Inflation and Unemployment Forecasting with Genetic Support Vector Regression

In this paper a hybrid genetic algorithm–support vector regression (GA-SVR) model in economic forecasting and macroeconomic variable selection is introduced. The proposed algorithm is applied to the task of forecasting US inflation and unemployment. GA-SVR genetically optimizes the SVR parameters and adapts to the optimal feature subset from a feature space of potential inputs. The feature space includes a wide pool of macroeconomic variables that might affect the two series under study. The forecasting performance of GA-SVR is benchmarked with a random walk model, an autoregressive moving average model, a moving average convergence/divergence model, a multi-layer perceptron, a recurrent neural network and a genetic programming algorithm. In terms of our results, GA-SVR outperforms all benchmark models and provides evidence on which macroeconomic variables can be relevant predictors of US inflation and unemployment in the specific period under study. Copyright © 2014 John Wiley & Sons, Ltd.

[1]  Kyoung-jae Kim.  Artificial neural networks with evolutionary instance selection for financial forecasting , 2006, Expert Syst. Appl..

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

[3]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[4]  Chew Lian Chua,et al.  A latent variable approach to forecasting the unemployment rate , 2012 .

[5]  Georgios Sermpinis,et al.  Forecasting and trading the EUR/USD exchange rate with Gene Expression and Psi Sigma Neural Networks , 2012, Expert Syst. Appl..

[6]  T. Sargent,et al.  Drifts and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S. , 2003 .

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

[8]  Zijun Wang.  Directed graphs, information structure and forecast combinations: an empirical examination of US unemployment rates , 2010 .

[9]  Francesco Ravazzolo,et al.  Real-Time Inflation Forecasting in a Changing World , 2013 .

[10]  Paul Newbold,et al.  Testing the equality of prediction mean squared errors , 1997 .

[11]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[12]  Shu-Heng Chen,et al.  Genetic Algorithms and Genetic Programming in Computational Finance , 2002 .

[13]  Lijuan Cao,et al.  c-ascending support vector machines for financial time series forecasting , 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..

[14]  L. Kilian,et al.  How Useful Is Bagging in Forecasting Economic Time Series? A Case Study of U.S. Consumer Price Inflation , 2008 .

[15]  Bernhard Schölkopf,et al.  Shrinking the Tube: A New Support Vector Regression Algorithm , 1998, NIPS.

[16]  Hans-Georg Zimmermann,et al.  Forecasting with Recurrent Neural Networks: 12 Tricks , 2012, Neural Networks: Tricks of the Trade.

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[18]  Sveriges Riksbank,et al.  Finding Good Predictors for Inflation: A Bayesian Model Averaging Approach , 2002 .

[19]  G. C. Tiao,et al.  Journal of the American Statistical Association Forecasting the U.s. Unemployment Rate Forecasting the U.s. Unemployment Rate , 2022 .

[20]  E. Olmedo.  Forecasting Spanish Unemployment Using Near Neighbour and Neural Net Techniques , 2013, Computational Economics.

[21]  A. Atkeson,et al.  Are Phillips Curves Useful for Forecasting Inflation , 2001 .

[22]  Philip Rothman,et al.  Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts , 2008 .

[23]  Olivier Darné,et al.  Are Disaggregate Data Useful for Factor Analysis in Forecasting French GDP? , 2009 .

[24]  Zehang Sun,et al.  Object detection using feature subset selection , 2004, Pattern Recognit..

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

[26]  Arnold F. Shapiro,et al.  A Hitchhiker’s guide to the techniques of adaptive nonlinear models , 2000 .

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

[28]  So Young Sohn,et al.  Support vector machines for default prediction of SMEs based on technology credit , 2010, Eur. J. Oper. Res..

[29]  P. McNelis,et al.  Forecasting Inflation with Thick Models and Neural Networks , 2004 .

[30]  Saeed Moshiri,et al.  Unemployment Variation Over the Business Cycles: A Comparison of Forecasting Models , 2004 .

[31]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[32]  J. Stock,et al.  Why Has U.S. Inflation Become Harder to Forecast? , 2006 .

[33]  Kui-wai Li.  A study on the volatility forecast of the US housing market in the 2008 crisis , 2012 .

[34]  Mark W. Watson,et al.  Generalized Shrinkage Methods for Forecasting Using Many Predictors , 2012 .

[35]  John R. Koza,et al.  Evolution of Iteration in Genetic Programming , 1996, Evolutionary Programming.

[36]  Anatoly Zhigljavsky,et al.  Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis , 2013 .

[37]  Jack Sklansky,et al.  A note on genetic algorithms for large-scale feature selection , 1989, Pattern Recognit. Lett..

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

[39]  Ming Zhang,et al.  Artificial Higher Order Neural Networks for Economics and Business , 2008 .

[40]  P. Lisboa,et al.  Business Applications of Neural Networks:The State-of-the-Art of Real-World Applications , 2000 .

[41]  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.

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

[43]  Theodore B. Trafalis,et al.  Support vector machine for regression and applications to financial forecasting , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[44]  Chien-Feng Huang,et al.  A hybrid stock selection model using genetic algorithms and support vector regression , 2012, Appl. Soft Comput..

[45]  S. Sathiya Keerthi,et al.  Evaluation of simple performance measures for tuning SVM hyperparameters , 2003, Neurocomputing.

[46]  J. Murphy.  Technical Analysis of the Financial Markets , 1999 .

[47]  G. Koop,et al.  Forecasting In ation Using Dynamic Model Averaging , 2009 .

[48]  Paolo Tenti,et al.  Forecasting Foreign Exchange Rates Using Recurrent Neural Networks , 1996, Appl. Artif. Intell..

[49]  Giorgio E. Primiceri,et al.  Inflation-Gap Persistence in the U.S , 2008 .

[50]  Philip Rothman,et al.  Forecasting Asymmetric Unemployment Rates , 1998 .

[51]  Alexander J. Smola,et al.  Learning with kernels , 1998 .