Evaluating a Global Vector Autoregression for Forecasting

Global vector autoregressions (GVARs) have several attractive features: multiple potential channels for the international transmission of macroeconomic and financial shocks, a standardized economically appealing choice of variables for each country or region examined, systematic treatment of long-run properties through cointegration analysis, and flexible dynamic specification through vector error correction modeling. Pesaran et al. (2009) generate and evaluate forecasts from a paradigm GVAR with 26 countries, based on Dées, di Mauro et al. (2007). The current paper empirically assesses the GVAR in Dées, di Mauro et al. (2007) with impulse indicator saturation (IIS)—a new generic procedure for evaluating parameter constancy, which is a central element in model-based forecasting. The empirical results indicate substantial room for an improved, more robust specification of that GVAR. Some tests are suggestive of how to achieve such improvements.

[1]  Kuldeep Kumar.  The Methodology and Practice of Econometrics: a Festschrift in Honour of David F. Hendry , 2012 .

[2]  Lutz Kilian,et al.  Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries , 2012 .

[3]  J. Lindé,et al.  Asymmetric Shocks in a Currency Union with Monetary and Fiscal Handcuffs , 2010, NBER International Seminar on Macroeconomics.

[4]  Neil R. Ericsson.  Improving Global Vector Autoregressions , 2011 .

[5]  Rochelle M. Edge,et al.  How Useful Are Estimated DSGE Model Forecasts for Central Bankers? , 2010 .

[6]  S. Dées,et al.  Stress-testing euro area corporate default probabilities using a global macroeconomic model☆ , 2010 .

[7]  Tim Bollerslev,et al.  Volatility and Time Series Econometrics , 2010 .

[8]  Jennifer L. Castle,et al.  Nowcasting from disaggregates in the face of location shifts , 2010 .

[9]  D. Hendry,et al.  An Automatic Test of Super Exogeneity , 2010 .

[10]  Jennifer L. Castle,et al.  Nowcasting is not Just Contemporaneous Forecasting , 2009, National Institute Economic Review.

[11]  H. Varian,et al.  Predicting the Present with Google Trends , 2009 .

[12]  M. Pesaran,et al.  Infinite Dimensional VARs and Factor Models , 2009, SSRN Electronic Journal.

[13]  Jurgen A. Doornik,et al.  Encompassing and Automatic Model Selection , 2008 .

[14]  David F. Hendry,et al.  Automatic selection of indicators in a fully saturated regression , 2008, Comput. Stat..

[15]  Til Schuermann,et al.  Forecasting Economic and Financial Variables with Global VARs , 2007 .

[16]  Bent Nielsen,et al.  An Analysis of the Indicator Saturation Estimator as a Robust Regression Estimator , 2008 .

[17]  Michael P. Clements,et al.  Explaining Forecast Failure in Macroeconomics , 2007 .

[18]  M. Pesaran,et al.  Long Run Macroeconomic Relations in the Global Economy , 2007, SSRN Electronic Journal.

[19]  I. Vansteenkiste,et al.  International trade, technological shocks and spillovers in the labour market: a GVAR analysis of the US manufacturing sector , 2007 .

[20]  F. Smets,et al.  Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach , 2007 .

[21]  Mark P. Taylor,et al.  What if the UK or Sweden had joined the Euro in 1999? An empirical evaluation using a global VAR , 2007 .

[22]  David F. Hendry,et al.  Robustifying forecasts from equilibrium-correction systems , 2006 .

[23]  Anthony Garratt,et al.  Global and National Macroeconometric Modelling: A Long-Run Structural Approach , 2006 .

[24]  M. Hashem Pesaran,et al.  Global and National Macroeconometric Modelling , 2006 .

[25]  Ron Smith,et al.  Macroeconometric Modelling with a Global Perspective , 2006, SSRN Electronic Journal.

[26]  M. Pesaran,et al.  Exploring the International Linkages of the Euro Area: A Global VAR Analysis , 2006, SSRN Electronic Journal.

[27]  Katarina Juselius,et al.  The Cointegrated VAR Model: Methodology and Applications , 2006 .

[28]  Jennifer L. Castle.  Automatic Econometric Model Selection using PcGets. , 2006 .

[29]  Luca Guerrieri,et al.  Sigma: A New Open Economy Model for Policy Analysis , 2005 .

[30]  Laura Mayoral.  The Persistence of Inflation in OECD Countries: A Fractionally Integrated Approach , 2005 .

[31]  D. Hendry.  Econometric methodology: a personal perspective , 2005 .

[32]  H. White,et al.  A consistent model selection procedure based on m-testing , 2005 .

[33]  David F. Hendry,et al.  The Properties of Automatic Gets Modelling , 2004 .

[34]  M. Pesaran,et al.  Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model , 2004 .

[35]  K. Hoover,et al.  Truth and Robustness in Cross-Country Growth Regressions , 2000 .

[36]  K. Hoover,et al.  Improving on ‘ Data mining reconsidered ’ by , 2000 .

[37]  David F. Hendry,et al.  Computer Automation of General-to-Specific Model Selection Procedures , 2001 .

[38]  David F. Hendry,et al.  Improving on "Data mining reconsidered" by K.D. Hoover and S.J. Perez , 1999 .

[39]  S. Johansen,et al.  Asymptotic Inference on Cointegrating Rank in Partial Systems , 1998 .

[40]  Neil R. Ericsson,et al.  Exogeneity, Cointegration, and Economic Policy Analysis , 1998 .

[41]  Kevin D. Hoover,et al.  Data mining reconsidered: encompassing and the general-to-specific approach to specification search , 1997 .

[42]  James G. MacKinnon,et al.  Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration , 1996 .

[43]  P. Perron,et al.  Estimating and testing linear models with multiple structural changes , 1995 .

[44]  D. Andrews.  Tests for Parameter Instability and Structural Change with Unknown Change Point , 1993 .

[45]  K. Juselius.  Domestic and foreign effects on prices in an open economy: The case of Denmark , 1992 .

[46]  Søren Johansen,et al.  Cointegration in partial systems and the efficiency of single-equation analysis , 1992 .

[47]  Michael P. Clements,et al.  FORECASTING ECONOMIC TIME SERIES , 2000, Econometric Theory.

[48]  David M. Grether,et al.  Forecasting Non-Stationary Economic Time Series , 1966 .

[49]  G. Chow.  Tests of equality between sets of coefficients in two linear regressions (econometrics voi 28 , 1960 .