Regime-switching cointegration

Jochmann, Markus; Koop, Gary

doi:10.1515/snde-2012-0064

Cited by 27 publications

(25 citation statements)

References 34 publications

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“…This section introduces a Bayesian approach to a Markov switching cointegration model based on Jochmann and Koop (2015). Cheung and Lai (1993) show that the Johansen"s cointegration tests are sensitive to the choice of the lag length.…”

Section: Markov Switching Cointegration Modelmentioning

confidence: 99%

“…Cheung and Lai (1993) show that the Johansen"s cointegration tests are sensitive to the choice of the lag length. Bayesian cointegration analysis would also be affected by the choice of the lag length, so that we arrange the original model by Jochmann and Koop (2015), that suppose the lag length is constant over all regimes, to allow the regime shifts in the lag length.…”

Section: Markov Switching Cointegration Modelmentioning

confidence: 99%

“…The MSVECM in (2) differs from the model used by Jochmann and Koop (2015) or Sugita (2016): the model in (2) allows the lag length to change with regime. Let = (Δ ′ 1 , … , Δ ′ )′, then the MSVECM in (2) can be written as a matrix format in the following way:…”

Section: Markov Switching Cointegration Modelmentioning

confidence: 99%

“…Koop et al (2010) develop an efficient posterior simulation method using a collapsed Gibbs sampler, and Jochmann and Koop (2015) and Sugita (2016) apply the method for a Markov switching cointegration model. Koop et al (2010) propose the following priors:…”

Section: Markov Switching Cointegration Modelmentioning

confidence: 99%

“…A Bayesian MSVECM, based on Jochmann and Koop (2015), allows the regime shift in any parameter in the vector error correction model such as the cointegration rank, the cointegrating vector, and the lag length as well as other parameters in the model, so that we can study whether the non-linearities by monetary policy influence the error correction mechanism and the dynamic mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Non-Linear Analysis of the Fisher Effect: In the Case of Japan

Sugita¹

2017

IJEF

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The Fisher effect has been commonly analyzed to investigate the long-run relationship between nominal interest rates and expected inflation rate, though it is rarely successful in finding the cointegration relationship as the Fisher effect states. In this paper, a Bayesian Markov switching vector error correction model is applied to analyze non-linearity in the Fisher effect in the case of Japan. We find that the Fisher effect holds in one regime although it does not hold in another regime when the nominal interest rate is stable and does not respond against disequilibrium by the monetary policy such as the zero interest rate policy. This model reveals non-linearity in the error correction mechanism of the Fisher effect in Japan.

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Section: Markov Switching Cointegration Modelmentioning

confidence: 99%

Section: Markov Switching Cointegration Modelmentioning

confidence: 99%

Section: Markov Switching Cointegration Modelmentioning

confidence: 99%

Section: Markov Switching Cointegration Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-Linear Analysis of the Fisher Effect: In the Case of Japan

Sugita¹

2017

IJEF

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Bayesian Markov switching model for BRICS currencies' exchange rates

Kumar,

Ahmad,

Uddin

2024

Journal of Forecasting

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Exchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time‐varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis‐à‐vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal‐gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime‐specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime‐specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out‐of‐sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model.

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An efficient Bayesian approach to multiple structural change in multivariate time series

Maheu

Song

2017

J of Applied Econometrics

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This paper provides a feasible approach to estimation and forecasting of multiple structural breaks for vector autoregressions and other multivariate models. Due to conjugate prior assumptions we obtain a very efficient sampler for the regime allocation variable. A new hierarchical prior is introduced to allow for learning over different structural breaks. The model is extended to independent breaks in regression coefficients and the volatility parameters. Two empirical applications show the improvements the model has over benchmarks. In a macro application with 7 variables we empirically demonstrate the benefits from moving from a multivariate structural break model to a set of univariate structural break models to account for heterogeneous break patterns across data series. * Maheu thanks SSHRC of Canada for financial support and the 2015 Eminent Research Scholarship,

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Regime-switching cointegration

Cited by 27 publications

References 34 publications

Non-Linear Analysis of the Fisher Effect: In the Case of Japan

Non-Linear Analysis of the Fisher Effect: In the Case of Japan

Bayesian Markov switching model for BRICS currencies' exchange rates

An efficient Bayesian approach to multiple structural change in multivariate time series

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