10,497 research outputs found

    Measuring Business Cycle Turning Points in Japan with a Dynamic Markov Switching Factor Model

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    In the dynamic factor model, a single unobserved factor common to some macroeconomic variables is defined as a composite index to measure business cycles. This model has recently been developed by combining with the regime switching model so that the mean growth of the index may shift depending on whether the economy is in a boom regime or in a recession regime. An advantage of this dynamic Markov switching factor model is that estimating the model by a Bayesian method produces the posterior probabilities that the economy is in the recession regime, which can be used to date the business cycle turning points. This article estimates the dynamic Markov switching factor model using some macroeconomic variables in Japan. The model comparison using the Bayes factor does not provide strong evidence that the mean growth of the index shifts, but the dynamic Markov switching factor model is found to produce the estimates of turning points close to the reference dates of the Economic and Social Research Institute in the Cabinet Office, unless only weakly correlated variables are used.

    Sources of the Great Moderation: shocks, frictions, or monetary policy?

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    We study the sources of the Great Moderation by estimating a variety of medium-scale dynamic stochastic general equilibrium (DSGE) models that incorporate regime switches in shock variances and the inflation target. The best-fit model—the one with two regimes in shock variances—gives quantitatively different dynamics compared with the benchmark constant-parameter model. Our estimates show that three kinds of shocks accounted for most of the Great Moderation and business-cycle fluctuations: capital depreciation shocks, neutral technology shocks, and wage markup shocks. In contrast to the existing literature, we find that changes in the inflation target or shocks in the investment-specific technology played little role in macroeconomic volatility. Moreover, our estimates indicate considerably fewer nominal rigidities than the literature suggests.Econometric models

    Sources of the Great Moderation: shocks, friction, or monetary policy?

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    We study the sources of the Great Moderation by estimating a variety of medium-scale DSGE models that incorporate regime switches in shock variances and in the inflation target. The best-fit model, the one with two regimes in shock variances, gives quantitatively different dynamics in comparison with the benchmark constant-parameter model. Our estimates show that three kinds of shocks accounted for most of the Great Moderation and business-cycle fluctuations: capital depreciation shocks, neutral technology shocks, and wage markup shocks. In contrast to the existing literature, we find that changes in the inflation target or shocks in the investment-specific technology played little role in macroeconomic volatility. Moreover, our estimates indicate much less nominal rigidities than those suggested in the literature.Econometric models ; Business cycles

    Pseudo Bayesian Estimation of One-way ANOVA Model in Complex Surveys

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    We devise survey-weighted pseudo posterior distribution estimators under 2-stage informative sampling of both primary clusters and secondary nested units for a one-way ANOVA population generating model as a simple canonical case where population model random effects are defined to be coincident with the primary clusters. We consider estimation on an observed informative sample under both an augmented pseudo likelihood that co-samples random effects, as well as an integrated likelihood that marginalizes out the random effects from the survey-weighted augmented pseudo likelihood. This paper includes a theoretical exposition that enumerates easily verified conditions for which estimation under the augmented pseudo posterior is guaranteed to be consistent at the true generating parameters. We reveal in simulation that both approaches produce asymptotically unbiased estimation of the generating hyperparameters for the random effects when a key condition on the sum of within cluster weighted residuals is met. We present a comparison with frequentist EM and a methods that requires pairwise sampling weights.Comment: 46 pages, 9 figure

    Forecasting Time Series Subject to Multiple Structural Breaks

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    This paper provides a novel approach to forecasting time series subject to discrete structural breaks. We propose a Bayesian estimation and prediction procedure that allows for the possibility of new breaks over the forecast horizon, taking account of the size and duration of past breaks (if any) by means of a hierarchical hidden Markov chain model. Predictions are formed by integrating over the hyper parameters from the meta distributions that characterize the stochastic break point process. In an application to US Treasury bill rates, we find that the method leads to better out-of-sample forecasts than alternative methods that ignore breaks, particularly at long horizons.structural breaks, forecasting, hierarchical hidden Markov chain model, Bayesian model averaging.
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