Determining p-values for Systems Cointegration Tests With a Prior Adjustment for Deterministic Terms

In this paper I present a procedure to approximate the asymptotic distributions of systems cointegration tests with a prior adjustment for deterministic terms suggested by Lütkepohl, Saikkonen & Trenkler (2004), Saikkonen & Lütkepohl (2000a, 2000b, 2000c), and Saikkonen & Luukkonen (1997). The asymptotic distributions are approximated by the Gamma distribution and the parameters necessary to fit the Gamma distributions are obtained from response surfaces which I describe in this paper. The approximation can be easily used to derive arbitrary p-values or percentiles. --p-values,systems cointegration tests,response surface

Codependence and Cointegration

We introduce the idea of common serial correlation features among non-stationary, cointegrated variables. That is, the time series do not only trend together in the long run, but adjustment restores equilibrium immediately in the period following a deviation. Allowing for delayed re-equilibration, we extend the framework to codependence. The restrictions derived for VECMs exhibiting the common feature are checked by LR and GMM-type tests. Alongside, we provide corrected maximum codependence orders and discuss identification. The concept is applied to US and European interest rate data, examining the capability of the Fed and ECB to control overnight money market rates

Testing for Codependence of Non-Stationary Variables

We analyze non-stationary time series that do not only trend together in the long run, but restore the equilibrium immediately in the period following a deviation. While this represents a common serial correlation feature, the framework is extended to codependence, allowing for delayed adjustment. We show which restrictions are implied for VECMs and lay out a likelihood ratio test. In addition, due to identification problems in codependent VECMs a GMM test approach is proposed. We apply the concept to US and European interest rate data, examining the capability of the Fed and ECB to control overnight money market rates

Identifying the Shocks behind Business Cycle Asynchrony in Euroland

This paper investigates which shocks drive asynchrony of business cycles in the euro area. Thereby, it unites two strands of literature, those on common features and on structural VAR analysis. In particular, we show that the presence of a common cycle implies collinearity of structural impulse responses. Several Wald tests are applied to the latter hypothesis. Results reveal that differences in the GDP dynamics in several peripheral countries compared to a euro zone core are triggered by idiosyncratic, and to a lesser extent also world, shocks. Additionally, real shocks prove relevant rather than nominal ones

Cointegrated VARMA models and forecasting US interest rates

We bring together some recent advances in the literature on vector autoregressive moving-average models creating a relatively simple specification and estimation strategy for the cointegrated case. We show that in the cointegrated case with fixed initial values there exists a so-called final moving representation which is usually simpler but not as parsimonious than the usual Echelon form. Furthermore, we proof that our specification strategy is consistent also in the case of cointegrated series. In order to show the potential usefulness of the method, we apply it to US interest rates and find that it generates forecasts superior to methods which do not allow for moving-average terms.Cointegration, VARMA models, forecasting

Codependence and Cointegration

We introduce the idea of common serial correlation features among non-stationary, cointegrated variables. That is, the time series do not only trend together in the long run, but adjustment restores equilibrium immediately in the period following a deviation. Allowing for delayed re-equilibration, we extend the framework to codependence. The restrictions derived for VECMs exhibiting the common feature are checked by LR and GMM-type tests. Alongside, we provide corrected maximum codependence orders and discuss identification. The concept is applied to US and European interest rate data, examining the capability of the Fed and ECB to control overnight money market rates.VAR; serial correlation common features; codependence; cointegration

On the Identification of Codependent VAR and VEC Models

In this paper we discuss identification of codependent VAR and VEC models. Codependence of order q is given if a linear combination of autocorrelated variables eliminates the serial correlation after q lags. Importantly, maximum likelihood estimation and corresponding likelihood ratio testing are only possible if the codependence restrictions can be uniquely imposed. However, our study reveals that codependent VAR and VEC models are not generally identified. Nevertheless, we show that one can guarantee identification in case of serial correlation common features, i.e. when q=0, and for a single vector generating codependence of order q=1.Codependence; identification; VAR; cointegration; serial correlation common features

Are Eastern European Countries Catching Up? Time Series Evidence for Czech Republic, Hungary, and Poland

The catching up process in Czech Republic, Hungary, and Poland is analyzed by investigating the integration properties of log-differences in per-capita GDP versus the EU15 and a Mediterranean country group. We account for structural changes by using unit root tests that allow for two endogenous breaks in the level and the trend. We find that Czech Republic and Hungary are stochastically converging towards the Mediterranean group, while only Czech Republic is stochastically converging towards EU15. Remaining per capita GDP differences are only reduced by deterministic trends. Extrapolating these trends we find that catching up will take about 20 years.Stochastic convergence, Catching up, Unit root tests, EU accession