Using the yield curve to forecast economic growth
Tóm tắt
This paper finds the yield curve to have a well‐performing ability to forecast the real gross domestic product growth in the USA, compared to professional forecasters and time series models. Past studies have different arguments concerning growth lags, structural breaks, and ultimately the ability of the yield curve to forecast economic growth. This paper finds such results to be dependent on the estimation and forecasting techniques employed. By allowing various interest rates to act as explanatory variables and various window sizes for the out‐of‐sample forecasts, significant forecasts from many window sizes can be found. These seemingly good forecasts may face issues, including persistent forecasting errors. However, by using statistical learning algorithms, such issues can be cured to some extent. The overall result suggests, by scientifically deciding the window sizes, interest rate data, and learning algorithms, many outperforming forecasts can be produced for all lags from one quarter to 3 years, although some may be worse than the others due to the irreducible noise of the data.
Từ khóa
Tài liệu tham khảo
Bauer M. D. &Mertens T. M.(2018).Information in the yield curve about future recessions. (Working Paper 2018‐20).San Francisco CA: Federal Reserve Bank of San Francisco.
Chinn M. D. &Kucko K. J.(2010).The predictive power of the yield curve across countries and time. (Working Paper 16398).Cambridge MA: National Bureau of Economic Research.
Engstrom E. C. &Sharpe S. A.(2018).The near‐term forward yield spread as a leading indicator: A less distorted mirror. (Working Paper 2018‐055).Washington DC: Board of Governors of the Federal Reserve System.
Federal Reserve Bank of Philadelphia(2018).A note to users of data from the Philadelphia Fed's. Retrieved fromhttps://www.philadelphiafed.org/-/media/research-and-data/real-time-center/survey-of-professional-forecasters/spf-documentation.pdf?la=en
Federal Reserve Bank of Philadelphia(2019).Second quarter 2019 Survey of Professional Forecasters. Retrieved fromhttps://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters/2019/survq219
Federal Reserve Bank of Philadelphia(2019b). Survey of Professional Forecasters. Retrieved fromhttps://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters/
Federal Reserve Bank of St. Louis(2019).FRED economic data. Retrieved fromhttps://fred.stlouisfed.org/
Harvey A. C., 1989, Forecasting, structural time series models and the Kalman filter
Harvey A. C., 1993, Time Series Models
Hastie T., 2016, The elements of statistical learning: Data mining, inference and prediction
Ljungqvist L., 2012, Recursive macroeconomic theory
Rudebusch G. D. &Williams J. C.(2008).Forecasting recessions: The puzzle of the enduring power of the yield curve. (Working Paper 2007‐16): Federal Reserve Bank of San Francisco.
Sample I.(2011).Stephen Hawking: “There is no heaven; it's a fairy story”. Retrieved fromhttps://www.theguardian.com/science/2011/may/15/stephen-hawking-interview-there-is-no-heaven
Shumway R. H., 2010, Time series analysis and its applications
Tsay R. S., 2014, Multivariate time series analysis
Vapnik V. N., 1998, Statistical learning theory