Principles, ideas and theory in econometric time series

With examples from cointegration, bootstrap, ARCH, state space and big data models


Juri Marcucci, Bank of Italy


Søren Johansen (University of Copenhagen)

Anders Rahbek (University of Copenhagen)

Course outline

The course will be in two main parts: The first part discusses econometric methods and theory, which are applied in the second part, where selected topics from cointegration, statespace models, the bootstrap and multivariate ARCH models, as well as big data modelling will be discussed in detail from recent research.

Course description:

In Part I, we give an introduction, aimed for graduate/Ph.D. level students in econo-metrics, to (i) asymptotic theory for stationary, i.i.d. as well as non-stationary (integratedof order one) variables; (ii) theory for the bootstrap; (iii) theory for cointegration and for(multivariate) ARCH models; and, (iv) theory for the Kalman filter. All theory presented will be in terms of examples where details are explained, rather than providing a general introduction to the field(s).

In Part II, we discuss recent research with reference to the theory and methodology introduced in Part I.

The topics include:

Cointegration and adjustment in a common trends causal model and the role of weakexogeneity.

Optimal hedging and cointegration in the presence of heteroscedastic errors.

Bootstrap based inference in stationary and non-stationary (conditionally heteroscedas-tic) autoregressive models.

Models, Methods and Big Data