Bayesian Vector Autoregressive Procedure for Forecasting Swiss Economy: BVARs methodology for forecasting real GDP and inflation growth in Switzerland using asset prices - Brossura

Rey, Lucien

 
9783659831669: Bayesian Vector Autoregressive Procedure for Forecasting Swiss Economy: BVARs methodology for forecasting real GDP and inflation growth in Switzerland using asset prices
Brossura
ISBN 10: 3659831662 ISBN 13: 9783659831669
Casa editrice: LAP LAMBERT Academic Publishing, 2016

Sinossi

This book adopts a methodology for forecasting real GDP and inflation growth in Switzerland. Introduced by Litterman (1986), this study builds forecast models for the Swiss economy. Firstly, autodistributed lagged models (ARDL) are computed, followed by the framework of Bayesian models. Bayesian vector autoregressive models (BVARs) strongly rely on the VAR framework, however they allow a better exploitation of all the information available. Using the data from 1980, out-of-sample forecasts have been computed from 2000 to 2014. Suggesting four categories that variables are grouped into, this study finds that Bayesian VAR models improve forecast errors, principally for inflation. An extension of the model is performed using foreign data, which further reduces forecast errors. Asset prices are found to contain valuable information in forecasting real GDP and, particularly in predicting inflation growth. However, BVARs cannot substitute for a complete structural method for economic policies analysis. Nevertheless, these models tend to produce good forecasts performance and thus, should be used as complementary benchmark forecasting models for the Swiss National Bank.

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L'autore

Lucien was born in 1988 in Chermignon, southern French-part of Switzerland. After his Swiss military service, Lucien moved to Manchester (UK) studying a BSc (Hons) in Economics & Finance followed by an MSc in Finance at the Manchester Business School. Lucien is currently living in London working for a financial software, data and media company.

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Editore
LAP LAMBERT Academic Publishing
Data di pubblicazione
2016
Lingua
Inglese
ISBN 10 
3659831662
ISBN 13 
9783659831669
Rilegatura
Copertina flessibile
Numero di pagine
76
Contatto del produttore
non disponibile
Persona responsabile
non disponibile