Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/261371 
Year of Publication: 
2022
Series/Report no.: 
BoF Economics Review No. 4/2022
Publisher: 
Bank of Finland, Helsinki
Abstract: 
We propose a new Bayesian VAR model for forecasting household loan stocks in Finland. The model is designed to work as a satellite model of a larger DSGE model for the Finnish economy, the Aino 2.0 model. The forecasts produced with the BVAR model can be conditioned on projections of several macro variables obtained from the Aino 2.0 model. We study several specifications for the set of variables and lags included in the BVAR, and evaluate their out-of-sample forecast accuracy with root mean squared forecasting errors (RMSFEs). We then select a preferred specification that performs best in predicting the loan stocks over forecast horizons ranging from one to twelve quarters ahead. The model adds to the existing toolkit of forecast models currently in use at the Bank of Finland and improves our understanding of household debt trends in Finland.
Subjects: 
household debt
Bayesian estimation
conditional forecasting
JEL: 
C11
C32
E37
Persistent Identifier of the first edition: 
Document Type: 
Research Report

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