Journal ArticleDOI
Global Macroeconomic Uncertainty
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TLDR
The authors empirically identify global macroeconomic uncertainty using a dynamic factor model, where the conditional variances of all factors are modeled as stochastic volatility processes, and find that global uncertainty unambiguously lowers national growth rates and raises national inflation rates, and that key macroeconomic variables like oil, commodity and stock prices as well as global liquidity act as drivers of the global dimension of uncertainty.About:
This article is published in Journal of Macroeconomics.The article was published on 2017-09-01. It has received 34 citations till now. The article focuses on the topics: Recession & Stochastic volatility.read more
Citations
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Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
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Bayesian Inference on GARCH Models using the Gibbs Sampler
Luc Bauwens,Michel Lubrano +1 more
TL;DR: It is shown that the Gibbs sampler can be combined with a unidimensional deterministic integration rule applied to each coordinate of the posterior density to perform Bayesian inference on GARCH models.
Journal ArticleDOI
Measuring Global and Country-Specific Uncertainty
TL;DR: In this paper, the authors decompose the uncertainty of a typical forecaster into common and idiosyncratic uncertainty, and develop monthly measures of macroeconomic uncertainty covering 45 countries and construct a measure of global uncertainty as the weighted average of country-specific uncertainties.
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Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty
TL;DR: In this paper, the role of a news-based measure of economic policy uncertainty (EPU) in explaining time-varying co-movements in economic activity and volatility of 48 US states and 51 largest MSAs was analyzed.
References
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Book
Applied Econometric Time Series
TL;DR: In this article, the authors present an alternative solution method for Deterministic Processes by iteratively solving homogeneous difference equation and finding particular solutions for deterministic processes, and conclude that the proposed solution is the best solution.
Book
Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this article, the Kalman filter and state space models were used for univariate structural time series models to estimate, predict, and smoothen the univariate time series model.
Journal ArticleDOI
Measuring Economic Policy Uncertainty
TL;DR: The authors developed a new index of economic policy uncertainty based on newspaper coverage frequency and found that policy uncertainty spikes near tight presidential elections, Gulf Wars I and II, the 9/11 attacks, the failure of Lehman Brothers, the 2011 debt ceiling dispute and other major battles over fiscal policy.
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Forecasting, Structural Time Series Models and the Kalman Filter
TL;DR: In this paper, the authors provide a unified and comprehensive theory of structural time series models, including a detailed treatment of the Kalman filter for modeling economic and social time series, and address the special problems which the treatment of such series poses.
Posted Content
The Impact of Uncertainty Shocks
TL;DR: In this paper, a model with a time varying second moment is proposed to simulate a macro uncertainty shock, which produces a rapid drop and rebound in aggregate output and employment, which occurs because higher uncertainty causes firms to temporarily pause their investment and hiring.
Related Papers (5)
The evolving impact of global, region-specific and country-specific uncertainty
Haroon Mumtaz,Alberto Musso +1 more