Advanced Search
MyIDEAS: Login to save this paper or follow this series

Forecasting US Real Private Residential Fixed Investment Using a Large Number of Predictors

Contents:

Author Info

  • Goodness C. Aye

    (University of Pretoria)

  • Rangan Gupta

    (University of Pretoria)

  • Stephen M. Miller

    (University of Nevada, Las Vegas and University of Connecticut)

  • Mehmet Balcilar

    (Eastern Mediterranean University)

Abstract

This paper employs classical bivariate, factor augmented (FA), slab-and-spike variable selection (SSVS)-based, and Bayesian semi-parametric shrinkage (BSS)-based predictive regression models to forecast US real private residential fixed investment over an out-of-sample period from 1983:Q1 to 2011:Q2, based on an in-sample estimates for 1963:Q1 to 1982:Q4. Both large-scale (188 macroeconomic series) and small-scale (20 macroeconomic series) FA, SSVS, and BSS 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-quarters-ahead forecasts and test their significance based on the McCracken (2004, 2007) MSE-F statistic. We find that, on average, the SSVS-Large model provides the best forecasts amongst all the models. We also find that one of the individual regression models, using house for sale (H4SALE) as a predictor, performs best at the four- and eight-quarters-ahead horizons. Finally, we use these two models to predict the relevant turning points of the residential investment, via an ex-ante forecast exercise from 2011:Q3 to 2012:Q4. The SSVS-Large model forecasts the turning points more accurately, although the H4SALE model does better toward the end of the sample. Our results suggest that economy-wide factors, in addition to specific housing market variables, prove important when forecasting in the real estate market.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://web2.uconn.edu/economics/working/2014-10.pdf
File Function: Full text
Download Restriction: no

Bibliographic Info

Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2014-10.

as in new window
Length: 26 pages
Date of creation: May 2014
Date of revision:
Handle: RePEc:uct:uconnp:2014-10

Contact details of provider:
Postal: University of Connecticut 341 Mansfield Road, Unit 1063 Storrs, CT 06269-1063
Phone: (860) 486-4889
Fax: (860) 486-4463
Web page: http://www.econ.uconn.edu/
More information through EDIRC

Related research

Keywords: Private residential investment; predictive regressions; factor-augmented models; Bayesian shrinkage; forecasting;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Jonas D. M. Fisher & Martin Gervais, 2007. "First-time home buyers and residential investment volatility," Working Paper Series WP-07-15, Federal Reserve Bank of Chicago.
  2. Gary Koop & Simon Potter, 2004. "Forecasting in dynamic factor models using Bayesian model averaging," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 550-565, December.
  3. Ilyes Abid & Khaled Guesmi & Olfa Kaabia & Duc Khuong Nguyen, 2014. "Financial Crises and Contagion Effects between the US and OECD Equity Markets," Working Papers 2014-451, Department of Research, Ipag Business School.
  4. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
  5. Koop, Gary & Korobilis, Dimitris, 2009. "UK Macroeconomic Forecasting with Many Predictors: Which Models Forecast Best and When Do They Do So?," SIRE Discussion Papers 2009-40, Scottish Institute for Research in Economics (SIRE).
  6. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-59, April.
  7. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521634809.
  8. KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," CORE Discussion Papers 2011021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  9. Dimitris Korobilis, 2012. "Bayesian forecasting with highly correlated predictors," Working Papers 2012_12, Business School - Economics, University of Glasgow.
  10. Baghestani, Hamid, 2011. "Federal Reserve and private forecasts of growth in investment," Journal of Economics and Business, Elsevier, vol. 63(4), pages 290-305, July.
  11. Bertrand Candelon & Jameel Ahmed & Stefan Straetmans, 2014. "Predicting and Capitalizing on Stock Market Bears in the U.S," Working Papers 2014-409, Department of Research, Ipag Business School.
  12. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  13. Stelios Bekiros & Alessia Paccagnini, 2014. "Forecasting the US Economy with a Factor-Augmented Vector Autoregressive DSGE model," Working Papers 2014-183, Department of Research, Ipag Business School.
  14. Irfan Akbar Kazi & Hakimzadi Wagan, 2014. "Are emerging markets exposed to contagion from U.S.: Evidence from stock and sovereign bond markets," Working Papers 2014-058, Department of Research, Ipag Business School.
  15. Stephen M. Miller & Luis F. Martins & Rangan Gupta, 2014. "A Time-Varying Approach of the US Welfare Cost of Inflation," Working papers 2014-11, University of Connecticut, Department of Economics.
  16. Karen E. Dynan & Douglas W. Elmendorf & Daniel E. Sichel, 2005. "Can financial innovation help to explain the reduced volatility of economic activity?," Finance and Economics Discussion Series 2005-54, Board of Governors of the Federal Reserve System (U.S.).
  17. McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
  18. Furkan Emirmahmutoglu & Nicholas Apergis & Beatrice D. Simo-Kengne & Tsangyao Chang & Rangan Gupta, 2014. "Causal relationship between asset prices and output in the US: Evidence from state-level panel Granger causality test," Working Papers 201411, University of Pretoria, Department of Economics.
  19. Mohamed Arouri & Duc Khuong Nguyen & Kuntara Pukthuanthong, 2014. "Diversification benefits and strategic portfolio allocation across asset classes: The case of the US markets," Working Papers 2014-294, Department of Research, Ipag Business School.
  20. Heni Boubaker & Nadia Sghaier, 2014. "On the dynamic dependence between US and other developed stock markets: An extreme-value time-varying copula approach," Working Papers 2014-281, Department of Research, Ipag Business School.
  21. Dunson, David B. & Herring, Amy H. & Engel, Stephanie M., 2008. "Bayesian Selection and Clustering of Polymorphisms in Functionally Related Genes," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 534-546, June.
  22. Goodness C. Aye & Rangan Gupta & Stephen M. Miller & Mehmet Balcilar, 2014. "Forecasting US Real Private Residential Fixed Investment Using a Large Number of Predictors," Working papers 2014-10, University of Connecticut, Department of Economics.
  23. Richard K. Green, 1996. "Follow the Leader: How Changes In Residential and Non-Residential Investment Predict Changes in GDP," Wisconsin-Madison CULER working papers 96-05, University of Wisconsin Center for Urban Land Economic Research.
  24. Luci Ellis & Laura Berger-Thomson, 2004. "Housing Construction Cycles and Interest Rates," Econometric Society 2004 Australasian Meetings 335, Econometric Society.
  25. Jonathan McCarthy & Richard W. Peach, 2002. "Monetary policy transmission to residential investment," Economic Policy Review, Federal Reserve Bank of New York, issue May, pages 139-158.
  26. Edward E. Leamer, 2007. "Housing is the business cycle," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 149-233.
  27. Joe Peek & James A. Wilcox, 2006. "Housing, Credit Constraints, and Macro Stability: The Secondary Mortgage Market and Reduced Cyclicality of Residential Investment," American Economic Review, American Economic Association, vol. 96(2), pages 135-140, May.
  28. Frédérick Demers, 2005. "Modelling and Forecasting Housing Investment: The Case of Canada," Working Papers 05-41, Bank of Canada.
  29. repec:ipg:wpaper:201417 is not listed on IDEAS
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Beatrice D. Simo-Kengne & Stephen M. Miller & Rangan Gupta, 2013. "Evolution of Monetary Policy in the US: The Role of Asset Prices," Working Papers 201343, University of Pretoria, Department of Economics.
  2. Vasilios Plakandaras & Rangan Gupta & Periklis Gogas & Theophilos Papadimitriou, 2014. "Forecasting the U.S. Real House Price Index," Working Papers 201418, University of Pretoria, Department of Economics.
  3. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2014. "Forecasting US Real Private Residential Fixed Investment Using a Large Number of Predictors," Working Papers 2014-465, Department of Research, Ipag Business School.
  4. Furkan Emirmahmutoglu & Nicholas Apergis & Beatrice D. Simo-Kengne & Tsangyao Chang & Rangan Gupta, 2014. "Causal relationship between asset prices and output in the US: Evidence from state-level panel Granger causality test," Working Papers 2014-466, Department of Research, Ipag Business School.
  5. Wendy Nyakabawo & Stephen M. Miller & Mehmet Balcilar & Sonali Das & Rangan Gupta, 2014. "Temporal Causality between House Prices and Output in the U. S.: A Bootstrap Rolling-Window Approach," Working Papers 2014-476, Department of Research, Ipag Business School.
  6. Mohamed Arouri & Shawkat Hammoudeh & Fredj Jawadi & Duc Khuong Nguyen, 2014. "Financial Linkages between U.S. Sector Credit Default Swaps Markets," Working Papers 2014-553, Department of Research, Ipag Business School.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:uct:uconnp:2014-10. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Kasey Kniffin).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.