Infinite-Variance Stable Errors and Robust Estimation Procedures (Paperback)


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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Product Description

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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Product Details

General

Imprint

Lap Lambert Academic Publishing

Country of origin

Germany

Release date

December 2011

Availability

Expected to ship within 10 - 15 working days

First published

December 2011

Authors

Dimensions

229 x 152 x 9mm (L x W x T)

Format

Paperback - Trade

Pages

152

ISBN-13

978-3-8465-4732-8

Barcode

9783846547328

Categories

LSN

3-8465-4732-8



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