The paper considers a test for structural break based on quantile regressions instead of OLS estimates. Besides granting robustness, this allows to verify the impact of a break in more than one point of the conditional distribution. Then the quantile regression test is repeatedly implemented as a diagnostic tool to uncover partial or spurious breaks. The test is also implemented to measure the contribution of each explanatory variable to the instability of the regression coefficients, thus finding which one of the different possible sources of break linked to the nature of the explanatory variables is the most effective. A real data example on exchange rates shows the presence of a time driven break, but only at the lower quartile, while the analysis on the explanatory variable excludes its involvement in the break. Since the asymptotic distribution of the OLS test for structural change depends on i.i.d. normal errors and on the exogeneity of the explanatory variables, a Monte Carlo study analyzes the behavior of OLS and quantile regression tests for structural changes with lagged endogenous variables, non-normal errors, spurious or partial breaks, and misspecification.
Quantile regression estimates and the analysis of structural breaks
FURNO, Marilena
2012-01-01
Abstract
The paper considers a test for structural break based on quantile regressions instead of OLS estimates. Besides granting robustness, this allows to verify the impact of a break in more than one point of the conditional distribution. Then the quantile regression test is repeatedly implemented as a diagnostic tool to uncover partial or spurious breaks. The test is also implemented to measure the contribution of each explanatory variable to the instability of the regression coefficients, thus finding which one of the different possible sources of break linked to the nature of the explanatory variables is the most effective. A real data example on exchange rates shows the presence of a time driven break, but only at the lower quartile, while the analysis on the explanatory variable excludes its involvement in the break. Since the asymptotic distribution of the OLS test for structural change depends on i.i.d. normal errors and on the exogeneity of the explanatory variables, a Monte Carlo study analyzes the behavior of OLS and quantile regression tests for structural changes with lagged endogenous variables, non-normal errors, spurious or partial breaks, and misspecification.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
