Some Variables are More Worthy Than Others: New Diffusion Index Evidence on the Monitoring of Key Economic Indicators
AbstractCentral banks regularly monitor select financial and macroeconomic variables in order to obtain early indication of the impact of monetary policies. This practice is discussed on the Federal Reserve Bank of New York website, for example, where one particular set of macroeconomic “indicators” is given. In this paper, we define a particular set of “indicators” that is chosen to be representative of the typical sort of variable used in practice by both policy-setters and economic forecasters. As a measure of the “adequacy” of the “indicators”, we compare their predictive content with that of a group of observable factor proxies selected from amongst 132 macroeconomic and financial time series, using the diffusion index methodology of Stock and Watson (2002a,b) and the factor proxy methodology of Bai and Ng (2006a,b) and Armah and Swanson (2010). The variables that we predict are output growth and inflation, two representative variables from our set of indicators that are often discussed when assessing the impact of monetary policy. Interestingly, we find that that indicators are all contained within the set the observable variables that proxy our factors. Our findings, thus, support the notion that a judiciously chosen set of macroeconomic indicators can effectively provide the same macroeconomic policy-relevant information as that contained in a largescale time series dataset. Of course, the large-scale datasets are still required in order to select the key indicator variables or confirm one’s prior choice of key variables. Our findings also suggest that certain yield “spreads” are also useful indicators. The particular spreads that we find to be useful are the difference between Treasury or corporate yields and the federal funds rate. After conditioning on these variables, traditional spreads, such as the yield curve slope and the reverse yield gap are found to contain no additional marginal predictive content. We also find that the macroeconomic indicators (not including spreads) perform best when forecasting inflation in non-volatile time periods, while inclusion of our spread variables improves predictive accuracy in times of high volatility.
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Bibliographic InfoPaper provided by Rutgers University, Department of Economics in its series Departmental Working Papers with number 201115.
Length: 20 pages
Date of creation: 15 May 2011
Date of revision:
Publication status: Published in Applied Financial Economics, 21, 43-60.
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diffusion index; factor; forecast; macroeconometrics; monetary policy; parameter estimation error; proxy; federal reserve bank;
Other versions of this item:
- Nii Ayi Armah & Norman Swanson, 2011. "Some variables are more worthy than others: new diffusion index evidence on the monitoring of key economic indicators," Applied Financial Economics, Taylor & Francis Journals, vol. 21(1-2), pages 43-60.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-12-13 (All new papers)
- NEP-CBA-2011-12-13 (Central Banking)
- NEP-FOR-2011-12-13 (Forecasting)
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- Dean Croushore & Tom Stark, 1999.
"A real-time data set for macroeconomists,"
99-4, Federal Reserve Bank of Philadelphia.
- Jorion, Philippe & Mishkin, Frederic, 1991.
"A multicountry comparison of term-structure forecasts at long horizons,"
Journal of Financial Economics,
Elsevier, vol. 29(1), pages 59-80, March.
- Philippe Jorion & Frederic Mishkin, 1991. "A Multi-Country Comparison of Term Structure Forecasts at Long Horizons," NBER Working Papers 3574, National Bureau of Economic Research, Inc.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- Gerlach, Stefan, 1997.
"The Information Content of the Term Structure: Evidence for Germany,"
Springer, vol. 22(2), pages 161-79.
- Gerlach, Stefan, 1995. "The Information Content of the Term Structure: Evidence for Germany," CEPR Discussion Papers 1264, C.E.P.R. Discussion Papers.
- Stefan Gerlach, 1995. "The information content of the term structure: evidence for Germany," BIS Working Papers 29, Bank for International Settlements.
- Diebold, Francis X & Mariano, Roberto S, 1995.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(3), pages 253-63, July.
- Francis X. Diebold & Robert S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Tom Doan, . "DMARIANO: RATS procedure to compute Diebold-Mariano Forecast Comparison Test," Statistical Software Components RTS00055, Boston College Department of Economics.
- Forni, Mario & Reichlin, Lucrezia, 1995.
"Dynamic Common Factors in Large Cross-Sections,"
CEPR Discussion Papers
1285, C.E.P.R. Discussion Papers.
- Jushan Bai & Serena Ng, 2000.
"Determining the Number of Factors in Approximate Factor Models,"
Boston College Working Papers in Economics
440, Boston College Department of Economics.
- Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003.
"The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting,"
LEM Papers Series
2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2005. "The generalised dynamic factor model: one sided estimation and forecasting," ULB Institutional Repository 2013/10129, ULB -- Universite Libre de Bruxelles.
- Forni M. & Hallin M., 2003. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Computing in Economics and Finance 2003 143, Society for Computational Economics.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2002. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," CEPR Discussion Papers 3432, C.E.P.R. Discussion Papers.
- Kenneth D. West, 1994.
"Asymptotic Inference About Predictive Ability,"
- Sharon Kozicki, 1997. "Predicting real growth and inflation with the yield spread," Economic Review, Federal Reserve Bank of Kansas City, issue Q IV, pages 39-57.
- Robert D. Laurent, 1988. "An interest rate-based indicator of monetary policy," Economic Perspectives, Federal Reserve Bank of Chicago, issue Jan, pages 3-14.
- Jushan Bai & Serena Ng, 2004.
"Evaluating Latent and Observed Factors in Macroeconomics and Financ,"
- Bai, Jushan & Ng, Serena, 2006. "Evaluating latent and observed factors in macroeconomics and finance," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 507-537.
- Bai, Jushan & Ng, Serena, 2007. "Determining the Number of Primitive Shocks in Factor Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 52-60, January.
- Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000.
"The generalised dynamic factor model: identification and estimation,"
ULB Institutional Repository
2013/10143, ULB -- Universite Libre de Bruxelles.
- Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
- Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 1999. "The Generalized Dynamic Factor Model: Identification and Estimation," CEPR Discussion Papers 2338, C.E.P.R. Discussion Papers.
- Estrella, Arturo & Mishkin, Frederic S., 1997. "The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank," European Economic Review, Elsevier, vol. 41(7), pages 1375-1401, July.
- Benjamin M. Friedman & Kenneth N. Kuttner, 1991.
"Why does the paper-bill spread predict real economic activity?,"
Working Paper Series, Macroeconomic Issues
91-16, Federal Reserve Bank of Chicago.
- Benjamin M. Friedman & Kenneth Kuttner, 1993. "Why Does the Paper-Bill Spread Predict Real Economic Activity?," NBER Chapters, in: Business Cycles, Indicators and Forecasting, pages 213-254 National Bureau of Economic Research, Inc.
- Benjamin M. Friedman & Kenneth N. Kuttner, 1994. "Why Does the Paper-Bill Spread Predict Real Economic Activity?," NBER Working Papers 3879, National Bureau of Economic Research, Inc.
- Andrea Nobili, 2005. "Forecasting Output Growth And Inflation In The Euro Area: Are Financial Spreads Useful?," Temi di discussione (Economic working papers) 544, Bank of Italy, Economic Research and International Relations Area.
- Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January.
- McCracken, Michael W., 2007. "Asymptotics for out of sample tests of Granger causality," Journal of Econometrics, Elsevier, vol. 140(2), pages 719-752, October.
- Arturo Estrella & Gikas A. Hardouvelis, 1989.
"The term structure as a predictor of real economic activity,"
8907, Federal Reserve Bank of New York.
- Estrella, Arturo & Hardouvelis, Gikas A, 1991. " The Term Structure as a Predictor of Real Economic Activity," Journal of Finance, American Finance Association, vol. 46(2), pages 555-76, June.
- E. P. Davis & S. G. B. Henry, 1994. "The Use of Financial Spreads as Indicator Variables: Evidence for the U.K. and Germany," IMF Working Papers 94/31, International Monetary Fund.
- Harvey, Campbell R., 1988. "The real term structure and consumption growth," Journal of Financial Economics, Elsevier, vol. 22(2), pages 305-333, December.
- Todd Clark & Michael McCracken, 2005. "Evaluating Direct Multistep Forecasts," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 369-404.
- Plosser, Charles I. & Geert Rouwenhorst, K., 1994. "International term structures and real economic growth," Journal of Monetary Economics, Elsevier, vol. 33(1), pages 133-155, February.
- Davis, E Philip & Fagan, Gabriel, 1997. "Are Financial Spreads Useful Indicators of Future Inflation and Output Growth in EU Countries?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(6), pages 701-14, Nov.-Dec..
- Kihwan Kim & Norman Swanson, 2013. "Diffusion Index Model Specification and Estimation Using Mixed Frequency Datasets," Departmental Working Papers 201315, Rutgers University, Department of Economics.
- Yucel, Eray, 2011. "A Review and Bibliography of Early Warning Models," MPRA Paper 32893, University Library of Munich, Germany.
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