WALS Estimation and Forecasting in Factor-based Dynamic Models with an Application to Armenia

Two model averaging approaches are used and compared in estimating and forecasting dynamic factor models, the well-known Bayesian model averaging (BMA) and the recently developed weighted average least squares (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 growth and inflation.

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Year of publication:	2012
Authors:	Poghosyan, Karen ; Magnus, Jan R.
Published in:	International Econometric Review (IER). - Ekonometrik Araştırmalar Derneği, ISSN 1308-8815. - Vol. 4.2012, 1, p. 40-58
Publisher:	Ekonometrik Araştırmalar Derneği
Subject:	Dynamic Models \| Factor Analysis \| Model Averaging \| Monte Carlo \| Armenia

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Extent:	application/pdf
Type of publication:	Article
Classification:	C11 - Bayesian Analysis ; C13 - Estimation ; C52 - Model Evaluation and Testing ; C53 - Forecasting and Other Model Applications ; E52 - Monetary Policy (Targets, Instruments, and Effects) ; E58 - Central Banks and Their Policies
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010684138