Abstract not found

Exchange for providing the requisite data, and Lawrence Glosten and an anonymous reviewer for their extensive and insightful comments and suggestions. This paper is dedicated to Teppo Martikainen who contributed substantially to earlier versions of this paper but did not live to witness its completion.

Pure time series-based tests fail to nd empirical support for monetary exchange rate models. In this paper we apply pooled time series estimation on a forwardlooking monetary model, resulting in parameter estimates which are in compliance with the underlying theory. Based on a panel version of the Engle and Granger (1987) two-step procedure we nd that the residuals of the panel-based estimated monetary model are stationary. This indicates that on a pooled time series level there is cointegration between the exchange rate and the macroeconomic fundamentals of this monetary model. Keywords: monetary exchange rate models, nominal exchange rates, cointegration, panel data. JEL classication: C23, F30, G15. This paper has beneted from helpful comments by Charles Engel (the co-editor), two anonymous referees, Frank de Jong, Richard Paap, and seminar participants at the Erasmus University Rotterdam, the NAKE annual meeting in Amsterdam, the 1998 ESRC Econometric Study Group c...

Wald tests of restrictions on the coefficients of vector autoregressive (VAR) processes are known to have nonstandard asymptotic properties for I(1) and cointegrated systems of variables. A simple device is proposed which guarantees that Wald tests have asymptotic Ø 2 --distributions under general conditions. If the true generation process is a VAR(p) it is proposed to fit a VAR(p+1) to the data and perform a Wald test on the coefficients of the first p lags only. The power properties of the modified tests are studied both analytically and numerically by means of simple illustrative examples. (*) The research for this paper was supported by funds from Sonderforschungsbereich 373 at Humboldt Universitt Berlin. We are indebted to R. Mestre for invaluable research assistance. We have also benefited from comments by Peter C.B. Phillips, Enrique Sentana and participants at the European Meeting of the Econometric Society, Maastricht 1994. 1 Introduction Wald tests are standard tools fo...

Cointegration occurs when the long run multiplier of a vector autoregressive model exhibits rank reduction. Priors and posteriors of the parameters of the cointegration model are therefore proportional to priors and posteriors of the long run multiplier given that it has reduced rank. Rank reduction of the long run multiplier is modelled using a decomposition resulting from its singular value decomposition. It specifies the long run multiplier matrix as the sum of a matrix that equals the product of the adjustment parameters and the cointegrating vectors, i.e. the cointegration specification, and a matrix that models the deviation from cointegration. Priors and posteriors for the parameters of the cointegration model are obtained by restricting the latter matrix to zero in the prior and posterior of the unrestricted long run multiplier. The special decomposition of the long run multiplier results in unique posterior densities. This theory leads to a complete Bayesian framework for cointegration analysis. It includes prior specification, simulation schemes for obtaining posterior distributions and determination of the cointegration rank via Bayes factors. We illustrate the analysis with several simulated series, the UK data

this paper we focus on the first approach with reference to first order cointegrated systems; we distinguish two different solution both based on the same reduced form model, namely the error correction model. The starting point is a famous result by Stock and Watson (1988), who extended the univariate Beveridge-Nelson decomposition showing that cointegrated systems can be represented in terms of a reduced number of common stochastic trends, whose data generating process is the random walk, plus transitory or stationary components. King et al. (1991) pursued further the issue carrying out an exercise

Following Bierens (1997a,b) and Vogelsang (1998a,b), unit root tests can be constructed which are asymptotically invariant to parameters involved by the short run dynamics of the process. Such an approach is called nonparametric by Bierens (1997b) and can be used to test a wide range of nonlinear models and is robust against structural changes in the short-run dynamics. A variance ratio statistic is suggested which is similar to the test statistic suggested by Kwiatkowski et al. (1992) but assumes nonstationarity under the null hypothesis. A straightforward generalization of the variance ratio statistic is suggested, which can be used to test the cointegration rank in the spirit of Johansen (1988).

This paper suggests a new methodological approach to the analysis of cointegrated linear systems subject to changes in regime. We consider cointegrated vector autoregressive processes where Markovian shifts occur in the equilibrium mean and the drift of the system. A two-stage maximum likelihood estimation technique is proposed. In the first stage, based on a finite order VAR approximation of the cointegrated VARMA representation, the Johansen cointegration analysis is invoked to determine the cointegration rank and to estimate the cointegration matrix. An EM algorithm delivers the maximum likelihood estimates of the remaining parameters. The methodology is illustrated with an investigation of international and global business cycles. Keywords: Cointegration; Markov switching; Structural Breaks; Cobreaking. JEL Classification: C50. C32, F47. Most of this research was carried out while the author was visiting the Sonderforschungsbereich 373. Financial support from the Deutsche Forsch...

In this paper we outline conditions under which the Diebold and Mariano (DM: 1995) test for predictive ability can be extended to the case of two forecasting models, each of which may include cointegrating relations, when allowing for parameter estimation error. We show that in the cases where either the loss function is quadratic or the length of the prediction period, P, grows at a slower rate than the length of the regression period, R, the standard DM test can be used. On the other hand, in the case of a generic loss function, if P R ! as T ! 1, 0 < < 1, then the asymptotic normality result of West (1996) no longer holds. We also extend the "data snooping" technique of White (2000) for comparing the predictive ability of multiple forecasting models to the case of cointegrated variables. In a series of Monte Carlo experiments, we examine the impact of both short run and cointegrating vector parameter estimation error on DM, data snooping, and related tests. Our results sugge...

We propose a generalization of existing business cycle models which allows us to decompose recessions into permanent and transitory components. We find that the transitory component of recessions accounts for between 77% and 96% of the observed variance of monthly indicator series. Our results suggest the following three phase characterization of the business cycle: recession, high-growth recovery during which output partially reverts to its previous peak, and normal growth following the recovery. In addition, we find significant timing differences between the permanent and transitory components of recessions; most notably the lack of the usual high-growth recovery phase following the 1990-91 recession. JEL Codes: C32, E32 Kim: Department of Economics, Korea University, Seoul, 136-701, Korea (cjkim@kuccnx.korea.ac.kr); Murray: (Corresponding author) Department of Economics, University of Houston, Houston, TX 77204-5882 (cjmurray@uh.edu), Tel: 713-743-3835, Fax: 713-743-3798 1 1. In...