The Power of Single Equation Tests for Cointegration when the Cointegrating Vector is Prespecified.

In this paper I present an alternative derivation of the asymptotic distribution of Kremers, Ericsson and Dolado's (1992) conditional ECM- based t-test for no-cointegration with a single prespecified cointegrating vector. This alternative distribution, which is identical to the distribution of Hansen's (1995) covariate augmented t-test for a unit root, is valid for weakly exogenous regressors and depends on a consistently estimable nuisance parameter that takes on values in the unit interval. I show analytically, using asymptotic power functions based on near cointegrated alternatives, that the ECM t-test with a prespecified cointegrating vector can have much higher power than single equation tests for cointegration based on estimating the cointegrating vector. I also characterize situations in which the ECM t-test computed with a misspecified cointegrating vector will have high power.cointegration, common factor, error correction model, local power, misspecification, near-cointegration, strong exogeneity, weak exogeneity.

Practical Issues in the Analysis of Univariate GARCH Models

This paper gives a tour through the empirical analysis of univariate GARCH models for financial time series with stops along the way to discuss various practical issues associated with model specification, estimation, diagnostic evaluation and forecasting.

Cointegration and Forward and Spot Exchange Rate Regressions

In this paper we investigate in detail the relationship between models of cointegration between the current spot exchange rate, st, and the current forward rate, ft, and models of cointegration between the future spot rate, st+1, and ft and the implications of this relationship for tests of the forward rate unbiasedness hypothesis (FRUH). We argue that simple models of cointegration between st and ft more easily capture the stylized facts of typical exchange rate data than simple models of cointegration between st+1 and ft and so serve as a natural starting point for the analysis of exchange rate behavior. We show that simple models of cointegration between st and ft imply rather complicated models of cointegration between st+1 and ft. As a result, standard methods are often not appropriate for modeling the cointegrated behavior of (st+1, ft)' and we show that the use of such methods can lead to erroneous inferences regarding the FRUH.cointegration, exchange rates, forward rate unbiasedness, weak exogeneity

State Space Modeling Using SsfPack in S+FinMetrics 3.0

This paper presents two illustrations of state space modeling in S-PLUS using the SsfPack 3.0 routines implemented in S+FinMetrics 3.0. The state space modeling functions in S+FinMetrics/SsfPack are extremely flexible and powerful and can be used for a wide variety of linear Gaussian state space models and for some non-linear and non-Gaussian state space models.

Indirect Inference Based on the Score

The Ecient Method of Moments (EMM) estimator popularized by Gallant and Tauchen (1996) is an indirect inference estimator based on the simulated auxiliary score evaluated at the sample estimate of the auxiliary parameters. We study an alternative estimator that uses the sample auxiliary score evaluated at the simulated binding function which maps the structural parameters of interest to the auxiliary parameters. We show that the alternative estimator has the same asymptotic properties as the EMM estimator but in finite samples behaves more like the distance-based indirect inference estimator of Gourieroux, Monfort and Renault (1993).

A new method of projection-based inference in GMM with weakly identified nuisance parameters

Projection-based methods of inference on subsets of parameters are useful for obtaining tests that do not over-reject the true parameter values. However, they are also often criticized for being conservative. We show that the usual method of pro jection can be modifed to obtain tests that are as powerful as the conventional tests for subsets of parameters. Like the usual projection-based methods, one can always put an upper bound to the rate at which the new method over-rejects the true value of the parameters of interest. The new method is described in the context of GMM with possibly weakly identifed parameters.

Implications of Two Measures of Persistence for Correlation Between Permanent and Transitory Shocks in U.S. Real GDP

Conventionally, shocks to permanent and transitory components in the unobserved components (UC) model for the log of real GDP are assumed to be uncorrelated. This assumption is mainly for identification of model parameters. In this paper, we show important implications of two popular measures of persistence for the correlation between permanent and transitory shocks in the UC model, and demonstrate that the correlation is negative for the log of U.S. real GDP under a very general specification of the cycle process.

Indirect Inference Based on the Score

The Efficient Method of Moments (EMM) estimator popularized by Gallant and Tauchen (1996) is an indirect inference estimator based on the simulated auxiliary score evaluated at the sample estimate of the auxiliary parameters. We study an alternative estimator that uses the sample auxiliary score evaluated at the simulated binding function which maps the structural parameters of interest to the auxiliary parameters. We show that the alternative estimator has the same asymptotic properties as the EMM estimator but in finite samples behaves more like the distance-based indirect inference estimator of Gourieroux, Monfort and Renault (1993).simulation based estimation, indirect inference, efficient method of moments

A Time Series Model of Multiple Structural changes in Level, Trend and Variance

We consider a deterministically trending dynamic time series model in which multiple changes in level, trend and error variance are modeled explicitly and the number but not the timing of the changes are known. Estimation of the model is made possible by the use of the Gibbs sampler. The determination of the number of structural breaks and the form of structural change is considered as a problem of model selection and we compare the use of marginal likelihoods, posterior odds ratios and Schwarz' BIC model selection criterion to select the most appropriate model from the data. We evaluate the efficacy of the Bayesian approach using a small Monte Carlo experiment. As empirical examples, we investigate structural changes in the U.S. ex-post real interest rate and in a long time series of U.S. GDP.BIC, Gibbs sampling, multiple structural changes, posterior odds ratio