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Testing for Random Walk Coefficients in Regression and State Space Models

  • Book
  • © 1998

Overview

Authors:
  1. Martin Moryson

    1. Wiesbaden, Germany

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Part of the book series: Contributions to Statistics (CONTRIB.STAT.)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction

    • Martin Moryson
    Pages 1-5

  3. The Linear State Space Model

    • Martin Moryson
    Pages 7-59

  4. Exact Tests for Univariate Random Walk Coefficients

    • Martin Moryson
    Pages 61-99

  5. Asymptotic Tests for Univariate Random Walk Coefficients in Models with Stationary Regressors

    • Martin Moryson
    Pages 101-130

  6. Asymptotic Tests for Univariate Random Walk Coefficients in Models with Non-Stationary Regressors

    • Martin Moryson
    Pages 131-181

  7. Testing Trend Stationarity Against Difference Stationarity in Time Series

    • Martin Moryson
    Pages 183-200

  8. Testing for Multivariate Random Walk Coefficients in Regression Models

    • Martin Moryson
    Pages 201-254

  9. Testing for Random Walk Coefficients in the Presence of Varying Coefficients Under H0

    • Martin Moryson
    Pages 255-275

  10. The Term Structure of German Interest Rates — Testing the Expectations Hypothesis

    • Martin Moryson
    Pages 277-295

  11. Résumé and Prospects

    • Martin Moryson
    Pages 297-300

  12. Back Matter

    Pages 301-317
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Keywords

About this book

Regression and state space models with time varying coefficients are treated in a thorough manner. State space models are introduced as a means to model time varying regression coefficients. The Kalman filter and smoother recursions are explained in an easy to understand fashion. The main part of the book deals with testing the null hypothesis of constant regression coefficients against the alternative that they follow a random walk. Different exact and large sample tests are presented and extensively compared based on Monte Carlo studies, so that the reader is guided in the question which test to choose in a particular situation. Moreover, different new tests are proposed which are suitable in situations with autocorrelated or heteroskedastic errors. Additionally, methods are developed to test for the constancy of regression coefficients in situations where one knows already that some coefficients follow a random walk, thereby one is enabled to find out which of the coefficients varies over time.

Authors and Affiliations

  • Wiesbaden, Germany

    Martin Moryson

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