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Inflation forecasting using a neural network

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  • Nakamura, Emi

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File URL: http://www.sciencedirect.com/science/article/B6V84-4DVBWHY-4/2/1045fdfc62aab3b34618beed40356981
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Bibliographic Info

Article provided by Elsevier in its journal Economics Letters.

Volume (Year): 86 (2005)
Issue (Month): 3 (March)
Pages: 373-378

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Handle: RePEc:eee:ecolet:v:86:y:2005:i:3:p:373-378

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Web page: http://www.elsevier.com/locate/ecolet

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References

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  1. Brock, W.A., 1995. "A Rational Route to Randomness," Working papers 9530, Wisconsin Madison - Social Systems.
  2. William Barnett & Alfredo Medio & Apostolos Serletis, 2012. "Nonlinear And Complex Dynamics In Economics," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201223, University of Kansas, Department of Economics, revised Sep 2012.
  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.
  4. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  5. 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.
  6. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.
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Cited by:
  1. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," AQR Working Papers 201312, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2013.
  2. Binner, Jane M. & Elger, C. Thomas & Nilsson, Birger & Tepper, Jonathan A., 2006. "Predictable non-linearities in U.S. inflation," Economics Letters, Elsevier, vol. 93(3), pages 323-328, December.
  3. Leonidas Spiliopoulos, 2005. "Can the human mind learn to backward induce? A neural network answer," Game Theory and Information 0505008, EconWPA.
  4. Godarzi, Ali Abbasi & Amiri, Rohollah Madadi & Talaei, Alireza & Jamasb, Tooraj, 2014. "Predicting oil price movements: A dynamic Artificial Neural Network approach," Energy Policy, Elsevier, vol. 68(C), pages 371-382.
  5. María Clara Aristizábal Restrepo, . "Evaluación asimétrica de una red neuronal artificial:Aplicación al caso de la inflación en Colombia," Borradores de Economia 377, Banco de la Republica de Colombia.
  6. Anderson, Richard G. & Binner, Jane M. & Schmidt, Vincent A., 2012. "Connectionist-based rules describing the pass-through of individual goods prices into trend inflation in the United States," Economics Letters, Elsevier, vol. 117(1), pages 174-177.

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