86 research outputs found
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
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
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
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.Serial correlation common features; codependence; cointegration; overnight interest rates; central banks
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
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