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Sparse Change-Point VAR models
SSRN Electronic Journal, 2019AbstractChange‐point (CP) VAR models face a dimensionality curse due to the proliferation of parameters that arises when new breaks are detected. We introduce the Sparse CP‐VAR model which determines which parameters truly vary when a break is detected.
Dufays, A, Li, Z, Rombouts, JVK, Song, Y
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VAR cointegration in VARMA models [PDF]
, 1999 The method for estimation and testing for cointegration put forward by Johansen assumes that the data are described by a vector autoregressive process. In this article we extend the data generating process to autoregressive moving average models without unit roots in the MA polynomial.
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2006
Abstract The purpose of this chapter is to introduce the non-stationary VAR model and show that the presence of unit roots (i.e. stochastic trends) leads to a reduced rank condition on the long-run matrix .
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Abstract The purpose of this chapter is to introduce the non-stationary VAR model and show that the presence of unit roots (i.e. stochastic trends) leads to a reduced rank condition on the long-run matrix .
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2016
The discussion of forecasting with VAR models proceeds in two steps. First, we assume that the parameters of the model are known. Although this assumption is unrealistic, it will nevertheless allow us to introduce and analyze important concepts and ideas.
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The discussion of forecasting with VAR models proceeds in two steps. First, we assume that the parameters of the model are known. Although this assumption is unrealistic, it will nevertheless allow us to introduce and analyze important concepts and ideas.
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Markov-switching mixed-frequency VAR models
International Journal of Forecasting, 2015Abstract This paper introduces regime switching parameters to the Mixed-Frequency VAR model. We begin by discussing estimation and inference for Markov-switching Mixed-Frequency VAR (MSMF-VAR) models. Next, we assess the finite sample performance of the technique in Monte-Carlo experiments.
Foroni, Claudia +2 more
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A spectral decomposition for structural VAR models
Empirical Economics, 1996Based on structural VARs, this paper proposes a spectral decomposition which allows to infer the effects of changes in one variable on the other variables in the frequency domain. It is shown that there is a close relationship between this concept and conventional forecast error variance decomposition techniques for VARs.
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VaR Modelling on Long Run Horizons
Automation and Remote Control, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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1995
In the minds of some economic theorists and traditional econometricians, the vector autoregressive (VAR) approach to time-series data is unscientific, obscure, confusing, or simply wrong. Since the publication of Sims’s original contributions (1972, 1980a, 1980b, 1982), the methodology has spurred endless debates. Critics claim that the methodology has
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In the minds of some economic theorists and traditional econometricians, the vector autoregressive (VAR) approach to time-series data is unscientific, obscure, confusing, or simply wrong. Since the publication of Sims’s original contributions (1972, 1980a, 1980b, 1982), the methodology has spurred endless debates. Critics claim that the methodology has
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Model Reduction in Cointegrated VAR Models
2004Cointegrated vector autoregressive models have become a standard modeling tool in applied econometric time series analysis during the last decade. Therefore, in this chapter we explore the possibilities to extend the model selection strategies suggested in Chapter 2 to cointegrated VAR models.
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