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In this paper we introduce the general setting of a multivariate time series autoregressive model with stochastic time-varying coefficients and time-varying conditional variance of the error process. This allows modeling VAR dynamics for non-stationary times series and estimation of time varying...
Persistent link: https://www.econbiz.de/10011405250
Following Giraitis, Kapetanios, and Yates (2014b), this paper uses kernel methods to estimate a seven variable time-varying (TV) vector autoregressive (VAR) model on the data set constructed by Smets and Wouters (2007). We apply an indirect inference method to map from this TV VAR to time...
Persistent link: https://www.econbiz.de/10011405253
This paper evaluates the VaR forecasting performance of the Markov regime switching (MRS) based volatility models, allowing for EGARCH effects. As is argued in the literature, this extension of the MRS model model may improve its forecasting performance due to its ability to capture leverage...
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The online Supplement presents the proof the auxiliary Lemmas 1-6, the entire set of tables with results from the Monte Carlo and the empirical studies, and further discussion on selected topics.Full paper is available at: 'https://ssrn.com/abstract=2707176' https://ssrn.com/abstract=2707176
Persistent link: https://www.econbiz.de/10012968328
We address the issue of modelling and forecasting macroeconomic variables using rich datasets by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares...
Persistent link: https://www.econbiz.de/10012970411