Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks

Jochmann, Markus; Koop, Gary; Strachan, Rodney W.

doi:10.1016/j.ijforecast.2009.11.002

Cited by 40 publications

(21 citation statements)

References 41 publications

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“…We have used such algorithms in previous work (e.g. Korobilis, 2012 or Jochmann, Koop andStrachan, 2010), but they require the use of MCMC methods and, thus, are computationally daunting with large TVP-VARs. always forecasts better than the former for h = 1.…”

Section: Forecast Comparisonmentioning

confidence: 99%

Large time-varying parameter VARs

Koop

Korobilis

2013

Journal of Econometrics

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373

385

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In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden through the use forgetting factors. We then extend the TVP-VAR so that its dimension can change over time. For instance, we can have a large TVP-VAR as the forecasting model at some points in time, but a smaller TVP-VAR at others. A final extension lies in the development of a new method for estimating, in a timevarying manner, the parameter(s) of the shrinkage priors commonly-used with large VARs.These extensions are operationalized through the use of forgetting factor methods and are, thus, computationally simple. An empirical application involving forecasting inflation, real output and interest rates demonstrates the feasibility and usefulness of our approach.

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Section: Forecast Comparisonmentioning

confidence: 99%

Large time-varying parameter VARs

Koop

Korobilis

2013

Journal of Econometrics

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373

385

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“…The hierarchical prior for η takes the same mixture forms of Normal as discussed above for α. For further details, interested readers are referred to George et al (2008) and Jochmann et al (2010).…”

Section: Stochastic Search Variable Selection (Ssvs) Prior For Both Vmentioning

confidence: 99%

A DSGE-VAR model for forecasting key South African macroeconomic variables

Gupta

Steinbach

2013

Economic Modelling

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The paper develops a Small Open Economy New Keynesian DSGE-VAR (SOENKDSGE-VAR) model of the South African economy, characterised by incomplete pass-through of exchange rate changes, external habit formation, partial indexation of domestic prices and wages to past inflation, and staggered price and wage setting. The model is estimated using Bayesian techniques on data from the period 1980Q1 to 2003Q2, and then used to forecast output, inflation and nominal short-term interest rate for one-to eight-quarters-ahead over an out-of sample horizon of 2003Q3 to 2010Q4. When the forecast performance of the SOENKDSGE-VAR model is compared with an independently estimated DSGE model, the classical VAR and six alternative BVAR models, we find that, barring the BVAR model based on the SSVS prior on both VAR coefficients and the error covariance, the SOENKDSGE-VAR model is found to perform competitively, if not, better than all the other VAR models.JEL Classification: C11, C53, E37

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“…For instance, the trivariate VAR(4) used in Jochmann, Koop and Strachan (2009) has 36 VAR coefficients and SSVS picks out only 10 of these as having important explanatory power. In such a situation, the number of hypothesis tests required in a sequential testing procedure could lead the researcher to serious worries about pre-test problems.…”

Section: Introductionmentioning

confidence: 99%

Stochastic search variable selection in vector error correction models with an application to a model of the UK macroeconomy

Jochmann

Koop

Leon-Gonzalez

et al. 2011

J of Applied Econometrics

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This paper develops methods for Stochastic Search Variable Selection (currently popular with regression and Vector Autoregressive models) for Vector Error Correction models where there are many possible restrictions on the cointegration space. We show how this allows the researcher to begin with a single unrestricted model and either do model selection or model averaging in an automatic and computationally efficient manner. We apply our methods to a large UK macroeconomic model.

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Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks

Cited by 40 publications

References 41 publications

Large time-varying parameter VARs

Large time-varying parameter VARs

A DSGE-VAR model for forecasting key South African macroeconomic variables

Stochastic search variable selection in vector error correction models with an application to a model of the UK macroeconomy

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