Has Models' Forecasting Performance for US Output Growth and Inflation Changed over Time, and When?

We evaluate various models' relative performance in forecasting future US output growth and inflation on a monthly basis. Our approach takes into account the possibility that the models' relative performance can be varying over time. We show that the models' relative performance has, in fact, changed dramatically over time, both for revised and real-time data, and investigate possible factors that might explain such changes. In addition, this paper establishes two empirical stylized facts. Namely, most predictors for output growth lost their predictive ability in the mid-1970s, and became essentially useless in the last two decades. When forecasting inflation, instead, fewer predictors are significant (among which, notably, capacity utilization and unemployment), and their predictive ability significantly worsened around the time of the Great Moderation.

[1]  Tom Stark and Dean Croushore.  Forecasting with a Real-Time Data Set for Macroeconomists , 2001 .

[2]  Athanasios Orphanides and Simon van Norden.  The Reliability of Inflation Forecasts Based on Output Gaps in Real Time , 2001 .

[3]  Barbara Rossi,et al.  OPTIMAL TESTS FOR NESTED MODEL SELECTION WITH UNDERLYING PARAMETER INSTABILITY , 2005, Econometric Theory.

[4]  Dean Croushore,et al.  A real-time data set for macroeconomists , 2001 .

[5]  Kenneth J. Arrow,et al.  Handbooks in economics , 1981 .

[6]  Francis X. Diebold,et al.  Leading economic indicators: Turning point prediction with the composite leading index: An ex ante analysis , 1991 .

[7]  J. Stock,et al.  Phillips Curve Inflation Forecasts , 2008 .

[8]  Todd E. Clark,et al.  Forecasting with Small Macroeconomic VARs in the Presence of Instabilities , 2006 .

[9]  J. Stock,et al.  Interpreting Evidence on Money-Income Causality , 1987 .

[10]  Robert Fildes,et al.  Journal of business and economic statistics 5: Garcia-Ferrer, A. et al., Macroeconomic forecasting using pooled international data, (1987), 53-67 , 1988 .

[11]  Kajal Lahiri,et al.  Leading economic indicators : new approaches and forecasting records , 1991 .

[12]  Rochelle M. Edge,et al.  Learning and Shifts in Long-Run Productivity Growth , 2004 .

[13]  W. Newey,et al.  A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix , 1986 .

[14]  Todd E. Clark,et al.  Tests of Equal Forecast Accuracy and Encompassing for Nested Models , 1999 .

[15]  S. Kozicki.  Predicting real growth and inflation with the yield spread , 1997 .

[16]  Todd E. Clark,et al.  Averaging Forecasts from Vars with Uncertain Instabilities , 2006 .

[17]  W. Newey,et al.  A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelationconsistent Covariance Matrix , 1986 .

[18]  Barbara Rossi,et al.  Recursive Predictability Tests for Real-Time Data , 2005 .

[19]  J. Stock,et al.  Chapter 1 Business cycle fluctuations in us macroeconomic time series , 1999 .

[20]  K. West,et al.  Asymptotic Inference about Predictive Ability , 1996 .

[21]  T. Stark,et al.  Forecasting with a Real-Time Data Set for Macroeconomists , 2001 .

[22]  Halbert White,et al.  Tests of Conditional Predictive Ability , 2003 .

[23]  Frederic S. Mishkin,et al.  What Does the Term Structure Tell Us About Future Inflation? , 1988 .

[24]  Norman R. Swanson,et al.  The Real-Time Predictive Content of Money for Output , 2000 .

[25]  Todd E. Clark,et al.  Using Out-of-Sample Mean Squared Prediction Errors to Test the Martingale Difference Hypothesis , 2004 .

[26]  Simon van Norden,et al.  Série Scientifique Scientific Series the Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time the Reliability of Inflation Forecasts Based on Output Gap Estimates in Real Time* , 2022 .

[27]  Gabriel Perez-Quiros,et al.  Output Fluctuations in the United States: What Has Changed Since the Early 1980s? , 1998 .

[28]  A. Estrella.  Why Does the Yield Curve Predict Output and Inflation? , 2005 .

[29]  Glenn D. Rudebusch,et al.  Forecasting Output with the Composite Leading Index: A Real-Time Analysis , 1991 .

[30]  S. Kozicki,et al.  Rounding Error: A Distorting Influence on Index Data , 2004 .

[31]  Paolo Surico,et al.  (Un)Predictability and Macroeconomic Stability , 2006, SSRN Electronic Journal.

[32]  E. Prescott,et al.  Postwar U.S. Business Cycles: An Empirical Investigation , 1997 .

[33]  C. Granger,et al.  Handbook of Economic Forecasting , 2006 .

[34]  Barbara Rossi,et al.  Forecast comparisons in unstable environments , 2010 .

[35]  A. Rodrigues,et al.  How Stable is the Predictive Power of the Yield Curve? Evidence from Germany and the United States , 2000, Review of Economics and Statistics.

[36]  Dean Croushore,et al.  Forecasting with Real-Time Macroeconomic Data , 2006 .

[37]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[38]  J. Stock,et al.  Forecasting Output and Inflation: The Role of Asset Prices , 2001 .

[39]  Todd E. Clark,et al.  The power of tests of predictive ability in the presence of structural breaks , 2005 .

[40]  J. Stock,et al.  Business Cycle Fluctuations in U.S. Macroeconomic Time Series , 1998 .

[41]  Lutz Kilian,et al.  On the Selection of Forecasting Models , 2003, SSRN Electronic Journal.