IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v37y2005i6p665-680.html
   My bibliography  Save this article

A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia

Author

Listed:
  • Jane Binner
  • Rakesh Bissoondeeal
  • Thomas Elger
  • Alicia Gazely
  • Andrew Mullineux

Abstract

Linear models reach their limitations in applications with nonlinearities in the data. In this paper new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. It is also investigated whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that non-linear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a non-linear framework.

Suggested Citation

  • Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2005. "A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 665-680.
  • Handle: RePEc:taf:applec:v:37:y:2005:i:6:p:665-680
    DOI: 10.1080/0003684052000343679
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/0003684052000343679
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gorr, Wilpen L. & Nagin, Daniel & Szczypula, Janusz, 1994. "Comparative study of artificial neural network and statistical models for predicting student grade point averages," International Journal of Forecasting, Elsevier, vol. 10(1), pages 17-34, June.
    2. Balkin, Sandy D. & Ord, J. Keith, 2000. "Automatic neural network modeling for univariate time series," International Journal of Forecasting, Elsevier, vol. 16(4), pages 509-515.
    3. Church, Keith B. & Curram, Stephen P., 1996. "Forecasting consumers' expenditure: A comparison between econometric and neural network models," International Journal of Forecasting, Elsevier, vol. 12(2), pages 255-267, June.
    4. Johansen, Soren, 1992. "Determination of Cointegration Rank in the Presence of a Linear Trend," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 383-397, August.
    5. JØrgen Wolters & Helmut LØtkepohl, 1998. "A money demand system for German M3," Empirical Economics, Springer, vol. 23(3), pages 371-386.
    6. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    7. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    8. de Brouwer, Gordon & Ericsson, Neil R, 1998. "Modeling Inflation in Australia," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 433-449, October.
    9. Perman, Roger & Scouller, John, 1999. "Business Economics," OUP Catalogue, Oxford University Press, number 9780198775249.
    10. Stracca, Livio, 2001. "Does liquidity matter? Properties of a synthetic divisia monetary aggregate in the euro area," Working Paper Series 79, European Central Bank.
    11. William Barnett & Ping Chen, 2016. "Economic Theory as a Generator of Measurable Attractors," Mondes en développement, De Boeck Université, vol. 0(3), pages 171-171.
    12. Geraint Johnes, 2000. "Up Around the Bend: Linear and nonlinear models of the UK economy compared," International Review of Applied Economics, Taylor & Francis Journals, vol. 14(4), pages 485-493.
    13. William Barnett, 2005. "Monetary Aggregation," Macroeconomics 0503017, University Library of Munich, Germany.
    14. Hendry, David F & Doornik, Jurgen A, 1994. "Modelling Linear Dynamic Econometric Systems," Scottish Journal of Political Economy, Scottish Economic Society, vol. 41(1), pages 1-33, February.
    15. Barnett, William A., 1980. "Economic monetary aggregates an application of index number and aggregation theory," Journal of Econometrics, Elsevier, vol. 14(1), pages 11-48, September.
    16. Granger, C. W. J., 1981. "Some properties of time series data and their use in econometric model specification," Journal of Econometrics, Elsevier, vol. 16(1), pages 121-130, May.
    17. Drake, L. & Mullineux, A., 1995. "One Divisa Money for Europe?," Discussion Papers 95-04, Department of Economics, University of Birmingham.
    18. Jurgen A. Doornik & David F. Hendry & Bent Nielsen, 1998. "Inference in Cointegrating Models: UK M1 Revisited," Journal of Economic Surveys, Wiley Blackwell, vol. 12(5), pages 533-572, December.
    19. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    20. Nag, Ashok K & Mitra, Amit, 2002. "Forecasting Daily Foreign Exchange Rates Using Genetically Optimized Neural Networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(7), pages 501-511, November.
    21. Pantula, Sastry G., 1989. "Testing for Unit Roots in Time Series Data," Econometric Theory, Cambridge University Press, vol. 5(2), pages 256-271, August.
    22. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-389, June.
    23. Jane M. Binner & Alicia M. Gazely & Shu‐Heng Chen & Bin‐Tzong Chie, 2004. "Financial Innovation and Divisia Money in Taiwan: Comparative Evidence from Neural Network and Vector Error‐Correction Forecasting Models," Contemporary Economic Policy, Western Economic Association International, vol. 22(2), pages 213-224, April.
    24. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    25. Luca Stanca, 1999. "Asymmetries and nonlinearities in Italian macroeconomic fluctuations," Applied Economics, Taylor & Francis Journals, vol. 31(4), pages 483-491.
    26. Hendry, David F, 1980. "Econometrics-Alchemy or Science?," Economica, London School of Economics and Political Science, vol. 47(188), pages 387-406, November.
    27. Joseph Plasmans & William Verkooijen & Hennie Daniels, 1998. "Estimating structural exchange rate models by artificial neural networks," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 541-551.
    28. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
    29. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    30. James G. MacKinnon, 1990. "Critical Values for Cointegration Tests," Working Paper 1227, Economics Department, Queen's University.
    31. LeSage, James P, 1990. "A Comparison of the Forecasting Ability of ECM and VAR Models," The Review of Economics and Statistics, MIT Press, vol. 72(4), pages 664-671, November.
    32. Monterola, Christopher, et al, 2002. "Accurate Forecasting of the Undecided Population in a Public Opinion Poll," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(6), pages 435-449, September.
    33. A. M. Gazely & J. M. Binner, 2000. "The application of neural networks to the Divisia index debate: evidence from three countries," Applied Economics, Taylor & Francis Journals, vol. 32(12), pages 1607-1615.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ryadh M. Alkhareif & William Barnett, 2012. "Divisia Monetary Aggregates for the GCC Countries," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201209, University of Kansas, Department of Economics, revised Aug 2012.
    2. Rakesh K. Bissoondeeal & Michail Karoglou & Alicia M. Gazely, 2011. "Forecasting The Uk/Us Exchange Rate With Divisia Monetary Models And Neural Networks," Scottish Journal of Political Economy, Scottish Economic Society, vol. 58(1), pages 127-152, February.
    3. A. Malliaris & Mary Malliaris, 2013. "Are oil, gold and the euro inter-related? Time series and neural network analysis," Review of Quantitative Finance and Accounting, Springer, vol. 40(1), pages 1-14, January.
    4. Makram El-Shagi & Kiril Tochkov, 2021. "Divisia Monetary Aggregates for Russia: Money Demand, GDP Nowcasting, and the Price Puzzle," CFDS Discussion Paper Series 2021/1, Center for Financial Development and Stability at Henan University, Kaifeng, Henan, China.
    5. repec:ipg:wpaper:2014-471 is not listed on IDEAS
    6. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
    7. M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
    8. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
    9. López Menéndez, Ana Jesús & Pérez Suárez, Rigoberto, 2017. "Forecasting Performance and Information Measures. Revisiting the M-Competition /Evaluación de Predicciones y Medidas de Información. Reexamen de la M-Competición," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 299-314, Mayo.
    10. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk‐Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642, September.
    11. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    12. E. Balatskiy V. & M. Yurevich A. & Е. Балацкий В. & М. Юревич А., 2018. "Прогнозирование инфляции: практика использования синтетических процедур // Inflation Forecasting: The Practice of Using Synthetic Procedures," Мир новой экономики // The world of new economy, Финансовый университет при Правительстве Российской Федерации // Financial University under The Governtment оf The Russian Federation, vol. 12(4), pages 20-31.
    13. Michael Dietrich, 2006. "Neural networks and the evolution of firms and industries: An application to UK SIC34 and SIC72," Working Papers 2006007, The University of Sheffield, Department of Economics, revised May 2006.
    14. Tea Šestanović & Josip Arnerić, 2021. "Neural network structure identification in inflation forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 62-79, January.
    15. Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018. "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series 99, State Bank of Pakistan, Research Department.
    16. J. M. Binner & R. K. Bissoondeeal & A. W. Mullineux, 2005. "A composite leading indicator of the inflation cycle for the Euro area," Applied Economics, Taylor & Francis Journals, vol. 37(11), pages 1257-1266.
    17. Patrick T. Kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2016. "Forecasting South African inflation using non-linearmodels: a weighted loss-based evaluation," Applied Economics, Taylor & Francis Journals, vol. 48(26), pages 2412-2427, June.
    18. Emil Kraft & Dogan Keles & Wolf Fichtner, 2020. "Modeling of frequency containment reserve prices with econometrics and artificial intelligence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1179-1197, December.
    19. Farzan Aminian & E. Suarez & Mehran Aminian & Daniel Walz, 2006. "Forecasting Economic Data with Neural Networks," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 71-88, August.
    20. Yaya, OlaOluwa S & Ogbonna, Ephraim A & Furuoka, Fumitaka & Gil-Alana, Luis A., 2019. "A new unit root analysis for testing hysteresis in unemployment," MPRA Paper 96621, University Library of Munich, Germany.
    21. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    22. Marcos Álvarez-Díaz & Rangan Gupta, 2015. "Forecasting the US CPI: Does Nonlinearity Matter?," Working Papers 201512, University of Pretoria, Department of Economics.
    23. Bi-Huei Tsai, 2017. "Predicting the competitive relationships of industrial production between Taiwan and China using Lotka–Volterra model," Applied Economics, Taylor & Francis Journals, vol. 49(25), pages 2428-2442, May.
    24. S. DeVicerte & P. Alvarez & J. Perez & C. Caso, 2008. "Does currency crisis identification matter?," Applied Financial Economics, Taylor & Francis Journals, vol. 18(5), pages 387-395.
    25. Rakesh Bissoondeeal & Michail Karoglou & Andy Mullineux, 2014. "Breaks in the UK Household Sector Money Demand Function," Manchester School, University of Manchester, vol. 82, pages 47-68, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jane Binner & Rakesh Bissoondeeal & Thomas Elger & Alicia Gazely & Andrew Mullineux, 2004. "Vector autoregressive models versus neural networks in forecasting: an application to Euro-inflation and divisia money," Money Macro and Finance (MMF) Research Group Conference 2003 5, Money Macro and Finance Research Group.
    2. Binner, Jane & Elger, Thomas, 2002. "The UK Personal Sector Demand for Risky Money," Working Papers 2002:9, Lund University, Department of Economics.
    3. Elger Thomas & Binner Jane M., 2004. "The UK Household Sector Demand for Risky Money," The B.E. Journal of Macroeconomics, De Gruyter, vol. 4(1), pages 1-22, March.
    4. Rakesh K. Bissoondeeal & Michail Karoglou & Alicia M. Gazely, 2011. "Forecasting The Uk/Us Exchange Rate With Divisia Monetary Models And Neural Networks," Scottish Journal of Political Economy, Scottish Economic Society, vol. 58(1), pages 127-152, February.
    5. Kirstin Hubrich & Helmut Lutkepohl & Pentti Saikkonen, 2001. "A Review Of Systems Cointegration Tests," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 247-318.
    6. Neil R. Ericsson & James G. MacKinnon, 2002. "Distributions of error correction tests for cointegration," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 285-318, June.
    7. Ericsson, Neil R & Hendry, David F & Mizon, Grayham E, 1998. "Exogeneity, Cointegration, and Economic Policy Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 370-387, October.
    8. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    9. Yaya, OlaOluwa S & Ogbonna, Ephraim A & Furuoka, Fumitaka & Gil-Alana, Luis A., 2019. "A new unit root analysis for testing hysteresis in unemployment," MPRA Paper 96621, University Library of Munich, Germany.
    10. Carsten Trenkler*, 2005. "The Effects of Ignoring Level Shifts on Systems Cointegration Tests," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 89(3), pages 281-301, August.
    11. Sunil Sharma & Neil R. Ericsson, 1998. "Broad money demand and financial liberalization in Greece," Empirical Economics, Springer, vol. 23(3), pages 417-436.
    12. Antonio E. Noriega & Manuel Ramos-Francia & Cid Alonso Rodríguez-Pérez, 2015. "Money demand estimations in Mexico and of its stability 1986-2010, as well as some examples of its uses," Working Papers 2015-13, Banco de México.
    13. Lütkepohl, Helmut, 1999. "Vector autoregressive analysis," SFB 373 Discussion Papers 1999,31, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    14. Fatma Zeren & Levent Korap, 2010. "A Cost-based Empirical Model of the Aggregate Price Determination for the Turkish Economy: A Multivariate Cointegration Approach," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 57(2), pages 173-188, June.
    15. Lütkepohl, Helmut, 1999. "Vector autoregressions," SFB 373 Discussion Papers 1999,4, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    16. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    17. Hjelm, Goran & Johansson, Martin W., 2005. "A Monte Carlo study on the pitfalls in determining deterministic components in cointegrating models," Journal of Macroeconomics, Elsevier, vol. 27(4), pages 691-703, December.
    18. Kouretas, Georgios P. & Yannopoulos, Andreas, 2006. "Dynamic modelling of trade union behaviour: Evidence from the Greek manufacturing sector," Economic Modelling, Elsevier, vol. 23(2), pages 316-338, March.
    19. Reimers, Hans-Eggert, 2002. "Analysing Divisia Aggregates for the Euro Area," Discussion Paper Series 1: Economic Studies 2002,13, Deutsche Bundesbank.
    20. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:v:37:y:2005:i:6:p:665-680. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.