The Experts below are selected from a list of 105423 Experts worldwide ranked by ideXlab platform
Jouchi Nakajima - One of the best experts on this subject based on the ideXlab platform.
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Time-VARying Vector Autoregressive Model - A Survey with the Application to the Japanese Macroeconomic Data -
2012Co-Authors: Jouchi Nakajima, Toshiaki WatanabeAbstract:The time-VARying vector autoregressive (VAR) Model has recently attracted attention as a time series Model for the analysis of macroeconomic VARiables and developed in VARious directions. This article explains this Model and surveys the recent development of its structure and empirical applications. Since this Model is usually estimated using a Bayesian method via the Markov chain Monte Carlo (MCMC), we explain this estimation method in detail. We also provide empirical results based on the Japanese macroeconomic data and show the superior forecasting performance of the time-VARying VAR Model.
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Time-VARying Vector Autoregressive Modei-A Survey with the Application to the Japanese Macroeconomic Data-
Econometric Reviews, 2012Co-Authors: Jouchi Nakajima, Toshiaki WatanabeAbstract:The time-VARying vector autoregressive (VAR) Model has recently attracted attention as a time series Model for the analysis of macroeconomic VARiables and developed in VARious directions. This article explains this Model and surveys the recent development of its structure and empirical applications. Since this Model is usually estimated using a Bayesian method via the Markov chain Monte Carlo (MCMC), we explain this estimation method in detail. We also provide empirical results based on the Japanese macroeconomic data and show the superior forecasting performance of the time-VARying VAR Model. (This abstract was borrowed from another version of this item.)
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bayesian analysis of time VARying parameter vector autoregressive Model for the japanese economy and monetary policy
Journal of The Japanese and International Economies, 2011Co-Authors: Jouchi Nakajima, Munehisa Kasuya, Toshiaki WatanabeAbstract:This paper analyzes the time-VARying parameter vector autoregressive (TVP-VAR) Model for the Japanese economy and monetary policy. The time-VARying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-VARying structure of the Japanese economy and monetary policy during the period from 1981 to 2008. The marginal likelihoods of the TVP-VAR Model and other VAR Models are also estimated. The estimated marginal likelihoods indicate that the TVP-VAR Model best fits the Japanese economic data.
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Time-VARying Parameter VAR Model with Stochastic Volatility: An Overview of Methodology and Empirical Applications
2011Co-Authors: Jouchi NakajimaAbstract:This paper aims to provide a comprehensive overview of the estimation methodology for the time-VARying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR Model, combined with stochastic volatility, enables us to capture possible changes in underlying structure of the economy in a flexible and robust manner. In that respect, as shown in simulation exercises in the paper, the incorporation of stochastic volatility to the TVP estimation significantly improves estimation performance. The Markov chain Monte Carlo (MCMC) method is employed for the estimation of the TVP-VAR Models with stochastic volatility. As an example of empirical application, the TVP-VAR Model with stochastic volatility is estimated using the Japanese data with significant structural changes in dynamic relationship between the macroeconomic VARiables.
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time VARying parameter VAR Model with stochastic volatility an overview of methodology and empirical applications
Monetary and and Economic Studies, 2011Co-Authors: Jouchi NakajimaAbstract:This paper aims to provide a comprehensive overview of the estimation methodology for the time-VARying parameter structural vector autoregression (TVP-VAR) with stochastic volatility, in both methodology and empirical applications. The TVP-VAR Model, combined with stochastic volatility, enables us to capture possible changes in underlying structure of the economy in a flexible and robust manner. In this respect, as shown in simulation exercises in the paper, the incorporation of stochastic volatility into the TVP estimation significantly improves estimation performance. The Markov chain Monte Carlo method is employed for the estimation of the TVP-VAR Models with stochastic volatility. As an example of empirical application, the TVP-VAR Model with stochastic volatility is estimated using the Japanese data with significant structural changes in the dynamic relationship between the macroeconomic VARiables.
Masahiro Kawai - One of the best experts on this subject based on the ideXlab platform.
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was financial market contagion the source of economic crisis in asia evidence using a multiVARiate VAR Model
Journal of Asian Economics, 2003Co-Authors: Ahmed M Khalid, Masahiro KawaiAbstract:Abstract The episodes of financial crises in many parts of the world during the 1990s have sparked interest in identifying channels through which such crises spread from one country to another. Researchers have identified several factors that may have sparked and induced contagion. This study further extends the existing research by identifying and testing three financial market VARiables to trace the alleged origin and the subsequent path of the contagion during the 1997 Asian Crisis. Foreign exchange rates, stock market prices and interest rates are three main financial market indicators, representing the currency, stock and money markets, respectively. We use a sample of nine East Asian countries, including Japan, construct a VAR Model and use daily observations for empirical estimation. We investigate the interlinkages among different markets and different countries within the Asian region using the Granger causality. The empirical evidence, in this paper, does not find strong support for contagion. We further extend the analysis by looking at the impulse responses. The results still do not find strong support for a contagion case.
Massimiliano Marcellino - One of the best experts on this subject based on the ideXlab platform.
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Markov-switching mixed-frequency VAR Models
International Journal of Forecasting, 2015Co-Authors: Claudia Foroni, Pierre Guérin, Massimiliano MarcellinoAbstract:Abstract 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. Finally, the MSMF-VAR Model is used to predict GDP growth and business cycle turning points in the euro area. Its performance is then compared with those 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 for estimating the status of economic activity.
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Markov-Switching Mixed-Frequency VAR Models
2014Co-Authors: Claudia Foroni, Pierre Guérin, Massimiliano MarcellinoAbstract: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.
Toshiaki Watanabe - One of the best experts on this subject based on the ideXlab platform.
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Time-VARying Vector Autoregressive Model - A Survey with the Application to the Japanese Macroeconomic Data -
2012Co-Authors: Jouchi Nakajima, Toshiaki WatanabeAbstract:The time-VARying vector autoregressive (VAR) Model has recently attracted attention as a time series Model for the analysis of macroeconomic VARiables and developed in VARious directions. This article explains this Model and surveys the recent development of its structure and empirical applications. Since this Model is usually estimated using a Bayesian method via the Markov chain Monte Carlo (MCMC), we explain this estimation method in detail. We also provide empirical results based on the Japanese macroeconomic data and show the superior forecasting performance of the time-VARying VAR Model.
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Time-VARying Vector Autoregressive Modei-A Survey with the Application to the Japanese Macroeconomic Data-
Econometric Reviews, 2012Co-Authors: Jouchi Nakajima, Toshiaki WatanabeAbstract:The time-VARying vector autoregressive (VAR) Model has recently attracted attention as a time series Model for the analysis of macroeconomic VARiables and developed in VARious directions. This article explains this Model and surveys the recent development of its structure and empirical applications. Since this Model is usually estimated using a Bayesian method via the Markov chain Monte Carlo (MCMC), we explain this estimation method in detail. We also provide empirical results based on the Japanese macroeconomic data and show the superior forecasting performance of the time-VARying VAR Model. (This abstract was borrowed from another version of this item.)
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bayesian analysis of time VARying parameter vector autoregressive Model for the japanese economy and monetary policy
Journal of The Japanese and International Economies, 2011Co-Authors: Jouchi Nakajima, Munehisa Kasuya, Toshiaki WatanabeAbstract:This paper analyzes the time-VARying parameter vector autoregressive (TVP-VAR) Model for the Japanese economy and monetary policy. The time-VARying parameters are estimated via the Markov chain Monte Carlo method and the posterior estimates of parameters reveal the time-VARying structure of the Japanese economy and monetary policy during the period from 1981 to 2008. The marginal likelihoods of the TVP-VAR Model and other VAR Models are also estimated. The estimated marginal likelihoods indicate that the TVP-VAR Model best fits the Japanese economic data.
Michael J. Dueker - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Forecasts of Qualitative VARiables: A Qual VAR Model of U.S. Recessions
Journal of Business & Economic Statistics, 2005Co-Authors: Michael J. DuekerAbstract:This article presents a new Qual VAR Model for incorporating information from qualitative and/or discrete VARiables in vector autoregressions. With a Qual VAR, it is possible to create dynamic forecasts of the qualitative VARiable using standard VAR projections. Previous forecasting methods for qualitative VARiables, in contrast, produce only static forecasts. I apply the Qual VAR to forecasting the 2001 business recession out of sample and to analyzing the Romer and Romer narrative measure of monetary policy contractions as an endogenous VARiable in a VAR. Out of sample, the Model predicts the timing of the 2001 recession quite well relative to the recession probabilities put forth at the time by professional forecasters. Qual VARs—which include information about the qualitative VARiable—can also enhance the quality of density forecasts of the other VARiables in the system.
