Forecasting housing investment

Carlos Cañizares Martínez, Gabe de Bondt and Arne Gieseck

No 2807, Working Paper Series from European Central Bank

Abstract: This study applies a model averaging approach to conditionally forecast housing investment in the largest euro area countries and the euro area. To account for substantial modelling uncertainty, it estimates many vector error correction models (VECMs) using a wide set of short and long-run determinants and selects the most promising specifications based on in-sample and out-of-sample criteria. Our results highlight marked cross-country heterogeneity in the key drivers of housing investment which calls for country-specific housing market policies. A pseudo out-of-sample forecast exercise shows that our model averaging approach beats a battery of ambitious benchmark models, including BVARs, FAVARs, LASSO and Ridge regressions. This suggests that there is ample scope for model averaging tools in forecast exercises, notably as they also help to reduce model uncertainty and can be used to assess forecast uncertainty. JEL Classification: C32, C51, C52, C53, E22

Keywords: Housing investment; model and forecast averaging; Tobin’s Q; VECM (search for similar items in EconPapers)
Date: 2023-04
New Economics Papers: this item is included in nep-eec and nep-ure
Note: 337418
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Journal Article: Forecasting housing investment (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20232807

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