Macroeconomic forecast accuracy in a data‐rich environment

The performance of six classes of models in forecasting different types of economic series is evaluated in an extensive pseudo out‐of‐sample exercise. One of these forecasting models, regularized data‐rich model averaging (RDRMA), is new in the literature. The findings can be summarized in four points. First, RDRMA is difficult to beat in general and generates the best forecasts for real variables. This performance is attributed to the combination of regularization and model averaging, and it confirms that a smart handling of large data sets can lead to substantial improvements over univariate approaches. Second, the ARMA(1,1) model emerges as the best to forecast inflation changes in the short run, while RDRMA dominates at longer horizons. Third, the returns on the S&P 500 index are predictable by RDRMA at short horizons. Finally, the forecast accuracy and the optimal structure of the forecasting equations are quite unstable over time.

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

[2]  J. Chan Large Bayesian Vector Autoregressions , 2019, Macroeconomic Forecasting in the Era of Big Data.

[3]  Tom Boot,et al.  Forecasting Using Random Subspace Methods , 2017, Journal of Econometrics.

[4]  Claudia Foroni,et al.  Mixed Frequency Models with Ma Components , 2018, Journal of Applied Econometrics.

[5]  Francis X. Diebold,et al.  Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives , 2018, International Journal of Forecasting.

[6]  Pierre Guérin,et al.  Markov-Switching Three-Pass Regression Filter , 2017, Journal of Business & Economic Statistics.

[7]  Jean Boivin,et al.  Série Scientifique Scientific Series 2013 s-11 Dynamic Effects of Credit Shocks in a Data-Rich Environment , 2013 .

[8]  Sendhil Mullainathan,et al.  Machine Learning: An Applied Econometric Approach , 2017, Journal of Economic Perspectives.

[9]  Barbara Rossi,et al.  In-Sample Inference and Forecasting in Misspecified Factor Models , 2016 .

[10]  B. Rossi,et al.  Understanding the Sources of Macroeconomic Uncertainty , 2016 .

[11]  Dalibor Stevanovic,et al.  SELECTION OF THE NUMBER OF FACTORS IN PRESENCE OF STRUCTURAL INSTABILITY: A MONTE CARLO STUDY* , 2016 .

[12]  Matteo Barigozzi,et al.  Non-Stationary Dynamic Factor Models for Large Datasets , 2016, 1602.02398.

[13]  George Kapetanios,et al.  Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting , 2009, Comput. Stat. Data Anal..

[14]  Dalibor Stevanovic,et al.  FACTOR AUGMENTED AUTOREGRESSIVE DISTRIBUTED LAG MODELS WITH MACROECONOMIC APPLICATIONS , 2015 .

[15]  Serena Ng,et al.  Working Paper Series , 2019 .

[16]  Massimiliano Marcellino,et al.  Classical time varying factor‐augmented vector auto‐regressive models—estimation, forecasting and structural analysis , 2015 .

[17]  Davide Pettenuzzo,et al.  Forecasting Macroeconomic Variables Under Model Instability , 2016 .

[18]  Allan Timmermann,et al.  Complete subset regressions with large-dimensional sets of predictors , 2015 .

[19]  D. Stevanovic Common time variation of parameters in reduced-form macroeconomic models , 2015 .

[20]  Michele Lenza,et al.  Prior Selection for Vector Autoregressions , 2012, Review of Economics and Statistics.

[21]  Marco Del Negro,et al.  Dynamic Prediction Pools: An Investigation of Financial Frictions and Forecasting Performance , 2014 .

[22]  Bryan T. Kelly,et al.  The Three-Pass Regression Filter: A New Approach to Forecasting Using Many Predictors , 2014 .

[23]  Emanuele Romanazzo Exchange rate predictability , 2014 .

[24]  Allan Timmermann,et al.  Complete subset regressions , 2013 .

[25]  Frank Schorfheide,et al.  Shrinkage Estimation of High-Dimensional Factor Models with Structural Instabilities , 2013 .

[26]  J. Stock,et al.  Consistent Factor Estimation in Dynamic Factor Models with Structural Instability , 2013 .

[27]  Jean-Marie Dufour,et al.  Factor-Augmented VARMA Models With Macroeconomic Applications , 2013 .

[28]  G. Koop Forecasting with Medium and Large Bayesian VARs , 2013 .

[29]  S. Davis,et al.  Measuring Economic Policy Uncertainty , 2013 .

[30]  Xu Cheng,et al.  Forecasting with Factor-Augmented Regression: A Frequentist Model Averaging Approach , 2012 .

[31]  Dalibor Stevanovi,et al.  An Empirical Study of Credit Shock Transmission in a Small Open Economy , 2012 .

[32]  Todd E. Clark,et al.  Bayesian VARs: Specification Choices and Forecast Accuracy , 2011 .

[33]  Andrew J. Patton,et al.  Forecast Rationality Tests Based on Multi-Horizon Bounds , 2011 .

[34]  Jonathan H. Wright,et al.  Forecasting Inflation , 2011 .

[35]  Norman R. Swanson,et al.  Forecasting Financial and Macroeconomic Variables Using Data Reduction Methods: New Empirical Evidence , 2010 .

[36]  Alexei Onatski,et al.  Factor Analysis of a Large DSGE Model , 2010 .

[37]  Tatevik Sekhposyan,et al.  Understanding Models’ Forecasting Performance , 2010 .

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

[39]  Peter Reinhard Hansen,et al.  The Model Confidence Set , 2010 .

[40]  Massimiliano Marcellino,et al.  Forecasting with Factor-Augmented Error Correction Models , 2010 .

[41]  Antonello D’Agostino,et al.  Macroeconomic Forecasting and Structural Change , 2009, SSRN Electronic Journal.

[42]  D. Giannone,et al.  Large Bayesian vector auto regressions , 2010 .

[43]  Jörg Breitung,et al.  Testing for Structural Breaks in Dynamic Factor Models , 2011, SSRN Electronic Journal.

[44]  Guofu Zhou,et al.  Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy , 2009 .

[45]  Sven Ove Hansson,et al.  Measuring Uncertainty , 2009, Stud Logica.

[46]  J. Bai,et al.  Forecasting economic time series using targeted predictors , 2008 .

[47]  Tatevik Sekhposyan,et al.  Has Models' Forecasting Performance for US Output Growth and Inflation Changed over Time, and When? , 2008 .

[48]  N. Bloom The Impact of Uncertainty Shocks , 2007 .

[49]  M. Hallin,et al.  Determining the Number of Factors in the General Dynamic Factor Model , 2007 .

[50]  J. Stock,et al.  Why Has U.S. Inflation Become Harder to Forecast , 2007 .

[51]  C. De Mol,et al.  Forecasting Using a Large Number of Predictors: Is Bayesian Regression a Valid Alternative to Principal Components? , 2006, SSRN Electronic Journal.

[52]  R. Giacomini,et al.  Detecting and Predicting Forecast Breakdowns , 2006, SSRN Electronic Journal.

[53]  Jean Boivin,et al.  Has Monetary Policy Become More Effective? , 2003, The Review of Economics and Statistics.

[54]  Guillaume Chevillon,et al.  Direct Multi-Step Estimation and Forecasting , 2006 .

[55]  Sydney C. Ludvigson,et al.  The Empirical Risk-Return Relation: A Factor Analysis Approach , 2005 .

[56]  Serena Ng,et al.  Understanding and Comparing Factor-Based Forecasts , 2005 .

[57]  J. Stock,et al.  A Comparison of Direct and Iterated Multistep Ar Methods for Forecasting Macroeconomic Time Series , 2005 .

[58]  Allan Timmermann,et al.  Optimal Forecast Combination Under Regime Switching , 2004 .

[59]  J. Stock,et al.  Combination forecasts of output growth in a seven-country data set , 2004 .

[60]  Serena Ng,et al.  Are more data always better for factor analysis , 2006 .

[61]  Jean Boivin,et al.  Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach , 2003 .

[62]  John W. Galbraith,et al.  Content horizons for univariate time-series forecasts , 2003 .

[63]  J. Stock,et al.  Forecasting Using Principal Components From a Large Number of Predictors , 2002 .

[64]  Marco Lippi,et al.  The Generalized Dynamic Factor Model , 2002 .

[65]  J. Stock,et al.  Macroeconomic Forecasting Using Diffusion Indexes , 2002 .

[66]  Michael P. Clements,et al.  Pooling of Forecasts , 2004 .

[67]  Jianqing Fan,et al.  Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties , 2001 .

[68]  P. Perron,et al.  Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power , 2001 .

[69]  J. Bai,et al.  Determining the Number of Factors in Approximate Factor Models , 2000 .

[70]  S. Satchell,et al.  An assessment of the economic value of non‐linear foreign exchange rate forecasts , 1995 .

[71]  Serena Ng,et al.  Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties , 1996 .

[72]  R. Fildes Forecasting structural time series models and the kalman filter: Andrew Harvey, 1989, (Cambridge University Press), 554 pp., ISBN 0-521-32196-4 , 1992 .

[73]  M. Hashem Pesaran,et al.  A Simple Nonparametric Test of Predictive Performance , 1992 .

[74]  F. Diebold,et al.  Structural change and the combination of forecasts , 1986 .

[75]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .