Robust Testing for Unit Roots Based on Regression Rank Scores

The objective of this thesis is to provide a robust statistical procedure for testing unit root models, based on regression-rank scores (RRS) introduced by Gutenbrunner and Jureckova (1990). These RRS arise as a vector of solutions of the dual form of the linear program required to compute the regression quantile statistics of Koenker and Bassett (1978). They are simple ranks of the sample observations for location model. To test the unit root of $y\sb{t}$, we consider the model$\Delta y\sb{t}\equiv u\sb{t} = μ + (β - 1)y\sb{t-1} + \sum\sbsp{j=1}{p}\phi\sb{j}u\sb{t-j} + e\sb{t}$and test the null hypothesis, $(\beta-1)$ = 0, against local alternatives. In the finite variance case, the normal theory of our RRS based test statistics have the same rate of convergence 1/T, as the other existing tests based on least squares (LS) estimators, e.g. Dickey-Fuller (1979, 81), Phillips (1987), Phillips and Perron (1988). While their test statistics are complicated functionals of Brownian motion ours follow chi-square distribution asymptotically under both finite and infinite variance cases.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Economics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Hasan, Mohammad Nazmul

Contributors dc:contributor

• Koenker, Roger William,

• Economics, Theory
• Statistics

Identifier

(UMI)AAI9411647

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/72423