This supplementary appendix contains proofs of some results contained in the paper. Specifically, Section S1 provides proofs of Theorem 4 and its corollary, concerning the asymptotic distribution of flexible random effects estimators. Section S1 also proves Theorem 5, its corollary, and Theorem 6 concerning the bias and the asymptotic distribution of estimated marginal effects. Section S2 prove...

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.

The CMA (Common Monetary Area) is a quadrilateral monetary arrangement encompassing South Africa, Namibia, Lesotho, and Eswatini. four countries have undergone gradual improvement in regional economic integration for the effective coordination of their policymaking. Despite coordination, are still experiencing poor performance. This study traces how shock or an unanticipated change anchor count...

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.

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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 ...

The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singular multivariate beta distributions allowing a fully conjugate Bayesian infere...