Common trends models provide a useful tool for studying growth and business cycle phenomena in a joint framework. In this paper we study the problem of how to estimate and analyse a common stochastic trends model for an n dimensional time series which is cointegrated of order (1,1) with r<n cointegration vectors. Identification of k=n-r permanent (trend) and r transitory innovations is discussed in terms of impulse responses and variance decompositions. Finally, we derive analytical expressions of the asymptotic distributions for estimates of these functions, thereby making formal hypothesis testing and inference possible within this framework.

KEYWORDS: Cointegration, common trends, impulse response function, permanent and transitory shocks, variance decomposition.
JEL CLASSIFICATION NUMBERS: C32, C51.

We study the Danish unemployment experience 1905-92 using a common trends model with cointegration constraints. To justify the identifying assumptions about the cointegration vectors and the common trends we present a simple macroeconomic model of the labor market. The model determines the long run behavior of labor productivity, employment, unemployment, real product and real consumer wages. The empirical results give support for three cointegration relations and two common trends. Based on the economic model the trends are interpreted as representing labor productivity (technology) and labor supply. With unemployment being nonstationary, the common trends analysis indicates that labor supply shocks is the primary source for explaining the behavior of unemployment.

KEYWORDS: Cointegration, common trends, supply and demand shocks, unemployment.
JEL CLASSIFICATION NUMBERS: C15, C32, C52, E24.

This paper analyses three Granger noncausality hypotheses within a conditionally Gaussian MS-VAR model. Noncausality in mean is based on Granger's original concept for linear predictors by defining noncausality from the 1-step ahead forecast error variance for the conditional expectation. Noncausality in mean-variance concerns the conditional forecast error variance, while noncausality in distribution refers to the conditional distribution of the forecast errors. Necessary and sufficient parametric conditions for noncausality are presented for all noncausality hypotheses. As an illustration, the hypotheses are tested using monthly postwar U.S. data on money and income.

KEYWORDS: Granger causality, Markov process, nonlinear time series, regime switching, vector autoregression.
JEL CLASSIFICATION NUMBERS: C32.

The interest in empirical monetary policy research has increased in the last decade, possibly because deregulation and explicit monetary targets have made monetary policy issues more interesting. In particular, within the inflation targeting framework it has been argued that inflation forecasts can be used as optimal intermediate targets for monetary policy, and the development of empirical models that have good forecasting properties is therefore important. This paper shows that a VAR model with long run restrictions, justified by economic theory, is useful for both forecasting inflation and for analyzing other issues that are central to the conduct of monetary policy.

KEYWORDS: Cointegration, common stochastic trends, inflation targeting, monetary policy, vector autoregressions.
JEL CLASSIFICATION NUMBERS: C32, C52, C53, E31, E52.

We report evidence that the relation between the financial sector share, private savings and growth in the United States 1948-1996 is characterized by several regime shifts. The finding is based on vector autoregressions on quarterly data that allow for Markov switching regimes. The evidence may be interpreted as support for a hypothesis that the relation between financial development and growth evolves in a stepwise fashion. Theoretical models where financial market extensions entail fixed costs imply such stepwise patterns. The estimated variable relations are roughly consistent with the patterns to be expected from such models, although our data do not admit definite conclusions. The timing of the shifts coincide with changes in regulation and in the financial market structure.

KEYWORDS: Financial markets extensions, growth, Markov switching, saving, vector autoregression.
JEL CLASSIFICATION NUMBERS: C32, E44, O16, O51.

In this paper we study 2-state Markov switching VAR models of monthly unemployment and inflation for three countries: Sweden, United Kingdom, and the United States. The primary purpose is to examine if periods of low inflation are associated with high or low unemployment volatility. We find that MS-VAR models seem to provide a better description of the data than single regime VARs and need fewer lags to account for serial correlation. To interpret the regimes the empirical results are compared with the predictions from a version of Rogoff's (1985) model of monetary policy. We find that both the theoretical and the empirical results suggest that an increase in central bank "conservativeness" can be associated with either a higher or a lower variance in unemployment. In the U.S. case we find that the variance of unemployment is lower in the low inflation regime than in the high inflation regime, while the Swedish and the U.K. cases suggest that unemployment variability is higher in the low inflation regime.

KEYWORDS: Cointegration, monetary policy, Phillips curve, regime switching.
JEL CLASSIFICATION NUMBERS: C32, E31, E52.