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A model for predicting Finnish household loan stocks

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  • Nyholm, Juho
  • Silvo, Aino

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.

Suggested Citation

  • Nyholm, Juho & Silvo, Aino, 2022. "A model for predicting Finnish household loan stocks," BoF Economics Review 4/2022, Bank of Finland.
    • Handle: RePEc:zbw:bofecr:42022
    as

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    References listed on IDEAS

    as
    1. Kremer, Manfred & Lo Duca, Marco & Holló, Dániel, 2012. "CISS - a composite indicator of systemic stress in the financial system," Working Paper Series 1426, European Central Bank.
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    4. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    5. Claudio Borio & Mathias Drehmann, 2009. "Assessing the risk of banking crises - revisited," BIS Quarterly Review, Bank for International Settlements, March.
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    More about this item

    Keywords

    household debt; Bayesian estimation; conditional forecasting;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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