Forecasting US real private residential fixed investment using a large number of predictors
Empirical Economics, ISSN: 0377-7332, Vol: 51, Issue: 4, Page: 1557-1580
2016
- 7Citations
- 4Usage
- 11Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations7
- Citation Indexes5
- CrossRef5
- Policy Citations2
- Policy Citation2
- Usage4
- Abstract Views4
- Captures11
- Readers11
- 11
Article Description
This paper employs classical bivariate, slab-and-spike variable selection, Bayesian semi-parametric shrinkage, and factor-augmented predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983Q1 to 2005Q4, based on in-sample estimates for 1963Q1–1982Q4. Both large-scale (188 macroeconomic series) and small-scale (20 macroeconomic series) slab-and-spike variable selection, and Bayesian semi-parametric shrinkage, and factor-augmented predictive regressions, as well as 20 bivariate regression models, capture the influence of fundamentals in forecasting residential investment. We evaluate the ex post out-of-sample forecast performance of the 26 models using the relative average mean square error for one-, two-, four-, and eight-quarter-ahead forecasts and test their significance based on the McCracken (2004, J Econom 140:719–752, 2007) mean-square-error F statistic. We find that, on average, the slab-and-spike variable selection and Bayesian semi-parametric shrinkage models with 188 variables provides the best forecasts among all the models. Finally, we use these two models to predict the relevant turning points of the residential investment, via an ex ante forecast exercise from 2006Q1 to 2012Q4. The 188 variable slab-and-spike variable selection and Bayesian semi-parametric shrinkage models perform quite similarly in their accuracy of forecasting the turning points. Our results suggest that economy-wide factors, in addition to specific housing market variables, prove important when forecasting in the real estate market.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84954169295&origin=inward; http://dx.doi.org/10.1007/s00181-015-1059-z; http://link.springer.com/10.1007/s00181-015-1059-z; https://digitalscholarship.unlv.edu/econ_fac_articles/6; https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=1005&context=econ_fac_articles; https://dx.doi.org/10.1007/s00181-015-1059-z; https://link.springer.com/article/10.1007/s00181-015-1059-z
Springer Science and Business Media LLC
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