<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Forecasting inflation is of key relevance for central banks, not least because the objective of low and stable inflation is embodied in most central banks’ mandates and the monetary policy transmission mechanism is well known to be subject to long and variable lags. To our best knowledge, central banks around the world use conditional as well as unconditional forecasts for such purposes. Turning to unconditional forecasts, these can be derived on the basis of structural and non-structural models. Among the latter, vector autoregressive (VAR)-models are among the most popular tools.</p><p>This study aims at assessing and deriving a set of unconditional forecasts for euro area inflation based on several specifications which take into account the information content of, inter alia, monetary and credit variables. The models are ordered and based on their in-sample performance and the “best” model is selected accordingly. The results indicate that the inclusion of money and credit variables in the information set improves the quality of the forecasts over a horizon of one to eight quarters. This supports the view that central banks should regularly monitor developments in money and credit.</p></abstract>

Forecasting inflation is of key relevance for central banks, not least because the objective of low and stable inflation is embodied in most central banks’ mandates and the monetary policy transmission mechanism is well known to be subject to long and variable lags. To our best knowledge, central banks around the world use conditional as well as unconditional forecasts for such purposes. Turning to unconditional forecasts, these can be derived on the basis of structural and non-structural models. Among the latter, vector autoregressive (VAR)-models are among the most popular tools.This study aims at assessing and deriving a set of unconditional forecasts for euro area inflation based on several specifications which take into account the information content of, inter alia, monetary and credit variables. The models are ordered and based on their in-sample performance and the “best” model is selected accordingly. The results indicate that the inclusion of money and credit variables in the information set improves the quality of the forecasts over a horizon of one to eight quarters. This supports the view that central banks should regularly monitor developments in money and credit.

Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models

Frankfurt School of Finance and Management

Folia Oeconomica Stetinensia

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Forecasting inflation is of key relevance for central banks, not least because the objective of low and stable inflation is embodied in most central banks’...

Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models

Published Online: Dec 27, 2017

Page range: 19 - 34

Received: Feb 14, 2017

Accepted: Oct 09, 2017

DOI: https://doi.org/10.1515/foli-2017-0016

Keywords
inflation forecasts, euro area, Bayesian VAR

© 2017 Dieter Gerdesmeier et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Forecasting Euro Area Inflation Using Single-Equation and Multivariate VAR–Models

Published Online: Dec 27, 2017

Page range: 19 - 34

Received: Feb 14, 2017

Accepted: Oct 09, 2017

DOI: https://doi.org/10.1515/foli-2017-0016

Keywordsinflation forecasts, euro area, Bayesian VAR

© 2017 Dieter Gerdesmeier et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
inflation forecasts, euro area, Bayesian VAR