Coble, David and Pincheira, Pablo (2017): Nowcasting Building Permits with Google Trends.
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Abstract
We propose a useful way to predict building permits in the US, exploiting rich real-time data from web search queries. The time series on building permits is usually considered as a leading indicator of economic activity in the construction sector. Nevertheless, new data on building permits are released with a lag close to two months. Therefore, an accurate now-cast of this leading indicator is desirable. We show that models including Google search queries nowcast and forecast better than our good, not naïve, univariate benchmarks both in-sample and out-of-sample. We also show that our results are robust to different specifications, the use of rolling or recursive windows and, in some cases, to the forecasting horizon. Since Google queries information is free, our approach is a simple and inexpensive way to predict building permits in the United States.
Item Type: | MPRA Paper |
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Original Title: | Nowcasting Building Permits with Google Trends |
English Title: | Nowcasting Building Permits with Google Trends |
Language: | English |
Keywords: | Online Search; Prediction; Forecasting; Time Series; Building Permits; Real Estate; Google Trends. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - General C - Mathematical and Quantitative Methods > C5 - Econometric Modeling C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods F - International Economics > F3 - International Finance F - International Economics > F3 - International Finance > F37 - International Finance Forecasting and Simulation: Models and Applications |
Item ID: | 76514 |
Depositing User: | Pablo Matías Pincheira |
Date Deposited: | 02 Feb 2017 11:57 |
Last Modified: | 27 Sep 2019 17:11 |
References: | Aruoba, S. B., & Diebold, F. X. (2010). Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions. American Economic Review, 100(2), 20–24. Askitas, N. (2015). Trend-Spotting in the Housing Market (IZA Discussion Paper No. 9427). Askitas, N., & Zimmermann, K. (2009). Google Econometrics and Unemployment Forecasting. Askitas, N., & Zimmermann, K. F. (2011). Detecting Mortgage Delinquencies with Google Trends. IZA Discussion Paper 5895. Beracha, E., & Wintoki, M. B. (2013). Forecasting residential real estate price changes from online search activity. Journal of Real Estate Research, 35(3), 283–312. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8. Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., & Weber, I. (2012). Web search queries can predict stock market volumes. PloS One, 7(7), e40014. Carrière-Swallow, Y., & Labbé, F. (2013). Nowcasting with Google Trends in an Emerging Market. Journal of Forecasting, 32(4), 289–298. Chauvet, M., Gabriel, S., & Lutz, C. (2016). Mortgage default risk: New evidence from internet search queries. Journal of Urban Economics, 96(November), 91–111. Chen, S.-S. (2009). Predicting the bear stock market: Macroeconomic variables as leading indicators. Journal of Banking & Finance, 33(2), 211–223. Choi, H., & Varian, H. (2012). Predicting the Present with Google Trends. Economic Record, 88(SUPPL.1), 2–9. Clark, T. E., & McCracken, M. W. (2001). Tests of equal forecast accuracy and encompassing for nested models. Journal of Econometrics, 105(1), 85–110. Clark, T. E., & McCracken, M. W. (2005). Evaluating Direct Multistep Forecasts. Econometric Reviews, 24(4), 369–404. Clark, T. E., & McCracken, M. W. (2013). Evaluating the Accuracy of Forecasts from Vector Autoregressions. In T. Fomby, L. Killian, & A. Murphy (Eds.), Vector Autoregressive Modeling—New Developments and Applications: Essays in Honor of Christopher A. Sims. Clark, T. E., & West, K. D. (2007). Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics, 138(1), 291–311. D’Amuri, F., & Marcucci, J. (2012). The predictive power of Google searches in forecasting unemployment (Bank of Italy Working Paper No. 891). Da, Z., Engelberg, J., & Gao, P. (2015). The Sum of All FEARS Investor Sentiment and Asset Prices. Review of Financial Studies, 28(1), 1–32. DA, Z., ENGELBERG, J., & GAO, P. (2011). In Search of Attention. The Journal of Finance, 66(5), 1461–1499. Das, P., Ziobrowski, A., & Coulson, N. E. (2015). Online Information Search, Market Fundamentals and Apartment Real Estate. The Journal of Real Estate Finance and Economics, 51(4), 480–502. Diebold, F. X., & Mariano, R. S. (1995). Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 13(3), 253–263. Dimpfl, T., & Jank, S. (2016). Can Internet Search Queries Help to Predict Stock Market Volatility? European Financial Management, 22(2), 171–192. Dzielinski, M. (2012). Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, 9(3), 167–175. Estrella, A., & Mishkin, F. S. (1998). Predicting U.S. Recessions: Financial Variables as Leading Indicators. Review of Economics and Statistics, 80(1), 45–61. Giacomini, R., & White, H. (2006). Tests of Conditional Predictive Ability. Econometrica, 74(6), 1545–1578. Giannone, D., Reichlin, L., & Small, D. (2008). Nowcasting: The real-time informational content of macroeconomic data. Journal of Monetary Economics, 55(4), 665–676. Guzmán, G. (2011, January 1). Internet search behavior as an economic forecasting tool: The case of inflation expectations. Journal of Economic and Social Measurement. IOS Press. Harvey, D. S., Leybourne, S. J., & Newbold, P. (1998). Tests for Forecast Encompassing. Journal of Business & Economic Statistics, 16(2), 254–259. Huberty, M. (2015). Can we vote with our tweet? On the perennial difficulty of election forecasting with social media. International Journal of Forecasting, 31(3), 992–1007. Joseph, K., Babajide Wintoki, M., & Zhang, Z. (2011). Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. International Journal of Forecasting, 27(4), 1116–1127. Kearney, M. S., & Levine, P. B. (2015). Media Influences on Social Outcomes: The Impact of MTV’s 16 and Pregnant on Teen Childbearing †. American Economic Review, 105(12), 3597–3632. Kristoufek, L. (2013). Can Google Trends search queries contribute to risk diversification? Scientific Reports, 3, 2713. McLaren, N., & Shanbhogue, R. (2011). Using Internet Search Data as Economic Indicators. Bank of England Quarterly Bulletin, Q2(June 13, 2011). Moat, H. S., Curme, C., Avakian, A., Kenett, D. Y., Stanley, H. E., & Preis, T. (2013). Quantifying Wikipedia Usage Patterns Before Stock Market Moves. Scientific Reports, 3, 1801. Newey, W., & West, K. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelationconsistent covariance matrix. Econometrica, 55(3), 703–708. Oestmann, M., & Bennöhr, L. (2015). Determinants of house price dynamics. What can we learn from search engine data? (No. A15-V3). Beiträge zur Jahrestagung des Vereins für Socialpolitik. Pincheira, P. M., & West, K. D. (2016). A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts. Research in Economics, 70(2), 304–319. Preis, T., Reith, D., & Stanley, H. E. (2010). Complex dynamics of our economic life on different scales: insights from search engine query data. Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, 368(1933), 5707–19. Ripberger, J. T. (2011). Capturing Curiosity: Using Internet Search Trends to Measure Public Attentiveness. Policy Studies Journal, 39(2), 239–259. Smith, G. P. (2012). Google Internet search activity and volatility prediction in the market for foreign currency. Finance Research Letters, 9(2), 103–110. Smith, P. (2016). Google’s MIDAS Touch: Predicting UK Unemployment with Internet Search Data. Journal of Forecasting, n/a-n/a. Strauss, J. (2013). Does housing drive state-level job growth? Building permits and consumer expectations forecast a state’s economic activity. Journal of Urban Economics, 73(1), 77–93. Tefft, N. (2011). Insights on unemployment, unemployment insurance, and mental health. Journal of Health Economics, 30(2), 258–64. Vlastakis, N., & Markellos, R. N. (2012). Information demand and stock market volatility. Journal of Banking & Finance, 36(6), 1808–1821. Vosen, S., & Schmidt, T. (2011). Forecasting private consumption: survey-based indicators vs. Google trends. Journal of Forecasting, 30(6), 565–578. West, K. D. (1996). Asymptotic Inference about Predictive Ability. Econometrica, 64(5), 1067–1084. Wu, L., & Brynjolfsson, E. (2015). The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales. In Economic Analysis of the Digital Economy (pp. 89–118). |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/76514 |