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Advancing the iid Test Based on Integration across the Correlation Integral: Ranges, Competition, and Power

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Author Info
Evzen Kocenda (CERGE-EI)
Lubos Briatka (CERGE-EI)

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Abstract

This paper builds on Kočenda (2001) and extends it in two ways. First, two new intervals of the proximity parameter ε (over which the correlation integral is calculated) are specified. For these ε- ranges new critical values for various lengths of the data sets are introduced and through Monte Carlo studies it is shown that within new ε-ranges the test is even more powerful than within the original ε-range. A sensitivity analysis of the critical values with respect to ε-range choice is also given. Second, a comparison with existing results of the controlled competition of Barnett et al. (1997) as well as broad power tests on various nonlinear and chaotic data are provided. The results of the comparison strongly favor our robust procedure and confirm the ability of the test in finding nonlinear dependencies. An empirical comparison of the new ε-ranges with the original one shows that the test within the new ε-ranges is able to detect hidden patterns with much higher precision. Finally, new user-friendly and fast software is introduced.

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Publisher Info
Paper provided by EconWPA in its series Econometrics with number 0409001.

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Length: 40 pages
Date of creation: 02 Sep 2004
Date of revision:
Handle: RePEc:wpa:wuwpem:0409001

Note: Type of Document - pdf; pages: 40. Paper has the link to a webpage to download the software.
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Web page: http://129.3.20.41

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Related research
Keywords: chaos; nonlinear dynamics; correlation integral; Monte Carlo; single-blind competition; power tests; high-frequency economic and financial data;

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
F31 - International Economics - - International Finance - - - Foreign Exchange
G12 - Financial Economics - - General Financial Markets - - - Asset Pricing

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References listed on IDEAS
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  1. Bollerslev, Tim & Ole Mikkelsen, Hans, 1999. "Long-term equity anticipation securities and stock market volatility dynamics," Journal of Econometrics, Elsevier, vol. 92(1), pages 75-99, September. [Downloadable!] (restricted)
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    Other versions:
  4. Aguirre, Antonio & Aguirre, Luis Antonio, 2000. "Time Series Analysis of Monthly Beef Cattle Prices with Nonlinear Autoregressive Models," Applied Economics, Taylor and Francis Journals, vol. 32(3), pages 265-75, February. [Downloadable!] (restricted)
  5. Brooks, Chris & Henry, Olan T., 2000. "Can portmanteau nonlinearity tests serve as general mis-specification tests?: Evidence from symmetric and asymmetric GARCH models," Economics Letters, Elsevier, vol. 67(3), pages 245-251, June. [Downloadable!] (restricted)
    Other versions:
  6. Aydin Cecen, A. & Erkal, Cahit, 1996. "Distinguishing between stochastic and deterministic behavior in foreign exchange rate returns: Further evidence," Economics Letters, Elsevier, vol. 51(3), pages 323-329, June. [Downloadable!] (restricted)
  7. Brorsen, B Wade & Yang, Seung-Ryong, 1994. "Nonlinear Dynamics and the Distribution of Daily Stock Index Returns," Journal of Financial Research, Southern Finance Association and Southwestern Finance Association, vol. 17(2), pages 187-203, Summer.
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  9. Brooks, Chris & Heravi, Saeed M, 1999. "The Effect of (Mis-Specified) GARCH Filters on the Finite Sample Distribution of the BDS Test," Computational Economics, Springer, vol. 13(2), pages 147-62, April. [Downloadable!]
  10. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July. [Downloadable!] (restricted)
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