A Test for Serial Dependence Using Neural Networks
AbstractTesting serial dependence is central to much of time series econometrics. A number of tests that have been developed and used to explore the dependence properties of various processes. This paper builds on recent work on nonparametric tests of independence. We consider a fact that characterises serially dependent processes using a generalisation of the autocorrelation function. Using this fact we build dependence tests that make use of neural network based approximations. We derive the theoretical properties of our tests and show that they have superior power properties. Our Monte Carlo evaluation supports the theoretical findings. An application to a large dataset of stock returns illustrates the usefulness of the proposed tests.
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Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 609.
Date of creation: Oct 2007
Date of revision:
Independence; Neural networks; Strict stationarity; Bootstrap; S&P500;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-10-20 (All new papers)
- NEP-CMP-2007-10-20 (Computational Economics)
- NEP-ECM-2007-10-20 (Econometrics)
- NEP-ETS-2007-10-20 (Econometric Time Series)
- NEP-ICT-2007-10-20 (Information & Communication Technologies)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- George Kapetanios & Andrew P. Blake, 2007. "Boosting Estimation of RBF Neural Networks for Dependent Data," Working Papers 588, Queen Mary, University of London, School of Economics and Finance.
- Marcelo Fernandes & Breno Neri, 2010.
"Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes,"
Taylor & Francis Journals, vol. 29(3), pages 276-306.
- Fernandes, Marcelo, 2001. "Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes," Economics Working Papers (Ensaios Economicos da EPGE) 413, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
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