A comparison of time series models for forecasting GDP growth and inflation

Predicting the future evolution of GDP growth and inflation is a central concern in economics. Forecasts are typically produced either from economic theory based models or from simple linear time series models. While a time series model can provide a reasonable benchmark to evaluate the value added of economic theory relative to the pure explanatory power of the past behavior of the variable, recent developments in time series analysis suggest that more sophisticated time series models could provide more serious benchmarks for economic models. In this paper we evaluate whether these complicated time series models can outperform standard linear models for forecasting GDP growth and inflation. We consider a large variety of models and evaluation criteria, using a bootstrap algorithm to evaluate the statistical significance of our results. Our main conclusion is that in general linear time series models can be hardly beaten if they are carefully specified. However, we also identify some important cases where the adoption of a more complicated benchmark can alter the conclusions of economic analyses about the driving forces of GDP growth and inflation.

[1]  James H. Stock,et al.  VAR, ERROR CORRECTION AND PRETEST FORECASTS AT LONG HORIZONS , 2009 .

[2]  C. Sims,et al.  Were there Regime Switches in U.S. Monetary Policy , 2006 .

[3]  Min Wei,et al.  Do macro variables, asset markets, or surveys forecast inflation better? , 2006 .

[4]  Massimiliano Marcellino,et al.  A comparison of Direct and Iterated AR Methods for Forecasting Macroeconomic Series h-Steps Ahead , 2006 .

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

[6]  Halbert White,et al.  Approximate Nonlinear Forecasting Methods , 2006 .

[7]  M. Marcellino,et al.  Modelling and Forecasting Fiscal Variables for the Euro Area , 2005 .

[8]  Min Wei,et al.  Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better? , 2005 .

[9]  Frank Schorfheide,et al.  On the Fit and Forecasting Performance of New Keynesian Models , 2005, SSRN Electronic Journal.

[10]  Mark W. Watson,et al.  Has inflation become harder to forecast , 2005 .

[11]  H. White,et al.  Automatic Block-Length Selection for the Dependent Bootstrap , 2004 .

[12]  A Structural Break in U.S. GDP? , 2003 .

[13]  Jean-Francois Richard,et al.  A Non-Linear Forecasting Model of GDP Growth , 2003 .

[14]  Anindya Banerjee,et al.  Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth? , 2003 .

[15]  Philip Hans Franses,et al.  Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy , 2003 .

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

[17]  J. Stock,et al.  Macroeconomic forecasting in the Euro area: Country specific versus area-wide information , 2003 .

[18]  Dean Croushore,et al.  A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter? , 1999, Review of Economics and Statistics.

[19]  G. Elliott.  Comments on ‘Forecasting with a real-time data set for macroeconomists’ , 2002 .

[20]  Massimiliano Giuseppe Marcellino,et al.  Forecasting Emu Macroeconomic Variables , 2002 .

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

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

[23]  Massimiliano Marcellino,et al.  Fiscal forecasting: The track record of the IMF, OECD and EC , 2001 .

[24]  Olivier J. Blanchard,et al.  The Long and Large Decline in U.S. Output Volatility , 2001 .

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

[26]  T. Sargent,et al.  Evolving Post-World War II U.S. Inflation Dynamics , 2001, NBER Macroeconomics Annual.

[27]  Jeremy Piger,et al.  The Use and Abuse of 'Real-Time' Data in Economic Forecasting , 2000 .

[28]  H. White,et al.  A Reality Check for Data Snooping , 2000 .

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

[30]  Chang‐Jin Kim,et al.  Has the U.S. Economy Become More Stable? A Bayesian Approach Based on a Markov-Switching Model of the Business Cycle , 1999, Review of Economics and Statistics.

[31]  Francis X. Diebold,et al.  Unit-Root Tests Are Useful for Selecting Forecasting Models , 1999 .

[32]  Massimiliano Marcellino,et al.  Some Consequences of Temporal Aggregation in Empirical Analysis , 1999 .

[33]  J. Stock,et al.  A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series , 1998 .

[34]  Margaret Mary McConnell,et al.  Output Fluctuations in the United States: What Has Changed Since the Early 1980s? , 1998 .

[35]  Norman R. Swanson,et al.  A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks , 1997, Review of Economics and Statistics.

[36]  M. Hashem Pesaran,et al.  A floor and ceiling model of US output , 1997 .

[37]  Michael P. Clements,et al.  Multi-Step Estimation For Forecasting , 2009 .

[38]  Timo Teräsvirta,et al.  Modelling Economic Relationships with Smooth Transition Regressions , 1996 .

[39]  A. Timmermann,et al.  Predictability of Stock Returns: Robustness and Economic Significance , 1995 .

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

[41]  J. Stock,et al.  Evidence on Structural Instability in Macroeconomic Time Series Relations , 1994 .

[42]  C. Granger,et al.  Modelling Nonlinear Economic Relationships , 1995 .

[43]  J. Stock,et al.  Efficient Tests for an Autoregressive Unit Root , 1992 .

[44]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[45]  J. Nyblom.  Testing for the Constancy of Parameters over Time , 1989 .

[46]  Q. Vuong.  Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[47]  James D. Hamilton.  A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle , 1989 .

[48]  Francis X. Diebold,et al.  Forecast combination and encompassing: Reconciling two divergent literatures , 1989 .

[49]  D. Hendry,et al.  Econometric Evaluation of Linear Macro-Economic Models , 1986 .

[50]  Jean-Francois Richard,et al.  The Encompassing Principle and Its Application to Testing Non-nested Hypotheses , 1986 .

[51]  John Geweke,et al.  A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series , 1984 .

[52]  M. Pesaran,et al.  TESTING NON-NESTED NONLINEAR REGRESSION MODELS , 1978 .

[53]  Michael D. Geurts,et al.  Time Series Analysis: Forecasting and Control , 1977 .

[54]  Arthur F. Burns,et al.  Measuring Business Cycles. , 1946 .