Models with dependent and with non-identically distributed data

This chapter focuses on the quantile regression estimators for models characterized by heteroskedastic and by dependent errors. Section 5.1 considers the precision of the quantile regression model in case of i.i.d. errors, giving a closer look at the computation of confidence intervals and hypotesis testing on each estimated coefficient. The empirical example further analyzes the wage equation introduced in Chapter 3. Section 5.2 extends the analysis to the case of non identically distributed errors, discussing different ways to verify the presence of heteroskedasticity in the data. The example considers the series on changes in consumption analyzed in Section 3.1.2 and the Italian GDP growth rate. Section 5.3 takes into account the case of dependent observations and discusses the estimation of an exchange rate equation characterized by serially correlated errors.