The Time Varying Volatility of Macroeconomic Fluctuations

Justiniano, Alejandro; Primiceri, Giorgio E.

doi:10.3386/w12022

Cited by 316 publications

(491 citation statements)

References 41 publications

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“…These results are consistent with previous studies such as Kim and Nelson (1999a) and Justiniano and Pimiceri (2006) The paths of expected output under learning and rational expectations are very dierent.…”

Section: Resultssupporting

confidence: 93%

Regime Switching, Learning, and the Great Moderation

Murray

2008

SSRN Journal

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This paper examines the bad luck explanation for changing volatility in U.S. ination and output when agents do not have rational expectations, but instead form expectations through least squares learning with an endogenously changing learning gain. It has been suggested that this type of endogenously changing learning mechanism can create periods of excess volatility without the need for changes in the variance of the underlying shocks. Bad luck is modeled into a standard New Keynesian model by augmenting it with two states that evolve according to a Markov chain, where one state is characterized by large variances for structural shocks, and the other state has relatively smaller variances. To assess whether learning can explain the Great Moderation, the New Keynesian model with volatility regime switching and dynamic gain learning is estimated by maximum likelihood. The results show that learning does lead to lower variances for the shocks in the volatile regime, but changes in regime is still signicant in dierences in volatility from the 1970s and after the the 1980s.

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Section: Resultssupporting

confidence: 93%

Regime Switching, Learning, and the Great Moderation

Murray

2008

SSRN Journal

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“…Other authors have implemented similar Taylor rules (e.g., Del Negro et al, 2004;Justiniano and Primiceri, 2006). Other authors have implemented similar Taylor rules (e.g., Del Negro et al, 2004;Justiniano and Primiceri, 2006).…”

Section: Governmentmentioning

confidence: 99%

“…As in Justiniano and Primiceri (2006), I assume that government purchases are a stochastic fraction of output: G t D S g,t Y t ; and that the law of motion for S g is log S g,t D 1 g log S g C g log S g,t 1 C g ε g,t , where ε g,t has a standard normal distribution. As in Justiniano and Primiceri (2006), I assume that government purchases are a stochastic fraction of output: G t D S g,t Y t ; and that the law of motion for S g is log S g,t D 1 g log S g C g log S g,t 1 C g ε g,t , where ε g,t has a standard normal distribution.…”

Section: Governmentmentioning

confidence: 99%

“…Such complexity makes researchers rely more and more frequently on numerical solutions to their mathematical abstractions of reality. Del Negro et al (2004), Justiniano and Primiceri (2006), Levin et al (2005), and Smets and Wouters (2007) estimate their models using data on output, consumption, investment, labor, real wages, interest rates, and inflation. Most of this model-based policy analysis elaborates on the important contributions of Rotemberg and Woodford (1997) and Christiano et al (2005, hereafter CEE).…”

Section: Introductionmentioning

confidence: 99%

“…

This paper explores the effects of using alternative combinations of observables for the estimation of Dynamic Stochastic General Equilibrium (DSGE) models. For example, while Del Negro et al (2004) and Justiniano and Primiceri (2006) use likelihood-based methods, CEE andAltig et al (2005) choose a minimum-distance estimator.At this stage it is unclear what effects the set of observables has on the estimated parameters and on the model's economic implications. In terms of the model's predictions, the exclusion of some observables may lead to estimated parameters with unexpected outcomes, such as recessions following a positive technology shock.

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confidence: 99%

See 2 more Smart Citations

What You Match Does Matter: The Effects of Data on DSGE Estimation

Guerrón‐Quintana

2007

SSRN Journal

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Policy‐Oriented Macroeconomic Forecasting with Hybrid DGSE and Time‐Varying Parameter VAR Models

Bekiros

Paccagnini

2016

Journal of Forecasting

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Micro-founded dynamic stochastic general equilibrium (DSGE) models appear to be particularly suited for evaluating the consequences of alternative macroeconomic policies. Recently, increasing e¤orts have been undertaken by policymakers to use these models for forecasting, although this proved to be problematic due to estimation and identi…cation issues. Hybrid DSGE models have become popular for dealing with some of model misspeci…cations and the trade-o¤ between theoretical coherence and empirical …t, thus allowing them to compete in terms of predictability with VAR models. However, DSGE and VAR models are still linear and they do not consider time-variation in parameters that could account for inherent nonlinearities and capture the adaptive underlying structure of the economy in a robust manner. This study conducts a comparative evaluation of the out-of-sample predictive performance of many di¤erent speci…cations of DSGE models and various classes of VAR models, using datasets for the real GDP, the harmonized CPI and the nominal short-term interest rate series in the Euro area. Simple and hybrid DSGE models were implemented including DSGE-VAR and Factor Augmented DGSE, and tested against standard, Bayesian and Factor Augmented VARs. Moreover, a new state-space time-varying VAR model is presented. The total period spanned from 1970:1 to 2010:4 with an out-of-sample testing period of 2006:1-2010:4, which covers the global …nancial crisis and the EU debt crisis. The results of this study can be useful in conducting monetary policy analysis and macro-forecasting in the Euro area.JEL Classi…cation: C11, C15, C32

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The Time Varying Volatility of Macroeconomic Fluctuations

Cited by 316 publications

References 41 publications

Regime Switching, Learning, and the Great Moderation

Regime Switching, Learning, and the Great Moderation

What You Match Does Matter: The Effects of Data on DSGE Estimation

Policy‐Oriented Macroeconomic Forecasting with Hybrid DGSE and Time‐Varying Parameter VAR Models

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