Macroeconomic and Business Cycle Measurement, Modeling and Forecasting
Aruoba, S.B., Diebold, F.X., Nalewaik, J. Schorfheide, F. and Song, D. (2013), "Improving GDP Measurement: A Measurement-Error Perspective"
We provide a new measure of U.S. GDP growth, obtained by applying optimal signal-extraction techniques to the noisy expenditure-side and income-side GDP estimates. The quarter-by-quarter values of our new measure often differ noticeably from those of the traditional measures. Its dynamic properties differ as well, indicating that the persistence of aggregate output dynamics is stronger than previously thought.
Diebold, F.X. (Revised December 2013), "Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold-Mariano Tests," Manuscript, Department of Economics, University of Pennsylvania.
The Diebold-Mariano (DM) test was intended for comparing forecasts; it has been, and remains, useful in that regard. The DM test was not intended for comparing models. Unfortunately, however, much of the large subsequent literature uses DM-type tests for comparing models, in (pseudo-) out-of-sample environments. In that case, much simpler yet more compelling full-sample model comparison procedures exist; they have been, and should continue to be, widely used. The hunch that (pseudo-) out-of-sample analysis is somehow the "only," or "best," or even a "good" way to provide insurance against in-sample over-fitting in model comparisons proves largely false. On the other hand, (pseudo-) out-of-sample analysis may be useful for learning about comparative historical predictive performance.
Diebold, F.X. (2012), "A Personal Perspective on the Origin(s) and Development of 'Big Data': The Phenomenon, the Term, and and the Discipline," Manuscript, Department of Economics, University of Pennsylvania.
I investigate Big Data, the phenomenon, the term, and the discipline, with emphasis on origins of the term, in industry and academics, in computer science and statistics/econometrics. Big Data the phenomenon continues unabated, Big Data the term is now firmly entrenched, and Big Data the discipline is emerging.
Andersen, T.G., Bollerslev, T., Christoffersen, P.F. and Diebold, F.X. (2012), "Financial Risk Measurement for Financial Risk Management," in G. Constantinedes, M. Harris and Rene Stulz (eds.), Handbook of the Economics of Finance, Elsevier.
We stress a conditional approach at both the portfolio and individual-asset levels, at both high frequencies and business cycle frequencies, with special attention to dimensionality-reduction and regularization methods for "vast" covariance matrices.
Aruoba, S.B., Diebold, F.X., Nalewaik, J. Schorfheide, F. and Song, D. (2012), "Improving GDP Measurement: A Forecast Combination Perspective," in X. Chen and N. Swanson (eds.), Causality, Prediction, and Specification Analysis: Recent Advances and Future Directions, Essays in Honor of Halbert L. White Jr., 1-26.
Two often-divergent U.S. GDP estimates are available, a widely-used expenditure side version GDPE, and a much less widely-used income-side version GDPI . We propose and explore a "forecast combination" approach to combining them. We then put the theory to work, producing a superior combined estimate of GDP growth for the U.S., GDPC. We compare GDPC to GDPE and GDPI, with particular attention to behavior over the business cycle. We discuss several variations and extensions. The bottom line: The U.S. should produce a similarly-combined headline GDP estimate, potentially using the methods introduced in this paper.
Aruoba, S.B., Diebold, F.X., Kose, M.A. and Terrones, M.E. (2011), "Globalization, the Business Cycle, and Macroeconomic Monitoring," in R. Clarida and F.Giavazzi (eds.), NBER International Seminar on Macroeconomics. Chicago: University of Chicago Press, 245-302.
We propose and implement a framework for characterizing and monitoring the global business cycle. Our framework utilizes high-frequency data, allows us to account for a potentially large amount of missing observations, and is designed to facilitate the updating of global activity estimates as data are released and revisions become available. We apply the framework to the G-7 countries and study various aspects of national and global business cycles, obtaining three main results. First, our measure of the global business cycle, the common G-7 real activity factor, explains a significant amount of cross-country variation and tracks the major global cyclical events of the past forty years. Second, the common G-7 factor and the idiosyncratic country factors play different roles at different times in shaping national economic activity. Finally, the degree of G-7 business cycle synchronization among country factors has changed over time.
Diebold, F.X. (2010), "Discussion of Jeremy J. Nalewaik: The Income and Product Side Estimates of U.S. Output Growth," Brookings Papers on Economic Activity, 107-112.
Aruoba, S.B. and Diebold, F.X. (2010), "Real-Time Macroeconomic Monitoring: Real Activity, Inflation, and Interactions," American Economic Review, 100, 20-24.
We sketch a framework for monitoring macroeconomic activity in real-time and push it in new directions. In particular, we focus not only on real activity, which has received most attention to date, but also on in ation and its interaction with real activity. As for the recent recession, we find that (1) it likely ended around July 2009; (2) its most extreme aspects concern a real activity decline that was unusually long but less unusually deep, and an inflation decline that was unusually deep but brief; and (3) its real activity and inflation interactions were strongly positive, consistent with an adverse demand shock.
Diebold, F.X. and Yilmaz, K. (2010), "Macroeconomic Volatility and Stock Market Volatility, Worldwide," in T. Bollerslev, J. Russell and M. Watson (eds.), Volatility and Time Series Econometrics: Essays in Honor of Robert F. Engle. Oxford: Oxford University Press, 97-116.
We study a broad international cross section of stock markets, and we find a clear link between macroeconomic fundamentals and stock market volatilities, with volatile fundamentals translating into volatile stock markets.
Aruoba, S.B., Diebold, F.X. and Scotti, C. (2009), "Real-Time Measurement of Business Conditions," Journal of Business and Economic Statistics, 27, 417-427 (lead article).
We construct a framework for measuring high-frequency economic activity using a variety of stock and flow data observed at mixed frequencies. Specifically, we propose a dynamic factor model that permits exact filtering, and we explore the effcacy of our methods both in an empirical example and in a simulation study.
Click here for real-time updates of the Aruoba-Diebold-Scotti Business Conditions Index, reported by the Federal Reserve Bank of Philadelphia.
Campbell, S.D. and Diebold, F.X. (2009), "Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence," Journal of Business and Economic Statistics, 27, 266-278.
Using half a century of Livingston expected business conditions data, we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a statistically and economically significant counter-cyclical fashion: depressed expected business conditions are associated with high expected excess returns. Moreover, inclusion of expected business conditions in otherwise-standard predictive return regressions substantially reduces the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R squared. Interestingly, one important and recently introduced non-financial predictor, the generalized consumption/wealth ratio ("CAY"), retains its predictive power even when controlling for expected business conditions, which accords with the view that the consumption/wealth ratio plays a role in asset pricing different from and complementary to that of expected business conditions. We argue that time-varying expected business conditions likely captures time-varying risk, while time-varying consumption/wealth captures time-varying risk aversion.
Andersen, T., Bollerslev, T., Diebold, F.X. and Vega, C. (2007), "Real-Time Price Discovery in Stock, Bond and Foreign Exchange Markets," Journal of International Economics, 73, 251-277.
We progress relative to Andersen, Bollerslev, Diebold and Vega (2003, AER) by using a unique dataset to characterize news responses across several markets and countries. Among other things, we show that equity markets react differently to the same news depending on the state of the economy. In particular, good news has negative effects in expansions, and the traditionally-expected positive effects in recessions, which we explain by temporal variation in the competing "cash flow" and "discount rate" effects in equity valuation. We believe that our results, in conjunction with recent work by Boyd, Jagannathan and Hu, make a powerful advance toward answering Barsky's (1989, AER) key question, "Why Don't the Prices of Stocks and Bonds Move Together?": they do move together insofar as the correlation between stock and bond returns is sizeable and important, but it switches sign in expansions vs. recessions, and it therefore appears spuriously small when averaged over the business cycle.
Diebold, F.X., Piazzesi, M. and Rudebusch, G.D. (2005), "Modeling Bond Yields in Finance and Macroeconomics," American Economic Review, 95, 415-420.
New aspects of the macro/finance interface as embodied in yield curve modeling. The tension between current finance approaches that have the theoretically appealing property of freedom from arbitrage but forecast poorly, and traditional macroeconomic approaches that admit arbitrage but forecast well. A step toward resolving the tension: making Nelson-Siegel arbitrage-free.
Diebold, F.X. (2005), “On Robust Monetary Policy with Structural Uncertainty,” in J. Faust, A. Orphanides and D. Reifschneider (eds.), Models and Monetary Policy: Research in the Tradition of Dale Henderson, Richard Porter, and Peter Tinsley. Washington, DC: Board of Governors of the Federal Reserve System, 82-86.
Pitfalls and opportunities associated with the new robust control. Local vs. global robustness, and the dangers of complacency.
Andersen, T., Bollerslev, T., Diebold, F.X. and Vega, C. (2003), "Micro Effects of Macro Announcements: Real- Time Price Discovery in Foreign Exchange," American Economic Review, 93, 38-62.
Diebold, F.X. (2003), "'Big Data' Dynamic Factor Models for Macroeconomic Measurement and Forecasting" (Discussion of Reichlin and Watson papers), in M. Dewatripont, L.P. Hansen and S. Turnovsky (Eds.), Advances in Economics and Econometrics, Eighth World Congress of the Econometric Society. Cambridge: Cambridge University Press, 115-122.
Diebold, F.X. and Li, C. (2006), “Forecasting the Term Structure of Government Bond Yields,” Journal of Econometrics, 130, 37-64.
The classic Nelson-Siegel curve, suitably dynamized and reinterpreted as a modern three-factor model of level, slope and curvature, forecasts bond yields surprisingly well, particularly at horizons between two and four quarters ahead.
Diebold, F.X., Rudebusch, G.D. and Aruoba, B. (2006), “The Macroeconomy and the Yield Curve: A Dynamic Latent Factor Approach,” Journal of Econometrics, 131, 309-338.
Do macroeconomic fundamentals help predict the yield curve? Does the yield curve help predict macroeconomic fundamentals? The answers are yes and yes, although the stronger direction of predictive causality seems to be from the macroeconomy to yields.
Bangia, A. Diebold, F.X., Kronimus, A., Schagen, C., and Schuermann, T. (2002), "Ratings Migration and the Business Cycle, with Application to Credit Portfolio Stress Testing," Journal of Banking and Finance, 26, 445- 474.
Diebold, F.X. and Kilian, L. (2001), "Measuring Predictability: Theory and Macroeconomic Applications," Journal of Applied Econometrics, 16, 657-669.
Diebold, F.X., Tay, A. and Wallis, K. (1999), "Evaluating Density Forecasts of Inflation: The Survey of Professional Forecasters," in R. Engle and H. White (eds.), Festschrift in Honor of C.W.J. Granger, 76-90. Oxford: Oxford University Press.
Christoffersen, P. and Diebold, F.X. (1998), "Cointegration and Long-Horizon Forecasting," Journal of Business and Economic Statistics, 16, 450-458.
Diebold, F.X. (1998), "The Past, Present and Future of Macroeconomic Forecasting," Journal of Economic Perspectives, 12, 175-192.
Diebold, F.X., Ohanian, L. and Berkowitz, J. (1998), "Dynamic Equilibrium Economies: A Framework for Comparing Models and Data," Review of Economic Studies, 65, 433-452.
Diebold, F.X., Neumark, D. and Polsky, D. (1997), "Job Stability in the United States,” Journal of Labor Economics, 15, 206-233.
Diebold, F.X. and Rudebusch, G. (1996), "Measuring Business Cycles: A Modern Perspective," Review of Economics and Statistics, 78, 67-77.
Diebold, F.X. and Mariano, R. (1995), “Comparing Predictive Accuracy,” Journal of Business and Economic Statistics, 13, 253-265.
Diebold, F.X., Lee, J.-H. and Weinbach, G. (1994), "Regime Switching with Time-Varying Transition Probabilities,” in C. Hargreaves (ed.), Nonstationary Time Series Analysis and Cointegration. (Advanced Texts in Econometrics, C.W.J. Granger and G. Mizon, eds.), 283-302. Oxford: Oxford University Press.
Diebold, F.X., Rudebusch, G.D. and Sichel, D. (1993), "Further Evidence on Business Cycle Duration Dependence” (with discussion), in J.H. Stock and M.W. Watson (eds.), Business Cycles, Indicators and Forecasting, 255-284. Chicago: University of Chicago Press for NBER.
Diebold, F.X. and Rudebusch, G.D. (1991), "Forecasting Output with the Composite Leading Index: An Ex Ante Analysis,” Journal of the American Statistical Association, 86, 603-610.
Diebold, F.X. and Rudebusch (1991), "Is Consumption too Smooth? Long Memory and the Deaton Paradox,” Review of Economics and Statistics, 73, 1-9.
Diebold, F.X. and Nason, J. (1990), "Nonparametric Exchange Rate Prediction?,” Journal of International Economics, 28, 315-332.
Diebold, F.X. and Rudebusch, G.D. (1989), "Long Memory and Persistence in Aggregate Output,” Journal of Monetary Economics, 24, 189-209.