Infinite-Variance Stable Errors and Robust Estimation Procedures - Serttas, Fatma Ö.
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Gaussian normal error assumption is a basic assumption for co-integration tests. Ordinary Least Squares (OLS) based regression techniques are also widely used together with the normality assumption. To consider the heavy-tailed structure observed in many economic and financial time series, new residual-based co-integration tests are developed and analyzed via Monte Carlo simulations. The new tests are based on Least Absolute Deviation (LAD) regressions, whose error structure follows the infinite-variance stable distribution. Empirical applications on Forward Rate Unbiasedness Hypothesis (FRUH)…mehr

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Produktbeschreibung
Gaussian normal error assumption is a basic assumption for co-integration tests. Ordinary Least Squares (OLS) based regression techniques are also widely used together with the normality assumption. To consider the heavy-tailed structure observed in many economic and financial time series, new residual-based co-integration tests are developed and analyzed via Monte Carlo simulations. The new tests are based on Least Absolute Deviation (LAD) regressions, whose error structure follows the infinite-variance stable distribution. Empirical applications on Forward Rate Unbiasedness Hypothesis (FRUH) and Purchasing Power Parity (PPP) verify the need to make use of the infinite-variance stable distributions as the error distributions.
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Autorenporträt
Fatma Özgü Serttä was born in Ankara, Turkey and graduated from T.E.D Ankara College in 1995. She attended Bilkent University and received her B.A. in Economics in 2000 and her M.A. in Economics in 2002. She obtained her Ph.D. in Economics from Iowa State University in 2010. Currently, she is an Assistant Professor at Y¿ld¿r¿m Beyaz¿t University.