Haider, Adnan and Hanif, Muhammad Nadeem (2007): Inflation Forecasting in Pakistan using Artificial Neural Networks.
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An artificial neural network (hence after, 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 conventional univariate time series forecasting models such as AR(1) and ARIMA based models and observed that RMSE of ANN based forecasts is much less than the RMSE of forecasts based on AR(1) and ARIMA models. At least by this criterion forecast based on ANN are more precise.
|Item Type:||MPRA Paper|
|Original Title:||Inflation Forecasting in Pakistan using Artificial Neural Networks|
|Keywords:||artificial neural network, forecasting, inflation|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E31 - Price Level; Inflation; Deflation
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications
|Depositing User:||Adnan Haider Adnan|
|Date Deposited:||16. Apr 2009 23:35|
|Last Modified:||15. Feb 2013 14:47|
Barnett, William A., Alfredo Medio, and Apostolos Serletis, (2003), Nonlinear and Complex Dynamics in Economics, Working Paper.
Beale, R., Jackson, T., (1990), Neural Computing: An introduction, Adam Hilger, Bristol England.
Brock, William A. and Cars H. Hommes, (1997), A Rational Route to Randomness, Econometrica, , 65 (5), pp 1059–1095.
Chen, Xiaohong, J. Racine, and N. Swanson, (2001), Semiparametric ARX Neural Network Models with an Application to Forecasting Inflation,” IEEE Transactions on Neural Networks, 12, pp 674–683.
Fernandez-Rodriguez, Fernando, Christian Gonzalez-Martel, and Simon Sosvilla-Rivero, (2000), On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market, Economics Letters, 69 (1), pp 89–94.
Gonzalez Steven, (2000), Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models, Finance Canada Working Paper 2000-07.
Greg Tkacz, Sarah Hu, (1999), Forecasting GDP growth using Artificial Neural Networks, Working Paper 99-3, Bank of Canada.
Hornik, K., M. Stinchcombe, and H. White, (1989), Multilayer feedforward networks are universal approximators, Neural Networks, 2, pp 359–366.
Haykin, Simon, (1994), Neural Networks: A comprehensive foundation, Macmillian College Publishing Company, New York. Kartalopoulos, Stamations V., (1996), Understanding Neural Networks and Fuzzy Logic: Basic concepts and applications, IEEE Press, New York.
Lebaron, B. and A.S. Weigend, (1998), A bootstrap evaluation of the effect of data splitting on financial time series, IEEE Transactions on Neural Networks, 9 (1), pp 213–220.
Marek Hlavacek, Michael Konak and Josef Cada, (2005), The application of structured feedforward neural networks to the modeling of daily series of currency in circulation. Working paper series 11, Czech National Bank.
McCracken, M. W. and K. D. West, (2001), Inference about Predictive Ability, in Michael Clements and David Hendry, eds., A Companion to Economic Forecasting, Oxford, U.K. Blackwell Publishers.
Moshiri, Saeed and Norman Cameron, (2000), Econometrics Versus ANN Models in Forecasting Inflation, Journal of Forecasting, February Issue, 19.
Nakamura, Emi, (2005), Inflation forecasting using a neural network, Working Paper, Havard University, Littauer Center, Cambridge MA.
Refenes, A. P. and H. White, (1998), Neural Networks and Financial Economics, International Journal of Forecasting, 6 (17).
Stock, James H. and Mark W. Watson, (1998), A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series, NBER Working Paper 6607, 7
Swanson, Norman R. and Halbert White, (1997), A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks, The Review of Economics and Statistics, November Issue, 79 (4), pp 540–550.
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Inflation Forecasting using Artificial Neural Networks. (deposited 30. May 2008 06:49)
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