A Markov Switching Regime Model of the Brazilian Business Cycle (original) (raw)
Related papers
Modeling Business Cycle Fluctuations through Markov Switching VAR:An Application to Iran
2016
IN this paper, the Iranian Business Cycle characteristics were investigated via uni-variate and multivariate Markov-switching specifications. By using Hamilton (1989) and Krolzig (1997) (MS-VAR) models, we examined the stochastic properties of the cyclical pattern of the quarterly Iranian real GDP between 1988:Q2 - 2008:Q3. The empirical analysis consists of mainly three parts. First, two kinds of alternative specifications were tried and we were adopted best specification with respect to various diagnostic statistics. Then, selected models were tested against their linear benchmarks. LR test results imply strong evidence in favor of the nonlinear regime switching behavior. Furthermore, the multivariate specification with various macro aggregates and changing variance parameter outperformed the other MS models with reference to one-step ahead forecasting performance. With this specification, we can detect the three recessionary periods experienced by the Iranian economy between 1988...
Business Fluctuations and Cycles, 2007
Duration dependent Markov-switching VAR (DDMS-VAR) models are time series models with data generating process consisting in a mixture of two VAR processes. The switching between the two VAR processes is governed by a two state Markov chain with transition probabilities that depend on how long the chain has been in a state. In the present paper we analyze the second order properties of such models and propose a Markov chain Monte Carlo algorithm to carry out Bayesian inference on the model’s unknowns. Furthermore, a freeware software written by the author for the analysis of time series by means of DDMS-VAR models is illustrated. The methodology and the software are applied to the analysis of the U.S. business cycle.
Business cycle analysis with multivariate Markov switching models
2004
The aim of the paper is to describe the cyclical phases of the economy using multivariate Markov switching models. The class of Markov switching models can be extended in two main directions in a multivariate framework. In the first approach, the switching dynamics are introduced by way of one common latent factor . In the second approach, developed by Krolzig (1997), a VAR model with parameters depending on one common Markov chain is considered (MS VAR). We will extend the MS VAR approach allowing for the presence of specific Markov chain in each equation of the VAR (Multiple Markov Switching VAR models, MMS VAR). Dynamic factor models with regime switches, MS VAR and MMS VAR models allow for a multi-country or a multi-sector simultaneous analysis in the search of common phases which are represented by the states of the switching latent factor. Moreover, in the MMS VAR approach we explore the introduction of correlated Markov chains which allow us to evaluate the relationships among phases in different economies or sectors and introduce causality relationships, which allow a more parsimonious representations. We apply the MMS model in order to study the relationship between cyclical phases of the industrial production in the U.S. and Euro zone. Moreover, we construct a MMS model in order to explore the cyclical relationship between the Euro zone industrial production and the industrial component of the European Sentiment Index (ESI).
2019
Markov-switching (MS) model is one of the most popular nonlinear time series models in the literature. However, as there are many methods for parameter estimation, the results including the plot are not similar and become more difficult for researchers to decide on the interpretation. Therefore, this study is conducted as we want to obtain a more sensitive estimation method for the MS model. This study attempts to improve the way we estimate the MS model by developing a more flexible estimator for it to be called a maximum empirical likelihood estimation (MELE). A key point of this method is that a conventional parametric likelihood is replaced by the empirical likelihood function with relatively minor modifications to existing recursive filters. To evaluate the new method’s performance, we apply the MS model to the U.S. business cycle. The estimated results from the MELE are discussed and compared to those from classical parametric estimations. It is found that the empirical likeli...
A Markov switching factor-augmented VAR model for analyzing US business cycles and monetary policy
This paper develops a multivariate regime switching monetary policy model for the US economy. To exploit a large dataset we use a factor-augmented VAR with discrete regime shifts, capturing distinct business cycle phases. The transitions between regimes are modelled as time-varying, depending on a broad set of different indicators that may influence business cycle movements. Employing a dataset which consists of a large set of macroeconomic time series spanning the period from 1971Q4 to 2014Q2, the model is then used to draw a picture of the dynamic relationship between business cycle phases and monetary policy. Our results may be summarized as follows. First, we find that lagged prices, the share of people working in construction and the employment in manufacturing serve as good predictors for business cycle transitions. Second, our findings suggest that impulse response functions are in general more persistent in expansionary phases, while being of a more transient nature in recessions.
Information Management and Business Review, 2013
Commodity price always related to the movement of stock market index. However real economic time series data always exhibit nonlinear properties such as structural change, jumps or break in the series through time. Therefore, linear time series models are no longer suitable and Markov Switching Vector Autoregressive models which able to study the asymmetry and regime switching behavior of the data are used in the study. Intercept adjusted Markov Switching Vector Autoregressive (MSI-VAR) model is discuss and applied in the study to capture the smooth transition of the stock index changes from recession state to growth state. Results found that the dramatically changes from one state to another state are continuous smooth transition in both regimes. In addition, the 1-step prediction probability for the two regime Markov Switching model which act as the filtered probability to the actual probability of the variables is converged to the actual probability when undergo an intercept adju...
Some Regime-Switching Models for Economic Time Series: A Comparative Study
Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering
This paper mainly discusses some regime-switching models and explore their usefulness in modeling the economic time series. In recent years, several time series models have been proposed which shape the idea of the existence of different regimes produced by a stochastic process. Especially, nonlinear time series models have gained more attention because linear time series models faced various limitations. The purpose of this study is to establish the methodology of the Self-Exciting Threshold Autoregressive (SETAR) model, Smooth Transition Autoregressive (STAR) model and Markov-Switching (MSW) model from parametric nonlinear time series models in the mean and to compare these models with each other through two financial data sets. For this purpose, some theoretical information on the subject models are given without going into too much detail. In the light of the obtained theoretical information, all models are modeled by using two financial data sets. The obtained models are compar...
Comparing Probability Forecasts in Markov Regime Switching Business Cycle Models
Journal of Business Cycle Measurement and Analysis, 2007
We evaluate techniques for comparing the ability of Markov regime switching (MRS) models to fit underlying regimes of a series of interest. This is particularly important in the business cycle literature where one may be interested in determining whether using leading indicators to allow transition probabilities to vary improves the ability of MRS models to fit the NBER business cycle chronology. This is typically done using the quadratic probability score, or QPS (Diebold and Rudebusch (1989)). Although it is possible to statistically compare the QPS statistics for two MRS models using the Diebold and Mariano (1995) (DM) test statistic for comparing forecasts, we find using a Monte Carlo experiment that the DM statistic tends to under-reject (the null of "no difference in forecast accuracy") when comparing MRS models. This we believe is because of the strong non-normality of the forecast errors of such models. Furthermore, using simulation-based inference we demonstrate that leading indicators improve the fit of an MRS model of the US business cycle chronology by 24 percent, such improvement having a p-value of 0.001.
New directions in business cycle research and financial analysis
Empirical Economics, 2002
This paper serves as a partial introduction to and survey of the literature on Markov-switching models. We review the history of this class of models, describe their mathematical structure, and exposit the basic ideas behind estimation and inference. The paper also describes how the approach can be extended in a variety of directions, such as non-Gaussian distributions, time-varying transition probabilities, vector processes, state-space and GARCH models, and surveys recent methodological advances. The contributions of the other papers in this volume are reviewed. A final section o¤ers conclusions and implications for policy.