A Bayesian approach is presented for modeling a time series by an autoregressive-moving average model. The treatment is robust to innovation and additive outliers and identiies such outliers. It enforces stationarity on the autoregressive parameters and invertibility on the moving average parameters, and takes account of uncertainty about the correct model by averaging the parameter estimates a...

A closed-loop power control (CLPC) scheme with a multistep (indicating multiple prediction steps) linear autoregressive predictor is presented. The proposed CLPC relies on low-rate sample vector based autoregressive prediction. Compared to currently available predictive CLCP schemes, it demonstrates particularly robust performance in the presence of large loop delays and channel estimation errors.

We are grateful to Bernard Hanzon for helpful comments. The research for this paper was carried out within Sonderforschungsbereich 373 at the Humboldt University Berlin and was printed using funds made available by the Deutsche Forschungsgemeinschaft.

This paper reviews a class of methods to perform causal inference in the framework of a structural vector autoregressive model. We consider three different settings. In the first setting the underlying system is linear with normal disturbances and the structural model is identified by exploiting the information incorporated in the partial correlations of the estimated residuals. Zero partial co...

Vector autoregressive (VAR) modelling is one of the most popular approaches in multivariate time series analysis. The parameters interpretation is simple, and provide an intuitive identification of relationships and Granger causality among time series. However, the VAR modelling requires stationarity conditions which could not be valid in many practical applications. Locally stationary or time ...

In this paper we introduce a bootstrap procedure to test parameter restrictions in vector autoregressive models which is robust in cases of conditionally heteroskedastic error terms. The adopted wild bootstrap method does not require any parametric specification of the volatility process and takes contemporaneous error correlation implicitly into account. Via a Monte Carlo investigation empiric...

We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I(2) conditions (Johansen, 1996) is proved and polynomial cointegration discussed in the general setup.

This paper generalizes the cointegrated vector autoregressive model of Johansen (1988) to allow for structural changes. Estimation under various hypotheses is made possible by a new estimation technique, that makes it simple to derive a number of interesting likelihood ratio tests. E.g., the test for m structural changes against m+ k structural changes (occurring at fixed points in time), m ∈ N...

Level vector autoregressive (VAR) models are used extensively in empirical macroeconomic research. However, estimated level VAR models may contain explosive roots, which is at odds with the widespread consensus among macroeconomists that roots are at most unity. This paper investigates the frequency of explosive roots in estimated level VAR models in the presence of stationary and nonstationary...

Seasonal and periodic vector autoregressions are two common approaches to modeling vector time series exhibiting cyclical variations. The total number of parameters in these models increases rapidly with the dimension and order of the model, making it difficult to interpret the model and questioning the stability of the parameter estimates. To address these and other issues, two methodologies f...