Determining the Number of Regimes in Markov Switching VAR and VMA Models
Determining the Number of Regimes in Markov Switching VAR and VMA Models

Author: Maddalena Cavicchioli

Publisher:

Published: 2013

Total Pages: 28

ISBN-13:

DOWNLOAD EBOOK

We give stable finite order VARMA(p*; q*) representations for M-state Markov switching second-order stationary time series whose autocovariances satisfy a certain matrix relation. The upper bounds for p* and q* are elementary functions of the dimension K of the process, the number M of regimes, the autoregressive and moving average orders of the initial model. If there is no cancellation, the bounds become equalities, and this solves the identification problem. Our class of time series include every M-state Markov switching multivariate moving average models and autoregressive models in which the regime variable is uncorrelated with the observable. Our results include, as particular cases, those obtained by Krolzig (1997), and improve the bounds given by Zhang and Stine (2001) and Francq and Zakoian (2001) for our classes of dynamic models. Data simulations and an application on foreign exchange rates complete the paper.

Markov-Switching Vector Autoregressions
Markov-Switching Vector Autoregressions

Author: Hans-Martin Krolzig

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 369

ISBN-13: 364251684X

DOWNLOAD EBOOK

This book contributes to re cent developments on the statistical analysis of multiple time series in the presence of regime shifts. Markov-switching models have become popular for modelling non-linearities and regime shifts, mainly, in univariate eco nomic time series. This study is intended to provide a systematic and operational ap proach to the econometric modelling of dynamic systems subject to shifts in regime, based on the Markov-switching vector autoregressive model. The study presents a comprehensive analysis of the theoretical properties of Markov-switching vector autoregressive processes and the related statistical methods. The statistical concepts are illustrated with applications to empirical business cyde research. This monograph is a revised version of my dissertation which has been accepted by the Economics Department of the Humboldt-University of Berlin in 1996. It con sists mainly of unpublished material which has been presented during the last years at conferences and in seminars. The major parts of this study were written while I was supported by the Deutsche Forschungsgemeinschajt (DFG), Berliner Graduier tenkolleg Angewandte Mikroökonomik and Sondeiforschungsbereich 373 at the Free University and Humboldt-University of Berlin. Work was finally completed in the project The Econometrics of Macroeconomic Forecasting founded by the Economic and Social Research Council (ESRC) at the Institute of Economies and Statistics, University of Oxford. It is a pleasure to record my thanks to these institutions for their support of my research embodied in this study.

Regime-Dependent Impulse Response Functions in a Markov-Switching Vector Autoregression Model
Regime-Dependent Impulse Response Functions in a Markov-Switching Vector Autoregression Model

Author: Michael Ehrmann

Publisher:

Published: 2005

Total Pages: 0

ISBN-13:

DOWNLOAD EBOOK

In this paper we introduce identifying restrictions into a Markov-switching vector autoregression model. We define a separate set of impulse responses for each Markov regime to show how fundamental disturbances affect the variables in the model dependent on the regime. We go to illustrate the use of these regime-dependent impulse response functions in a model of the U.S. economy. The regimes we identify come close to the "old" and "new economy" regimes found in recent research. We provide evidence that oil price shocks are much less contractionary and inflationary than they used to be. We show furthermore that the decoupling of the US economic performance from oil price shocks cannot be explained by "good luck" alone, but that structural changes within the US economy have taken place.

Causal Inference in Econometrics
Causal Inference in Econometrics

Author: Van-Nam Huynh

Publisher: Springer

Published: 2015-12-28

Total Pages: 626

ISBN-13: 3319272845

DOWNLOAD EBOOK

This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

Structural Vector Autoregressions with Markov Switching
Structural Vector Autoregressions with Markov Switching

Author: Helmut Herwartz

Publisher:

Published: 2011

Total Pages: 37

ISBN-13:

DOWNLOAD EBOOK

In structural vector autoregressive (SVAR) analysis a Markov regime switching (MS) property can be exploited to identify shocks if the reduced form error covariance matrix varies across regimes. Unfortunately, these shocks may not have a meaningful structural economic interpretation. It is discussed how statistical and conventional identifying information can be combined. The discussion is based on a VAR model for the US containing oil prices, output, consumer prices and a shortterm interest rate. The system has been used for studying the causes of the early millennium economic slowdown based on traditional identication with zero and long-run restrictions and using sign restrictions. We find that previously drawn conclusions are questionable in our framework.

Joint Determination of the State Dimension and Autoregressive Order for Models With Markov Regime Switching
Joint Determination of the State Dimension and Autoregressive Order for Models With Markov Regime Switching

Author: Zacharias Psaradakis

Publisher:

Published: 2007

Total Pages: 0

ISBN-13:

DOWNLOAD EBOOK

This paper is concerned with the problem of joint determination of the state dimension and autoregressive order of models with Markov-switching parameters. A model selection procedure is proposed which is based on optimization of complexity-penalized likelihood criteria. The efficacy of the procedure is evaluated by means of Monte Carlo experiments.

Autoregressive Moving Average Infinite Hidden Markov-Switching Models
Autoregressive Moving Average Infinite Hidden Markov-Switching Models

Author: Luc Bauwens

Publisher:

Published: 2017

Total Pages: 47

ISBN-13:

DOWNLOAD EBOOK

Markov-switching models are usually specified under the assumption that all the parameters change when a regime switch occurs. Relaxing this hypothesis and being able to detect which parameters evolve over time is relevant for interpreting the changes in the dynamics of the series, for specifying models parsimoniously, and may be helpful in forecasting. We propose the class of sticky infinite hidden Markov-switching autoregressive moving average models, in which we disentangle the break dynamics of the mean and the variance parameters. In this class, the number of regimes is possibly infinite and is determined when estimating the model, thus avoiding the need to set this number by a model choice criterion. We develop a new Markov chain Monte Carlo estimation method that solves the path dependence issue due to the moving average component. Empirical results on macroeconomic series illustrate that the proposed class of models dominates the model with fixed parameters in terms of point and density forecasts.Appendix available at: 'https://ssrn.com/abstract=2965668' https://ssrn.com/abstract=2965668.

Markov-switching Mixed-frequency VAR Models
Markov-switching Mixed-frequency VAR Models

Author: Claudia Foroni

Publisher:

Published: 2014

Total Pages: 41

ISBN-13:

DOWNLOAD EBOOK

This paper introduces regime switching parameters in the Mixed-Frequency VAR model. We first discuss 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. Finally, the MSMF-VAR model is applied to predict GDP growth and business cycle turning points in the euro area. Its performance is compared with that of a number of competing models, including linear and regime switching mixed data sampling (MIDAS) models. The results suggest that MSMF-VAR models are particularly useful to estimate the status of economic activity.