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Macroeconomic Forecasting Performance under Alternative Specifications of Time-Varying Volatility: FORECASTING PERFORMANCE AND SPECIFICATIONS OF TIME-VARYING VOLATILITY
Abstract
This paper compares alternative models of time-varying volatility on the basis of the accuracy of real-time point and density forecasts of key macroeconomic time series for the USA. We consider Bayesian autoregressive and vector autoregressive models that incorporate some form of time-varying volatility, precisely random walk stochastic volatility, stochastic volatility following a stationary AR process, stochastic volatility coupled with fat tails, GARCH and mixture of innovation models. The results show that the AR and VAR specifications with conventional stochastic volatility dominate other volatility specifications, in terms of point forecasting to some degree and density forecasting to a greater degree. Copyright © 2014 John Wiley & Sons, Ltd.
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Citations
Cites
Generalized autoregressive conditional heteroskedasticity
Bollerslev, T. (1986) · Journal of Econometrics · doi:10.1016/0304-4076(86)90063-1 · Cited by 22,254
Engle, R. F. (1982) · Econometrica · doi:10.2307/1912773 · Cited by 20,795
Strictly Proper Scoring Rules, Prediction, and Estimation
Gneiting, T., & Raftery, A. E. (2007) · Journal of the American Statistical Association · doi:10.1198/016214506000001437 · Cited by 5,793
Diebold, F., & Mariano, R. (1994) · National Bureau of Economic Research · doi:10.3386/t0169 · Cited by 5,185
Diebold, F. X., & Mariano, R. S. (1995) · Journal of Business & Economic Statistics · doi:10.1080/07350015.1995.10524599 · Cited by 5,130
Time Varying Structural Vector Autoregressions and Monetary Policy
Primiceri, G. E. (2005) · The Review of Economic Studies · doi:10.1111/j.1467-937x.2005.00353.x · Cited by 3,030
Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
Kim, S., Shepherd, N., & Chib, S. (1998) · Review of Economic Studies · doi:10.1111/1467-937x.00050 · Cited by 2,261
Drifts and volatilities: monetary policies and outcomes in the post WWII US
Cogley, T., & Sargent, T. J. (2005) · Review of Economic Dynamics · doi:10.1016/j.red.2004.10.009 · Cited by 1,933
On Gibbs sampling for state space models
CARTER, C. K., & KOHN, R. (1994) · Biometrika · doi:10.1093/biomet/81.3.541 · Cited by 1,917
A forecast comparison of volatility models: does anything beat a GARCH(1,1)?
Hansen, P. R., & Lunde, A. (2005) · Journal of Applied Econometrics · doi:10.1002/jae.800 · Cited by 1,773
Why Has U.S. Inflation Become Harder to Forecast?
STOCK, J. H., & WATSON, M. W. (2007) · Journal of Money, Credit and Banking · doi:10.1111/j.1538-4616.2007.00014.x · Cited by 1,664
Tests of Conditional Predictive Ability
Giacomini, R., & White, H. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00718.x · Cited by 1,646
Were There Regime Switches in U.S. Monetary Policy?
Sims, C. A., & Zha, T. (2006) · American Economic Review · doi:10.1257/000282806776157678 · Cited by 1,590
An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator
Andrews, D. W. K., & Monahan, J. C. (1992) · Econometrica · doi:10.2307/2951574 · Cited by 1,089
Federal Reserve Information and the Behavior of Interest Rates
Romer, C. D., & Romer, D. H. (2000) · American Economic Review · doi:10.1257/aer.90.3.429 · Cited by 1,047
The Time-Varying Volatility of Macroeconomic Fluctuations
Justiniano, A., & Primiceri, G. E. (2008) · American Economic Review · doi:10.1257/aer.98.3.604 · Cited by 843
A real-time data set for macroeconomists
Croushore, D., & Stark, T. (2001) · Journal of Econometrics · doi:10.1016/s0304-4076(01)00072-0 · Cited by 765
Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
Jacquier, É., Polson, N. G., & Rossi, P. E. (2003) · Journal of Econometrics · doi:10.1016/j.jeconom.2003.09.001 · Cited by 622
NUMERICAL METHODS FOR ESTIMATION AND INFERENCE IN BAYESIAN VAR-MODELS
KADIYALA, K. R., & KARLSSON, S. (1997) · Journal of Applied Econometrics · doi:10.1002/(sici)1099-1255(199703)12:2<99::aid-jae429>3.0.co;2-a · Cited by 617
Has the Business Cycle Changed and Why?
Stock, J., & Watson, M. (2002) · National Bureau of Economic Research · doi:10.3386/w9127 · Cited by 560
Comparing Density Forecasts via Weighted Likelihood Ratio Tests
Amisano, G., & Giacomini, R. (2007) · Journal of Business & Economic Statistics · doi:10.1198/073500106000000332 · Cited by 518
Estimating Macroeconomic Models: A Likelihood Approach
FERNÁNDEZ-VILLAVERDE, J., & RUBIO-RAMÍREZ, J. F. (2007) · Review of Economic Studies · doi:10.1111/j.1467-937x.2007.00437.x · Cited by 513
Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules
Gneiting, T., & Ranjan, R. (2011) · Journal of Business & Economic Statistics · doi:10.1198/jbes.2010.08110 · Cited by 507
Comparing and evaluating Bayesian predictive distributions of asset returns
Geweke, J., & Amisano, G. (2010) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2009.10.007 · Cited by 376
Macroeconomic forecasting and structural change
D'Agostino, A., Gambetti, L., & Giannone, D. (2013) · Journal of Applied Econometrics · doi:10.1002/jae.1257 · Cited by 359
Has the Business Cycle Changed? Evidence and ExplanationsPreprint
Stock, J. H. (2003) · RePEc: Research Papers in Economics · Cited by 307
Regime Switches, Agents' Beliefs, and Post-World War II U.S. Macroeconomic Dynamics
Bianchi, F. (2013) · The Review of Economic Studies · doi:10.1093/restud/rds032 · Cited by 300
The Role of Models and Probabilities in the Monetary Policy Process
Sims, C. A. (2002) · Brookings Papers on Economic Activity · doi:10.1353/eca.2003.0009 · Cited by 299
Have We Underestimated the Likelihood and Severity of Zero Lower Bound Events?
CHUNG, H., LAFORTE, J., REIFSCHNEIDER, D., & WILLIAMS, J. C. (2012) · Journal of Money, Credit and Banking · doi:10.1111/j.1538-4616.2011.00478.x · Cited by 278
Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset
Faust, J., & Wright, J. H. (2009) · Journal of Business & Economic Statistics · doi:10.1198/jbes.2009.07214 · Cited by 241
Poirier, D. J. (2008) · Palgrave Macmillan UK eBooks · doi:10.1057/9780230226203.0108 · Cited by 223
Estimation and Forecasting in Models with Multiple Breaks
KOOP, G., & POTTER, S. M. (2007) · Review of Economic Studies · doi:10.1111/j.1467-937x.2007.00436.x · Cited by 220
Common Drifting Volatility in Large Bayesian VARs
Carriero, A., Clark, T. E., & Marcellino, M. (2016) · Journal of Business & Economic Statistics · doi:10.1080/07350015.2015.1040116 · Cited by 199
Self-citationCogley, T., Morozov, S., & Sargent, T. J. (2005) · Journal of Economic Dynamics and Control · doi:10.1016/j.jedc.2005.06.005 · Cited by 198
Real-Time Inflation Forecasting in a Changing World
Groen, J. J. J., Paap, R., & Ravazzolo, F. (2013) · Journal of Business & Economic Statistics · doi:10.1080/07350015.2012.727718 · Cited by 194
Self-citationCombining forecast densities from VARs with uncertain instabilities
Jore, A. S., Mitchell, J., & Vahey, S. P. (2010) · Journal of Applied Econometrics · doi:10.1002/jae.1162 · Cited by 180
Forecasting Performance of an Open Economy DSGE Model
Adolfson, M., Lindé, J., & Villani, M. (2007) · Econometric Reviews · doi:10.1080/07474930701220543 · Cited by 167
Chapter 17 Forecasting with Real-Time Macroeconomic Data
Croushore, D. (2006) · Handbook of economic forecasting · doi:10.1016/s1574-0706(05)01017-7 · Cited by 163
Efficient Bayesian Inference for Dynamic Mixture Models
Gerlach, R., Carter, C., & Kohn, R. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474273 · Cited by 151
Full Bayesian Inference for GARCH and EGARCH Models
Vrontos, I. D., Δελλαπόρτας, Π., & Politis, D. N. (2000) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2000.10524861 · Cited by 140
A unified approach to nonlinearity, structural change, and outliers
Giordani, P. E., Kohn, R., & van Dijk, D. J. C. (2006) · Journal of Econometrics · doi:10.1016/j.jeconom.2006.03.013 · Cited by 107
On the effects of rare disasters and uncertainty shocks for risk premia in non-linear DSGE models
Andreasen, M. M. (2011) · Review of Economic Dynamics · doi:10.1016/j.red.2011.08.001 · Cited by 94
Fortune or Virtue: Time-Variant Volatilities Versus Parameter Drifting in U.S. Data
Fernández‐Villaverde, J., Guerron-Quintana, P. A., & Rubio-Ramírez, J. F. (2010) · National Bureau of Economic Research · doi:10.3386/w15928 · Cited by 93
Financial Stress and Economic Dynamics: The Transmission of CrisesPreprint
Hubrich, K., & Tetlow, R. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2199557 · Cited by 45
Testing for Unconditional Predictive Ability
Clark, T. E., & McCracken, M. W. (2012) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780195398649.013.0015 · Cited by 42
Self-citationNested Forecast Model Comparisons: A New Approach to Testing Equal AccuracyPreprint
Clark, T. E., & McCracken, M. W. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2166366 · Cited by 38
Self-citationForecast densities for economic aggregates from disaggregate ensembles
Ravazzolo, F., & Vahey, S. P. (2013) · Studies in Nonlinear Dynamics and Econometrics · doi:10.1515/snde-2012-0088 · Cited by 33
Self-citationNakajima, J. (2011) · Japanese Economic Review · doi:10.1111/j.1468-5876.2011.00537.x · Cited by 19
Metadata
- Authors
- Clark, T. E., & Ravazzolo, F.
- Venue
- Journal of Applied Econometrics
- Publisher
- Wiley
- Published
- 2014-01-21
- Volume
- 30
- Issue
- 4
- Pages
- 551-575
- ISSN
- 0883-7252 (ISSN-L)
- OpenAlex
- W2113422836
- Cited by
- 317
- References
- 48
- FWCI
- 49.60
- Type
- journal article
- Language
- en
- Open access
- closed
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