Macroeconomic and Financial Time Series Modeling: Inference and Bootstrap Methods

This course will introduce participants to the theoretical properties of modern bootstrap algorithms, as well as guide them in the practical selection of the appropriate bootstrap methodology for the problem of interest. Applications will include macroeconomic models with structural breaks, financial (high-frequency data) and stochastic volatility/GARCH models. At the end of the course, students will possess the tools to assess the validity of the bootstrap through theoretical arguments, as well as to construct numerical simulations to show the finite-sample performance of the estimators and tests of interest.

Topics at a glance:
- Introduction to the bootstrap.
- Models for stationary time series and the bootstrap for stationary autoregressive models.
- Non-stationarity in time series models: unit roots and cointegration, bootstrapping non-stationarity time series models.
- Models for volatility and conditional variances and the bootstrap.
- Non-standard applications of the bootstrap.

The course consists of:
1. lectures: take place from Monday until Friday and are divided into two morning sessions (09:00-11:00 and 11:30-13:30)
2. computer practicums: take place in the afternoon (15:30-17:30) from Monday to Friday (except Wednesday afternoon)

Course leader

Giuseppe Cavaliere, Professor of Econometrics, University of Bologna and Distinguished Research Professor of Economics, University of Exeter Business School.

Target group

PhD Students, Early career Academics, Researchers and Practitioners

The course is designed for students, researchers and practitioners interested in the recent advances in estimation and inference for time-series models. Participants should have a preliminary background in econometrics and probability theory. While any prior knowledge of time series econometrics and bootstrap methods can be helpful, it is not necessary.

Course aim

The aim of this course is to provide an up-to-date overview of bootstrap methods for statistical inference, with a specific focus on its application to time series models for economic and financial data. Particular attention will be given to estimation and inference for univariate and multivariate models, both for stationary and non-stationary time series.

Credits info

4 EC
Students who attend all sessions and successfully complete a take-home assignment after the end of the Summer School will be awarded 4 ECTS credits. Participants should check with their home institution on how the certificate should be issued.

Fee info

EUR 1200: Tuition fees for PhD students are set at €1,200
EUR 2000: Tuition fees for non-students are €2,000

For both groups tuition fees include:
- Accommodation for six nights (from Sunday to Saturday) including breakfast
- Two coffee breaks during classes from Monday to Friday
- Daily lunch at the University restaurant from Monday to Friday
- A welcome reception on Monday evening and a farewell dinner in a local tavern on Friday evening
- Two excursions, on Wednesday afternoon and Saturday morning