IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v31y2015i2p276-285.html
   My bibliography  Save this article

Forecasting residential investment in the United States

Author

Listed:
  • Lunsford, Kurt G.

Abstract

This paper studies models for forecasting residential investment. It includes standard univariate and multivariate models, and proposes an error correction model (ECM) based on the stock-flow relationship of housing starts, completions and units under construction. All models are estimated on real-time data, and the root mean squared prediction errors (RMSPEs) of the models are compared, along with the RMSPEs of the Survey of Professional Forecasters (SPF) and the Federal Reserve’s Greenbook. For the 1981:Q3 to 2013:Q2 sample, the ECM improves upon the competing models, with the largest improvements on the univariate models coming from the current quarter forecasts and those on the multivariate models coming from the multi-step forecasts. Further, the ECM makes modest improvements to the SPF, and performs comparably to the Greenbook from 1981:Q3 to 2007:Q4. Relative to the current state of professional forecasting, the ECM performs best at multi-step forecast horizons and in volatile economic periods.

Suggested Citation

  • Lunsford, Kurt G., 2015. "Forecasting residential investment in the United States," International Journal of Forecasting, Elsevier, vol. 31(2), pages 276-285.
    • Handle: RePEc:eee:intfor:v:31:y:2015:i:2:p:276-285
    DOI: 10.1016/j.ijforecast.2014.07.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207014001411
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2014.07.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Engsted, Tom & Haldrup, Niels, 1999. "Multicointegration in Stock-Flow Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(2), pages 237-254, May.
    2. Edward E. Leamer, 2007. "Housing is the business cycle," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 149-233.
    3. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    4. Regis Barnichon & Christopher J. Nekarda, 2012. "The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(2 (Fall)), pages 83-131.
    5. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    6. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 631-653.
    7. Christopher A. Sims, 2002. "The Role of Models and Probabilities in the Monetary Policy Process," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2), pages 1-62.
    8. Lee, Tae-Hwy, 1992. "Stock-Flow Relationships in U.S. Housing Construction," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 419-430, August.
    9. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    10. Lee, Tae-Hwy, 1996. "Stock Adjustment for Multicointegrated Series," Empirical Economics, Springer, vol. 21(4), pages 633-639.
    11. Granger, C W J & Lee, T H, 1989. "Investigation of Production, Sales and Inventory Relationships Using Multicointegration and Non-symmetric Error Correction Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(S), pages 145-159, Supplemen.
    12. Faust, Jon & Wright, Jonathan H., 2009. "Comparing Greenbook and Reduced Form Forecasts Using a Large Realtime Dataset," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 468-479.
    13. Coulson, N Edward, 1999. "Housing Inventory and Completion," The Journal of Real Estate Finance and Economics, Springer, vol. 18(1), pages 89-105, January.
    14. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    15. Hoffman, Dennis L & Rasche, Robert H, 1996. "Assessing Forecast Performance in a Cointegrated System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 495-517, Sept.-Oct.
    16. Michael W. McCracken, 2011. "Housing's role in a recovery," Economic Synopses, Federal Reserve Bank of St. Louis.
    17. Demiralp, Berna & Gantt, Bonnie B. & Selover, David D., 2011. "Modeling unemployment as an inventory: A multicointegration approach," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 724-737.
    18. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    19. Richard K. Green, 1997. "Follow the Leader: How Changes in Residential and Non‐residential Investment Predict Changes in GDP," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 25(2), pages 253-270, June.
    20. 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.
    21. Granger, Clive W J, 1986. "Developments in the Study of Cointegrated Economic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 48(3), pages 213-228, August.
    22. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    23. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    24. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    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


    Cited by:

    1. Goodness C. Aye & Stephen M. Miller & Rangan Gupta & Mehmet Balcilar, 2016. "Forecasting US real private residential fixed investment using a large number of predictors," Empirical Economics, Springer, vol. 51(4), pages 1557-1580, December.
    2. Kishor, N. Kundan, 2023. "Forecasting House Prices: The Role of Fundamentals, Credit Conditions, and Supply Indicators," MPRA Paper 116819, University Library of Munich, Germany.
    3. Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus, 2021. "Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run," International Journal of Forecasting, Elsevier, vol. 37(2), pages 759-776.
    4. Carsten Juergens & Fabian M. Meyer-Heß & Marcus Goebel & Torsten Schmidt, 2021. "Remote Sensing for Short-Term Economic Forecasts," Sustainability, MDPI, vol. 13(17), pages 1-23, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    2. Fabian Krüger & Todd E. Clark & Francesco Ravazzolo, 2017. "Using Entropic Tilting to Combine BVAR Forecasts With External Nowcasts," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 470-485, July.
    3. Baghestani, Hamid & AbuAl-Foul, Bassam M., 2017. "Comparing Federal Reserve, Blue Chip, and time series forecasts of US output growth," Journal of Economics and Business, Elsevier, vol. 89(C), pages 47-56.
    4. Ahmed, Shamim & Liu, Xiaoquan & Valente, Giorgio, 2016. "Can currency-based risk factors help forecast exchange rates?," International Journal of Forecasting, Elsevier, vol. 32(1), pages 75-97.
    5. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    6. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    7. Pablo Pincheira & Jorge Selaive, 2011. "External imbalance, valuation adjustments and real Exchange rate: evidence of predictability in an emerging economy," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 26(1), pages 107-125, Junio.
    8. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    9. Pincheira, Pablo & Hardy, Nicolas, 2022. "Correlation Based Tests of Predictability," MPRA Paper 112014, University Library of Munich, Germany.
    10. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
    11. Pablo Pincheira Brown, 2022. "A Power Booster Factor for Out-of-Sample Tests of Predictability," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 45(89), pages 150-183.
    12. Jamali, Ibrahim & Yamani, Ehab, 2019. "Out-of-sample exchange rate predictability in emerging markets: Fundamentals versus technical analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 241-263.
    13. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    14. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    15. Rossi, Barbara & Sekhposyan, Tatevik, 2011. "Understanding models' forecasting performance," Journal of Econometrics, Elsevier, vol. 164(1), pages 158-172, September.
    16. Joseph Agyapong, 2021. "Application of Taylor Rule Fundamentals in Forecasting Exchange Rates," Economies, MDPI, vol. 9(2), pages 1-27, June.
    17. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    18. Firmin Doko Tchatoka & Qazi Haque, 2023. "On bootstrapping tests of equal forecast accuracy for nested models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1844-1864, November.
    19. Jian Wang & Jason J. Wu, 2012. "The Taylor Rule and Forecast Intervals for Exchange Rates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(1), pages 103-144, February.
    20. Sarno, Lucio & Thornton, Daniel L & Valente, Giorgio, 2005. "Federal Funds Rate Prediction," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 449-471, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:31:y:2015:i:2:p:276-285. See general information about how to correct material in RePEc.

    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 CitEc recognized a bibliographic reference but did not link an item in RePEc 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.