Hännikäinen, Jari (2016): When does the yield curve contain predictive power? Evidence from a datarich environment.

PDF
MPRA_paper_70489.pdf Download (447kB)  Preview 
Abstract
This paper analyzes the predictive content of the level, slope and curvature of the yield curve for U.S. real activity in a datarich environment. We find that the slope contains predictive power, but the level and curvature are not successful leading indicators. The predictive power of each of the yield curve factors fluctuates over time. The results show that economic conditions matter for the predictive ability of the slope. In particular, inflation persistence emerges as a key variable that affects the predictive content of the slope. The slope tends to forecast output growth better when inflation is highly persistent.
Item Type:  MPRA Paper 

Original Title:  When does the yield curve contain predictive power? Evidence from a datarich environment 
Language:  English 
Keywords:  yield curve; factor model; datarich environment; forecasting; macroeconomic regimes; conditional predictive ability 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods ; Simulation Methods C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C55  Large Data Sets: Modeling and Analysis E  Macroeconomics and Monetary Economics > E4  Money and Interest Rates > E43  Interest Rates: Determination, Term Structure, and Effects E  Macroeconomics and Monetary Economics > E4  Money and Interest Rates > E44  Financial Markets and the Macroeconomy E  Macroeconomics and Monetary Economics > E4  Money and Interest Rates > E47  Forecasting and Simulation: Models and Applications E  Macroeconomics and Monetary Economics > E5  Monetary Policy, Central Banking, and the Supply of Money and Credit > E52  Monetary Policy 
Item ID:  70489 
Depositing User:  Dr. Jari Hännikäinen 
Date Deposited:  06 Apr 2016 05:01 
Last Modified:  06 Apr 2016 06:30 
References:  Abdymomunov, A. (2013). Predicting output using the entire yield curve. Journal of Macroeconomics, 37, 333–344. AguiarConraria, L., Martins, M. M. F. & Soares, M. J (2012). The yield curve and the macroeconomy across time and frequencies. Journal of Economic Dynamics and Control, 36, 1950–1970. Andrews, D. W. K. & Chen, H.Y. (1994). Approximately medianunbiased estimation of autoregressive models. Journal of Business and Economic Statistics, 12, 187–204. Ang, A., Piazzesi, M. & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131, 359–403. Baele, L., Bekaert, G., Cho, S., Inghelbrecht, K. & Moreno, A. (2015). Macroeconomic regimes. Journal of Monetary Economics, 70, 51–71. Benati, L. (2008). Investigating inflation persistence across monetary regimes. Quarterly Journal of Economics, 123, 1005–1060. Benati, L. & Goodhart, C. (2008). Investigating timevariation in the marginal predictive power of the yield spread. Journal of Economic Dynamics and Control, 32, 1236–1272. Bernanke, B. S. (1990). On the predictive power of interest rates and interest rate spreads. New England Economic Review, 51–68. Bernanke, B. S. & Blinder, A. S. (1992). The federal funds rate and the channels of monetary transmission. American Economic Review, 82, 901–921. Bernanke, B. S. & Boivin, J. (2003). Monetary policy in a datarich environment. Journal of Monetary Economics, 50, 525–546. Blanchard, O. J. & Simon, J. A. (2001). The long and large decline in U.S. output volatility. Brookings Papers on Economic Activity, 135–164. Bordo, M. D. & Haubrich, J. G. (2004). The yield curve, recessions and the credibility of the monetary regime: long run evidence 1875–1997. NBER Working Paper, no. 10431. Bordo, M. D. & Haubrich, J. G. (2008a). Forecasting with the yield curve; level, slope, and output 1875–1997. Economics Letters, 99, 48–50. Bordo, M. D. & Haubrich, J. G. (2008b). The yield curve as a predictor of growth: longrun evidence, 1875–1997. Review of Economics and Statistics, 90, 182–185. Bordo, M. D. & Schwartz, A. J. (1999). Monetary policy regimes and economic performance: The historical record. In J. B. Taylor, & M. Woodford (Eds), Handbook of Macroeconomics, vol. 1, NorthHolland, Amsterdam. 149–234. Clark, T. E. (2006). Disaggregate evidence on the persistence of consumer price inflation. Journal of Applied Econometrics, 21, 563–587. Clements, M. P. (2015). Realtime factor model forecasting and the effects of instability. Computational Statistics and Data Analysis, forthcoming. Croushore, D. (2011). Frontiers of realtime data analysis. Journal of Economic Literature, 49, 72–100. D’Agostino, A., Giannone, D. & Surico, P. (2006). (Un)predictability and macroeconomic stability. European Central Bank Working Paper, no. 605. D’Agostino, A. & Surico, P. (2012). A century of inflation forecasts. Review of Economics and Statistics, 94, 1097–1106. Diebold, F. X. & Li, C. (2006). Forecasting the term structure of government bond yields. Journal of Econometrics, 130, 337–364. Diebold, F. X. & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business and Economic Statistics, 13, 253–265. Dotsey, M., Fujita, S. & Stark, T. (2015). Do Phillips curves conditionally help to forecast inflation? Federal Reserve Bank of Philadelphia Working Paper, no. 1516. Estrella, A. & Hardouvelis, G. A. (1991). The term structure as a predictor of real economic activity. Journal of Finance, 46, 555–576. Estrella, A., Rodrigues, A. P. & Schich, S. (2003). How stable is the predictive power of the yield curve? Evidence from Germany and the United States. Review of Economics and Statistics, 85, 629–644. Faust, J., Gilchrist, S., Wright, J. H. & Zakrajsek, E. (2013). Credit spreads as predictors of realtime economic activity: a Bayesian modelaveraging approach. Review of Economics and Statistics, 95, 1501–1519. Gertler, M. & Lown, C. S. (1999). The information in the highyield bond spread for the business cycle: evidence and some implications. Oxford Review of Economic Policy, 15, 132–150. Giacomini, R. & Rossi, B. (2006). How stable is the forecasting performance of the yield curve for output growth? Oxford Bulletin of Economics and Statistics, 68, 783–795. Giacomini, R. & Rossi, B. (2010). Forecast comparisons in unstable environments. Journal of Applied Econometrics, 25, 595–620. Giacomini, R. & White, H. (2006). Tests of conditional predictive ability. Econometrica, 74, 1545–1578. Gurkaynak, R. S., Sack, B. & Wright, J. H. (2007). The U.S. treasury yield curve: 1961 to the present. Journal of Monetary Economics, 54, 2291–2304. Hamilton, J. D. & Kim, D. H. (2002). A reexamination of the predictability of the yield spread for real economic activity. Journal of Money, Credit, and Banking, 34, 340–360. Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22, 305–333. Hännikäinen, J. (2015). Zero lower bound, unconventional monetary policy and indicator properties of interest rate spreads. Review of Financial Economics, 26, 47–54. Lamla, M. J. & Maag, T. (2012). The role of media for inflation forecast disagreement of households and professional forecasters. Journal of Money, Credit and Banking, 44, 1325–1350. Luciani, M. (2014). Largedimensional dynamic factor models in realtime: a survey. Working Paper, Universit´e libre de Bruxelles. Marques, C. R. (2005). Inflation persistence: facts or artefacts? Bank of Portugal Economic Bulletin, 69–79. McCracken, M. W. & Ng, S. (2015). FREDMD: A monthly database for macroeconomic research. Journal of Business and Economic Statistics, forthcoming. Mody, A. & Taylor, M. P. (2003). The highyield spread as a predictor of real economic activity: evidence of a financial accelerator for the United States. IMF Staff Papers, 50, 373–402. Nelson, C. R. & Siegel, A. F. (1987). Parsimonious modeling of yield curves. Journal of Business, 60, 473–489. Newey, W. K. & West, K. D. (1987). A simple, positive semidefinite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55, 703–708. Ng, S. & Wright, J. H. (2013). Facts and challenges from the Great Recession for forecasting and macroeconomic modeling. Journal of Economic Literature, 51, 1120–1154. Pivetta, F. & Reis, R. (2007). The persistence of inflation in the United States. Journal of Economic Dynamics and Control, 31, 1326–1358. Rossi, B. (2013). Advances in forecasting under instability. In G. Elliott, & A. Timmermann (Eds), Handbook of Economic Forecasting, vol. 2, NorthHolland, Amsterdam. 1203–1324. Rossi, B. & Sekhposyan, T. (2011). Have economic models’ forecasting performance for US output growth and inflation changed over time, and when? International Journal of Forecasting, 26, 808–835. Stock, J. H. & Watson, M. W. (2002a). Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association, 97, 1167–1179. Stock, J. H. & Watson, M. W. (2002b). Macroeconomic forecasting using diffusion indexes. Journal of Business and Economic Statistics, 20, 147–162. Stock, J. H. & Watson, M. W. (2003). Forecasting output and inflation: the role of asset prices. Journal of Economic Literature, 41, 788–829. Stock, J. H. & Watson, M. W. (2011). Dynamic factor models. In M. P. Clements, & D. F. Hendry (Eds), Oxford Handbook of Economic Forecasting, Oxford: Oxford University Press. 35–60. Wheelock, D. C. & Wohar, M. E. (2009). Can the term spread predict output growth and recessions? A survey of the literature. Federal Reserve Bank of St. Louis Review, 91, 419–440. Wright, J. H. (2011). Term premia and inflation uncertainty: Empirical evidence from an international panel dataset. American Economic Review, 101, 1514–1534. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/70489 