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Inflation Forecasting using Artificial Neural Networks

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Author Info
Bukhari, S. Adnan H. A. S. Bukhari
Hanif, Muhammad Nadeem

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Abstract

An artificial neural network (ANN) is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. In previous two decades, ANN applications in economics and finance; for such tasks as pattern reorganization, and time series forecasting, have dramatically increased. Many central banks use forecasting models based on ANN methodology for predicting various macroeconomic indicators, like inflation, GDP Growth and currency in circulation etc. In this paper, we have attempted to forecast monthly YoY inflation for Pakistan by using ANN for FY08 on the basis of monthly data of July 1993 to June 2007. We also compare the forecast performance of the ANN model with that of univariate AR(1) model and observed that RMSE of ANN based forecasts is much less than the RMSE of forecasts based on AR(1) model. At least by this criterion forecast based on ANN are more precise.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 8898.

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Date of creation: 13 Jul 2007
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Handle: RePEc:pra:mprapa:8898

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Related research
Keywords: artificial neural network forecasting inflation

Find related papers by JEL classification:
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation

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References listed on IDEAS
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  1. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March. [Downloadable!] (restricted)
  2. William A. Brock & Cars H. Hommes, 1997. "A Rational Route to Randomness," Econometrica, Econometric Society, vol. 65(5), pages 1059-1096, September.
  3. Steven Gonzalez, . "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models," Working Papers-Department of Finance Canada 2000-07, Department of Finance Canada. [Downloadable!]
  4. Marek Hlavacek & Michael Konak & Josef Cada, 2005. "The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation," Working Papers 2005/11, Czech National Bank, Research Department. [Downloadable!]
  5. repec:att:wimass:1920114 is not listed on IDEAS
  6. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October. [Downloadable!] (restricted)
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  7. Tkacz, Greg & Hu, Sarah, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Working Papers 99-3, Bank of Canada. [Downloadable!]
  8. William A. Barnett & Alfredo Medio & Apostolos Serletis, 1997. "Nonlinear and Complex Dynamics in Economics," Econometrics 9709001, EconWPA. [Downloadable!]
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