Trends and cycles in economic time series: A Bayesian approach

Harvey, Andrew; Trimbur, Thomas; Dijk, Herman K. van

doi:10.1016/j.jeconom.2006.07.006

Cited by 105 publications

(89 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Very natural extensions of the approach in this paper are to consider forms of nonlinearity and time variation in the model itself as Sargent (2001, 2005) and Primiceri (2005) do for the VAR. For instance, in using a SVAR for business cycle analysis one may use prior information on the length and amplitude of the period of oscillation (see Harvey, Trimbur and van Dijk (2007)). An example of a possible nonlinear time varying model that may prove useful is presented in Paap and van Dijk (2003).…”

Section: Discussionmentioning

confidence: 99%

Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks Using Bayesian Model Averaging

Strachan

Dijk

2010

SSRN Journal

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in ABSTRACTThe empirical support for a real business cycle model with two technology shocks is evaluated using a Bayesian model averaging procedure. This procedure makes use of a …nite mixture of many models within the class of vector autoregressive (VAR) processes. The linear VAR model is extended to permit cointegration, a range of deterministic processes, equilibrium restrictions and restrictions on long-run responses to technology shocks. We …nd support for a number of the features implied by the real business cycle model. For example, restricting long run responses to identify technology shocks has reasonable support and important implications for the short run responses to these shocks. Further, there is evidence that savings and investment ratios form stable relationships, but technology shocks do not account for all stochastic trends in our system. There is uncertainty as to the most appropriate model for our data, with thirteen models receiving similar support, and the model or model set used has signi…cant implications for the results obtained.

show abstract

Section: Discussionmentioning

confidence: 99%

Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks Using Bayesian Model Averaging

Strachan

Dijk

2010

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…Very natural extensions of our approach are to include prior inequality conditions in the parameter space of structural VARs and consider forms of nonlinearity and time variation in the model itself as Primiceri (2005) does for the VAR. For instance, in using a SVAR for business cycle analysis one may use prior information on the length and amplitude of the period of oscillation (see Harvey, Trimbur and van Dijk (2007)). An example of a possible nonlinear time varying structure that may prove useful is presented in Paap and van Dijk (2003).…”

Section: Discussionmentioning

confidence: 99%

Bayesian Averaging over Many Dynamic Model Structures with Evidence on the Great Ratios and Liquidity Trap Risk

Strachan

Dijk

2008

SSRN Journal

View full text Add to dashboard Cite

A Bayesian model averaging procedure is presented that makes use of a …nite mixture of many model structures within the class of vector autoregressive (VAR) processes. It is applied to two empirical issues. First, stability of the "Great Ratios" in U.S. macro-economic time series is investigated, together with the e¤ect of permanent shocks on business cycles. Second, the linear VAR model is extended to include a smooth transition function in a (monetary) equation and stochastic volatility in the disturbances. The risk of a liquidity trap in the USA and Japan is evaluated. Although this risk found to be reasonably high, we …nd only mild evidence that the monetary policy transmission mechanism is di¤erent and that central banks consider the expected cost of a liquidity trap in policy setting. Posterior probabilities of di¤erent models are evaluated using Markov chain Monte Carlo techniques.

show abstract

“…For fundamentally non-Gaussian models, the methods in Shephard and Pitt (1997) can be used. A recent contribution of Harvey et al (2005) uses Bayesian methods for state space models with trend and cyclical components, exploiting informative prior notions regarding the length of economic cycles.…”

Section: State-space Modelsmentioning

confidence: 99%

Bayesian time series analysis

Steel¹

The New Palgrave Dictionary of Economics

View full text Add to dashboard Cite

This article describes the use of Bayesian methods in the statistical analysis of time series. The use of Markov chain Monte Carlo methods has made even the more complex time series models amenable to Bayesian analysis. Models discussed in some detail are ARIMA models and their fractionally integrated counterparts, state-space models, Markov switching and mixture models, and models allowing for timevarying volatility. A final section reviews some recent approaches to nonparametric Bayesian modelling of time series. Bayesian methodsThe importance of Bayesian methods in econometrics has increased rapidly over the last decade. This is, no doubt, fuelled by an increasing appreciation of the advantages that Bayesian inference entails. In particular, it provides us with a formal way to incorporate the prior information we often possess before seeing the data, it fits perfectly with sequential learning and decision making and it directly leads to exact small sample results. In addition, the Bayesian paradigm is particularly natural for prediction, taking into account all parameter or even model uncertainty. The predictive distribution is the sampling distribution where the parameters are integrated out with the posterior distribution and is exactly what we need for forecasting, often a key goal of time-series analysis.Usually, the choice of a particular econometric model is not prespecified by theory and many competing models can be entertained. Comparing models can be done formally in a Bayesian framework through so-called posterior odds, which is the product of the prior odds and the Bayes factor. The Bayes factor between any two models is the ratio of the likelihoods integrated out with the corresponding prior and summarizes how the data favour one model over another. Given a set of possible models, this immediately leads to posterior model probabilities. Rather than choosing a single model, a natural way to deal with model uncertainty is to use the posterior model probabilities to average out the inference

show abstract

Trends and cycles in economic time series: A Bayesian approach

Cited by 105 publications

References 29 publications

Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks Using Bayesian Model Averaging

Evidence on a Real Business Cycle Model with Neutral and Investment-Specific Technology Shocks Using Bayesian Model Averaging

Bayesian Averaging over Many Dynamic Model Structures with Evidence on the Great Ratios and Liquidity Trap Risk

Bayesian time series analysis

Contact Info

Product

Resources

About