Connectionist-Based Rules Describing the Pass-Through of Individual Goods Prices into Trend Inflation in the United States

This paper examines the inflation ‘‘pass-through’’ problem in American monetary policy, defined as the relationship between changes in the growth rates of individual goods and the subsequent economy-wide rate of growth of consumer prices. Initial relationships are establishedwith Granger causality tests robust to structural breaks. A feedforward artificial neural network (ANN) is used to approximate the functional relationship between selected component subindexes and the headline CPI. Moving beyond the ANN ‘‘black box’’, we illustrate how decision rules can be extracted from the network. © 2012 Elsevier B.V. All rights reserved.

[1]  Rudy Setiono,et al.  Generating Concise Sets of Linear Regression Rules from Artificial Neural Networks , 2002, Int. J. Artif. Intell. Tools.

[2]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[3]  Warren S. Sarle,et al.  Neural Networks and Statistical Models , 1994 .

[4]  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.

[5]  Rudy Setiono,et al.  Extracting -of- Rules from Trained Neural Networks , 2000 .

[6]  D. M. Titterington,et al.  Neural Networks: A Review from a Statistical Perspective , 1994 .

[7]  Paul D. McNelis,et al.  Computational Macroeconomics for the Open Economy , 2008 .

[8]  Kurt Hornik,et al.  Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks , 1990, Neural Networks.

[9]  Olivier J. Blanchard,et al.  The Macroeconomic Effects of Oil Price Shocks: Why are the 2000s so Different from the 1970s? , 2007 .

[10]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[11]  Christopher A. Neilson,et al.  Another Pass-Through Bites the Dust?: Oil Prices and Inflation , 2007 .

[12]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[13]  Michael T. Owyang,et al.  Regime Switching and Monetary Policy Measurement , 2001 .

[14]  Jude Shavlik,et al.  THE EXTRACTION OF REFINED RULES FROM KNOWLEDGE BASED NEURAL NETWORKS , 1993 .

[15]  Hiroshi Tsukimoto,et al.  Extracting rules from trained neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[16]  Jerome A. Feldman,et al.  Connectionist Models and Their Properties , 1982, Cogn. Sci..

[17]  Halbert White,et al.  Learning in Artificial Neural Networks: A Statistical Perspective , 1989, Neural Computation.

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

[19]  I. Musirin,et al.  Optimizing one-hidden layer neural network design using Evolutionary Programming , 2009, 2009 5th International Colloquium on Signal Processing & Its Applications.

[20]  James D. Hamilton.  This is what happened to the oil price-macroeconomy relationship , 1996 .

[21]  James D. Hamilton.  What is an Oil Shock? , 2000 .

[22]  Adnan Haider,et al.  Inflation Forecasting in Pakistan using Artificial Neural Networks , 2007 .

[23]  Athanasios Orphanides,et al.  Robust Monetary Policy Rules with Unknown Natural Rates , 2002 .

[24]  Vincent A. Schmidt,et al.  Connectionist-Based Rules Describing the Pass-Through of Individual Goods Prices into Trend Inflation in the United States , 2011 .

[25]  G. Kendall,et al.  Does Money Matter in Inflation Forecasting? , 2010 .

[26]  K. Judd.  Numerical methods in economics , 1998 .

[27]  Joseph E. Gagnon,et al.  Monetary Policy and Exchange Rate Pass-Through , 2001 .

[28]  Olivier J. Blanchard,et al.  Why are the 2000s so Different from the 1970s? A Structural Interpretation of Changes in the Macroeconomic Effects of Oil Prices , 2009 .

[29]  J. Geweke,et al.  Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns , 2007 .

[30]  Vincent A. Schmidt,et al.  Analyzing Divisia Rules Extracted from a Feedforward Neural Network , 2006, IC-AI.

[31]  Alex Faseruk,et al.  Comparison of the Effectiveness of Option Price Forecasting: Black-Scholes vs. Simple and Hybrid Neural Networks , 2007 .

[32]  Emi Nakamura,et al.  Inflation forecasting using a neural network , 2005 .

[33]  Kenneth S. Rogoff,et al.  Can Exchange Rates Forecast Commodity Prices? , 2008 .

[34]  FUNDAMENTAL ANALYSIS WITH ARTIFICIAL NEURAL NETWORK , 2010 .

[35]  M. Hooker.  Are Oil Shocks Inflationary?: Asymmetric and Nonlinear Specifications versus Changes in Regime , 1999 .

[36]  William D. Nordhaus,et al.  Who’s Afraid of a Big Bad Oil Shock? , 2008 .

[37]  Jeremy Piger.  Does inflation targeting make a difference , 2004 .

[38]  Halbert White,et al.  Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.

[39]  Norman R. Swanson,et al.  A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks , 1995 .

[40]  Sholom M. Weiss,et al.  Predictive data mining - a practical guide , 1997 .

[41]  Nematollaah Shiri,et al.  New supervised multi layer feed forward neural network model to accelarate classification with high accuracy. , 2009 .

[42]  Stephen I. Gallant,et al.  Neural network learning and expert systems , 1993 .

[43]  Khurshid M. Kiani,et al.  Testing Forecast Accuracy of Foreign Exchange Rates: Predictions from Feed Forward and Various Recurrent Neural Network Architectures , 2008 .

[44]  Joachim Diederich,et al.  The truth will come to light: directions and challenges in extracting the knowledge embedded within trained artificial neural networks , 1998, IEEE Trans. Neural Networks.

[45]  Todd E. Clark,et al.  Time Variation in the Inflation Passthrough of Energy Prices , 2009 .

[46]  Frederic S. Mishkin,et al.  One Decade of Inflation Targeting in the World: What Do We Know and What Do We Need to Know? , 2001 .

[47]  Stephen I. Gallant,et al.  Connectionist expert systems , 1988, CACM.

[48]  Wei Li,et al.  Scalable data mining for rules , 1998 .

[49]  Halbert White,et al.  Artificial neural networks: an econometric perspective ∗ , 1994 .

[50]  Athanasios Orphanides,et al.  Robust Monetary Policy with Imperfect Knowledge , 2007, SSRN Electronic Journal.

[51]  Shiu‐Sheng Chen.  Oil Price Pass-Through into Inflation , 2009 .

[52]  P. McNelis.  Neural networks in finance , 2004 .

[53]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[54]  Kermit L. Schoenholtz,et al.  Understanding the Evolving Inflation Process , 2007 .

[55]  M. Watson,et al.  Systematic Monetary Policy and the Effects of Oil Price Shocks , 1997 .

[56]  P. Segal.  Why do oil price shocks no longer shock , 2007 .

[57]  J. Faraway,et al.  Time series forecasting with neural networks: a comparative study using the air line data , 2008 .

[58]  Lutz Kilian,et al.  Do We Really Know That Oil Caused the Great Stagflation? A Monetary Alternative , 2001, NBER macroeconomics annual.

[59]  Bart Baesens,et al.  Using Neural Network Rule Extraction and Decision Tables for Credit - Risk Evaluation , 2003, Manag. Sci..

[60]  Kenneth J. Stewart,et al.  Consumer Price Index Research Series Using Current Methods, 1978-98 , 1999 .

[61]  James D. Hamilton,et al.  Oil Shocks and Aggregate Macroeconomic Behavior: The Role of Monetary Policy: A Comment , 2004 .

[62]  David A. Medler.  A Brief History of Connectionism , 1998 .

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

[64]  L. Kilian.  The Economic Effects of Energy Price Shocks , 2007 .

[65]  Michael T. Owyang,et al.  Monetary Policy in a Markov-Switching Vector Error-Correction Model , 2005 .