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Testing for non-linearity in multivariate stochastic processes

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  • Marian Vavra

    (National Bank of Slovakia)

Abstract

Two well known multivariate non-linearity tests are modified using a principal component analysis. The Monte Carlo results show that the proposed principal component-based tests do provide a remarkable dimensionality reduction without any systematic power loss. It can be concluded that using linear dynamic economic models is in sharp contrast with our empirical findings.

Suggested Citation

  • Marian Vavra, 2013. "Testing for non-linearity in multivariate stochastic processes," Working and Discussion Papers WP 2/2013, Research Department, National Bank of Slovakia.
    • Handle: RePEc:svk:wpaper:1023
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    Cited by:

    1. Marian Vavra, 2013. "Testing for linear and Markov switching DSGE models," Working and Discussion Papers WP 3/2013, Research Department, National Bank of Slovakia.

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    More about this item

    Keywords

    non-linearity testing; principal component analysis; Monte Carlo method;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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