WALS estimation and forecasting in factor-based dynamic models with an application to Armenia

K. Poghosyan, J.R. Magnus

Research output: Working paperDiscussion paperOther research output

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Abstract

Two model averaging approaches are used and compared in estimating and forecasting dynamic factor models, the well-known BMA and the recently developed WALS. Both methods propose to combine frequentist estimators using Bayesian weights. We apply our framework to the Armenian economy using quarterly data from 2000–2010, and we estimate and forecast real GDP and inflation dynamics.
Original languageEnglish
Place of PublicationTilburg
PublisherEconometrics
Volume2011-054
Publication statusPublished - 2011

Publication series

NameCentER Discussion Paper
Volume2011-054

Fingerprint

Dynamic factor model
Real GDP
Armenia
Estimator
Factors
Inflation dynamics
Model averaging

Keywords

  • Dynamic models
  • Factor analysis
  • Model averaging
  • Monte Carlo
  • Armenia

Cite this

Poghosyan, K., & Magnus, J. R. (2011). WALS estimation and forecasting in factor-based dynamic models with an application to Armenia. (CentER Discussion Paper; Vol. 2011-054). Tilburg: Econometrics.
Poghosyan, K. ; Magnus, J.R. / WALS estimation and forecasting in factor-based dynamic models with an application to Armenia. Tilburg : Econometrics, 2011. (CentER Discussion Paper).
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Poghosyan, K & Magnus, JR 2011 'WALS estimation and forecasting in factor-based dynamic models with an application to Armenia' CentER Discussion Paper, vol. 2011-054, Econometrics, Tilburg.

WALS estimation and forecasting in factor-based dynamic models with an application to Armenia. / Poghosyan, K.; Magnus, J.R.

Tilburg : Econometrics, 2011. (CentER Discussion Paper; Vol. 2011-054).

Research output: Working paperDiscussion paperOther research output

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AB - Two model averaging approaches are used and compared in estimating and forecasting dynamic factor models, the well-known BMA and the recently developed WALS. Both methods propose to combine frequentist estimators using Bayesian weights. We apply our framework to the Armenian economy using quarterly data from 2000–2010, and we estimate and forecast real GDP and inflation dynamics.

KW - Dynamic models

KW - Factor analysis

KW - Model averaging

KW - Monte Carlo

KW - Armenia

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ER -

Poghosyan K, Magnus JR. WALS estimation and forecasting in factor-based dynamic models with an application to Armenia. Tilburg: Econometrics. 2011. (CentER Discussion Paper).