Forecasting housing investment

Arbeitspapier

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.

Object Details

• ISBN: 978-92-899-6070-0
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: ECB Working Paper ; No. 2807

Classification and Topics

• Classification: Wirtschaft ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Model Construction and Estimation ; Model Evaluation, Validation, and Selection ; Forecasting Models; Simulation Methods ; Investment; Capital; Intangible Capital; Capacity
• Subject: Housing investment ; model and forecast averaging ; Tobin's Q ; VECM

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Cañizares Martínez, Carlos ; de Bondt, Gabe J. ; Gieseck, Arne
• Event: Veröffentlichung
• (who): European Central Bank (ECB)
• (where): Frankfurt a. M.
• (when): 2023

Further information

• DOI: doi:10.2866/531765
• Handle: http://hdl.handle.net/10419/278483
• Last update: 10.03.2025, 11:44 AM CET