Project overview
To develop robust estimation and inference methodologies for predictive regression models under various econometric assumptions.
A first line of research within this theme is the development of asymptotic theory for detecting parameter instability in predictive regressions under the assumption of nonstationarity when the functional form corresponds to a conditional mean function as well as a conditional quantile function. Consequently, we can investigate the presence of mean or quantile predictability based on nonstationary time series data.
A second line of research within this theme, is the development of uniform inference with general autoregressive roots which is robust to the whole spectrum of persistence properties. In particular, we utilize current instrumental variable methodology from the literature (IVX) and focus on establishing the related asymptotic theory for the quantile autoregressive and quantile predictive regression models.
A first line of research within this theme is the development of asymptotic theory for detecting parameter instability in predictive regressions under the assumption of nonstationarity when the functional form corresponds to a conditional mean function as well as a conditional quantile function. Consequently, we can investigate the presence of mean or quantile predictability based on nonstationary time series data.
A second line of research within this theme, is the development of uniform inference with general autoregressive roots which is robust to the whole spectrum of persistence properties. In particular, we utilize current instrumental variable methodology from the literature (IVX) and focus on establishing the related asymptotic theory for the quantile autoregressive and quantile predictive regression models.