Uniform and Distribution-free Inference with General Autoregression Processes

Seminar abstract

A unified theory of estimation and inference is developed for an autoregressive process with root in (-1,∞) that includes the stable, unstable, explosive and all intermediate regions.

The discontinuity of the limit distribution of the t-statistic along autoregressive regions and its dependence on the distribution of the innovations in the explosive region (1,∞) are addressed simultaneously.

A novel estimation procedure, based on a data-driven combination of an artificially constructed near-stationary and mildly explosive instrument, delivers an asymptotic mixed-

Gaussian theory of estimation and gives rise to an asymptotically standard normal t-statistic across all autoregressive regions independently of the distribution of the innovations.

The resulting hypothesis tests and confidence intervals are shown to have correct asymptotic size (uniformly over the parameter space) both in autoregressive and in predictive regression models, thereby establishing a general and unified framework of inference with autoregressive processes.

Extensive Monte Carlo experimentation shows that the proposed methodology exhibits very good finite sample properties over the entire autoregressive parameter space (-1,∞) and compares favourably to existing methods within their parametric (-1,1) validity range.

We apply our procedure to early growth rates of Covid-19 infections across countries by employing a stochastic SIR model and constructing confidence intervals for the epidemic's basic reproduction number without a priory knowledge of the model's stability/explosivity properties.

How to join this seminar

This seminar is free to attend however entry is password protected we ask that you contact the organiser for access.

You can join this seminar online on Wednesday 24 November at 12pm

We warmly invite you to join with your friends, classmates and colleagues.

Speaker bio

Dr. Katerina Petrova is an assistant professor at the Department of Economics, Universitat Pompeu Fabra, Spain.

Her research interests include Econometrics, Macroeconometrics, DSGE and VAR Models, MCMC Methods.

Her work has been published in top places in econometrics and finance, such as;

• Journal of Econometrics,
• Journal of Economic Dynamics and Control,
• Journal of Time Series Analysis,
• Journal of Empirical Finance.