve center of much of modern macroeconomics. As a consequence, research on nonstationary time series has brought statisticians, econometricians, and macroeconomists close together in productive ways that simply could not have been anticipated 10 years ago.

The focus of this symposium issue of Econometric Theory is inference from multiple time series data with trends, and the symposium brings together researchers with these diverse interests. The papers included in the issue were, with two exceptions, presented at a conference called “Trending Multiple Time Series,” held at Yale University in the fall of 1993 under the financial sponsorship of the National Science Foundation. All of the papers were written by conference participants. The conference was the fourth in a series of small conferences at Yale on the general theme of “Applications of Functional Limit Theory to Econometrics and Statistics.”

This paper considers a nonparametric conditional moment test of stability of an econometric model against the alternative of instability. The alternative hypothesis allows for more than one structural change, although in this case it has to be fairly smooth. This complements existing results for stability in a parametric setting. Also, it is shown that the test is always consistent, unlike the available “parametric” tests, which normally rely on the assumption of a correct specification of the model, at least under the null hypothesis of no structural instability. Moreover, we show that the test has local power comparable to the parametric ones; that is, its asymptotic efficiency is greater than zero. A Monte Carlo experiment about the performance of our test is described.

In this article, we investigate a bias in an asymptotic expansion of the simulated maximum likelihood estimator introduced by Lerman and Manski (pp. 305–319 in C. Manski and D. McFadden (eds.), Structural Analysis of Discrete Data with Econometric Applications, Cambridge: MIT Press, 1981) for the estimation of discrete choice models. This bias occurs due to the nonlinearity of the derivatives of the log likelihood function and the statistically independent simulation errors of the choice probabilities across observations. This bias can be the dominating bias in an asymptotic expansion of the simulated maximum likelihood estimator when the number of simulated random variables per observation does not increase at least as fast as the sample size. The properly normalized simulated maximum likelihood estimator even has an asymptotic bias in its limiting distribution if the number of simulated random variables increases only as fast as the square root of the sample size. A bias-adjustment is introduced that can reduce the bias. Some Monte Carlo experiments have demonstrated the usefulness of the bias-adjustment procedure.

The primary purpose of this paper is to review a very few results on some basic elements of large sample theory in a restricted structural framework, as described in detail in the recent book by LeCam and Yang (1990, Asymptotics in Statistics: Some Basic Concepts. New York: Springer), and to illustrate how the asymptotic inference problems associated with a wide variety of time series regression models fit into such a structural framework. The models illustrated include many linear time series models, including cointegrated models and autoregressive models with unit roots that are of wide current interest. The general treatment also includes nonlinear models, including what have become known as ARCH models. The possibility of replacing the density of the error variables of such models by an estimate of it (adaptive estimation) based on the observations is also considered.

Under the framework in which the asymptotic problems are treated, only the approximating structure of the likelihood ratios of the observations, together with auxiliary estimates of the parameters, will be required. Such approximating structures are available under quite general assumptions, such as that the Fisher information of the common density of the error variables is finite and nonsingular, and the more specific assumptions, such as Gaussianity, are not required. In addition, the construction and the form of inference procedures will not involve any additional complications in the non-Gaussian situations because the approximating quadratic structure actually will reduce the problems to the situations similar to those involved in the Gaussian cases.

In this paper a new class of tests for parameter stability, the moving-estimates (ME) test, is proposed. It is shown that in the standard situation the ME test asymptotically equivalent to the maximal likelihood ratio test under the alternative of a temporary parameter shift. It is also shown that the asymptotic null distribution of the ME test is determined by the increments of a vector Brownian bridge and that under a broad class of alternatives the ME test is consistent and has nontrivial local power in general. Our simulations also demonstrate that the proposed test has power superior to other competing tests when parameters are temporarily instable.

This paper discusses inference for I(2) variables in a VAR model. The estimation procedure suggested consists of two reduced rank regressions. The asymptotic distribution of the proposed estimators of the cointegrating coefficients is mixed Gaussian, which implies that asymptotic inference can be conducted using the χ2 distribution. It is shown to what extent inference on the cointegration ranks can be conducted using the tables already prepared for the analysis of cointegration of I(1) variables. New tables are needed for the test statistics to control the size of the tests. This paper contains a multivariate test for the existence of I(2) variables. This test is illustrated using a data set consisting of U.K. and foreign prices and interest rates as well as the exchange rate.

The aim of this paper is the study of the path solutions of a multivariate rational expectations model. We describe several procedures for solving such dynamic systems based on either the adjoint operator method or the Smith form. As a by-product, we derive the dimension of the set of solutions in terms of martingale differences and the dimension of the set of linear stationary solutions when we restrict ourselves to the linear case. These dimensions are functions of the number of equations in the system, of the maximum lead, and of the orders of some eigenvalues of the characteristic equation associated with the system.

This paper is devoted to a detailed examination of the exact sampling properties of the instrumental variables (IV) estimator of the vector of coefficients on the exogenous variables in a single structural equation. The first two moments of a linear combination of the elements of this estimator and the joint distribution of these elements are considered. Estimable bounds for the first moment that can readily be incorporated into any IV estimation package are provided. The results obtained are in terms of the same special functions as those that characterize other results for this model, allowing a unified treatment of the model.

Problems with the asymptotic theory of nonlinear maximum likelihood estimation in integrated and cointegrated systems are discussed in this paper. One problem is that standard proofs of consistency generally do not apply; another one is that, even if the consistency has been established, it can be difficult to deduce the limiting distribution of a maximum likelihood estimator from a conventional Taylor series expansion of the score vector. It is argued in this paper that the latter difficulty can generally be resolved if, in addition to consistency, an appropriate result of the order of consistency of the long-run parameter estimator of the model is available and the standardized sample information matrix satisfies a suitable extension of previous stochastic equicontinuity conditions. To make this idea applicable in particular cases, extensions of the author's recent stochastic equicontinuity results, relevant for many integrated and cointegrated systems with nonlinearities in parameters, are provided. As an illustration, a simple regression model with integrated and stationary regressors and nonlinearities in parameters is considered. In this model, the consistency and order of consistency of the long-run parameter estimator are obtained by employing extensions of well-known sufficient conditions for consistency. These conditions are applicable quite generally, and their verification in the special case of this paper suggests how to proceed in more complex models.

In this article it is shown how the cointegration or joint trending behavior of economic time series helps to alleviate the errors in variables problem.

We consider the estimation and identification of the functional structures of nonlinear econometric systems of the ARCH type. We employ nonparametric kernel estimates for the nonlinear functions characterizing the systems, and we establish strong consistency along with sharp rates of convergence under mild regularity conditions. We also prove the asymptotic normality of the estimates.

The unknown structural parameters of a continuous/discrete state space model are estimated by maximum likelihood in the presence of irregular sampling, missing values, and cross-sections of time series (panel data). Exogenous (control) variables are included, and the sampling scheme and missing data pattern can be different for each variable and system. Furthermore, the derived non-linear optimization algorithm with analytical score function can be used for the discrete time case as well.

This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed that allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities, and they belong to the same family as the fully modified least-squares (FM-OLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57,99–125). Specific attention is given to fully modified least absolute deviation (FM-LAD) estimation and fully modified M (FM-M) estimation. The criterion function for LAD and some M-estimators is not always smooth, and this paper develops generalized function methods to cope with this difficulty in the asymptotics. The results given here include a strong law of large numbers and some weak convergence theory for partial sums of generalized functions of random variables. The limit distribution theory for FM-LAD and FM-M estimators that is developed includes the case of finite variance errors and the case of heavytailed (infinite variance) errors. Some simulations and a brief empirical illustration are reported.

This paper examines the exact sampling behavior of a family of instrumental variables estimators of the coefficients in a single structural equation when the model has been misspecified by the incorrect inclusion or exclusion of variables. It is found that such specification errors can have implications for the structure of the exact results obtained. A brief numerical examination of the analytical results is also provided.

It was recently shown (Abadir, 1993b) that nonstationarity causes the limiting distributions of the Wald (W) and Lagrange multiplier (LM) statistics to become different from each other. This paper demonstrates that such a divergence between the two distributions can be used as an indicator of the presence of a unit root. A test based on this idea is devised by modifying the normalized autocorrelation coefficient (NAC). It is then shown to be an improvement on NAC in large samples and an improvement on other existing tests in large effective samples. The paper also investigates the effect of nonstationarity on the well-known inequality W ≥ LR ≥ LM.

Conditional moment tests check to see whether or not population moment equalities, implied by the null model specification, hold approximately in the sample. Asymptotically valid conditional statistics can easily be calculated from the output of a so-called outer product of the gradient (OPG) artificial regression. However, several studies have now found that this OPG variant exhibits extremely poor finite sample behavior and that significant improvements can be made by employing the efficient variant. In the light of such evidence, this paper develops new artificial regressions that can be used to calculate the efficient variant of the test statistic. These artificial regressions can also serve several other purposes, including the construction of Hausmantype tests of parameter estimator consistency.

A quasi-maximum likelihood estimator of the break date is analyzed. Consistency of the estimator is demonstrated under very general conditions, provided that the data-generating process is not integrated. However, the asymptotic distribution of the estimator is quite different for time series that are integrated of order one. In that case, when there is no break, the analyst can be spuriously led to the estimation of a break near the middle of the time series.