Dear you all,
the Econometrics group and the VERA centre of the Ca’ Foscari University (https://www.unive.it/pag/35190) invite you to

Advances in Time Series Econometrics
Half-day Workshop

Venice May 17th, 2024
Ca’ Foscari University, San Giobbe Campus, Meeting Room 1, Venezia

Program:
- 9:30-10:15, Ovielt Baltodano Lopez, (Ca' Foscari University of Venice), Heterogeneous dynamic stochastic block models and an application to international trade

- 10:15-11:00, Alexander Simon Mayer (Ca' Foscari University of Venice), Least squares estimation in nonstationary nonlinear cohort panels with learning from experience

- 11:00-11:30, Break

- 11:30-12.15, Dario Palumbo (Ca' Foscari University of Venice), A simple parsimonious framework for extracting and modelling the term structure of interest rates

- 12:15-13:30, Esther Ruiz (University Carlos III of Madrid), Economic activity and climate change


Abstracts:

Ovielt Baltonado Lopez, Ca' Foscari University of Venice, Italy, https://www.unive.it/data/people/18754547

Heterogeneous dynamic stochastic block models and an application to international trade

The increasing complexity of the global economy represents a challenge for monitoring public debt risks. We propose a twofold framework by applying a Dynamic stochastic block model to a multi layer network composed of extracted debt relationships and trade ows for European countries. On the one hand, this approach allows for clusters of countries with di erent levels of debt synchronization that evolve over time and are subject to structural changes suggesting an increase in systemic risk. On the other hand, the layer dependence accounts for con icts in macroeconomic objectives. In particular, external balance and scal stability. In this sense, the network approach jointly considers interdependence between countries and the twin de cit phenomenon coherent with the composite nature of the risks in the nancial systems. We nd evidence of structural changes in the topology of the debt network during crisis, from an assortative behavior to a coreperiphery system with high interdependence among countries' debt. Moreover, the results suggest parameter heterogeneity. In stable periods unemployment (real GDP per capita) di erences lead to higher (lower) debt synchronization, while income inequality and nancial depth reduce the similarity in the debt evolution. There is no evidence of twinde cit phenomenon for the period 2003-2023.

Alexander Simon Mayer, Ca' Foscari University of Venice, Italy, https://www.unive.it/data/people/26394407

Least squares estimation in nonstationary nonlinear cohort panels with learning from experience

We discuss techniques of estimation and inference for nonstationary nonlinear cohort panels with learning from experience, showing, inter alia, the consistency and asymptotic normality of the nonlinear least squares estimator used in empirical practice. Potential pitfalls for hypothesis testing are identified and solutions proposed. Monte Carlo simulations verify the properties of the estimator and corresponding test statistics in finite samples, while an application to a panel of survey expectations demonstrates the usefulness of the theory developed.

Dario Palumbo, Ca' Foscari University of Venice, Italy, https://www.unive.it/data/people/22437798

A simple parsimonious framework for extracting and modelling the term structure of interest rates

This paper introduces a novel methodology for the extraction and modelling of the unobserved term structure of interest rates which incorporates in a single inferential framework both cross-sectional and time-series information from observed bond prices. In doing so, the paper introduces both a parametric and a semi-parametric dynamic model for the term structure which can be extended to capture also features such as heteroschedasticity in both the time and cross-section dimension, as well as the zero-lowerbound constraint. The models provide a coherent description of the term structure and outperforms current term structure extraction methods in fitting the observed bond prices surface. The models also outperform other existing dynamic term structure models in forecasting observed bond prices at one, six and twelve months horizons. Moreover, the study highlights a strong sensitivity of the forecasting errors of the existing dynamic models to the choice of the term structure extraction method used to construct the samples for the parameters’ estimation.

Prof. Esther Ruiz, University Carlos III of Madrid, Spain, https://researchportal.uc3m.es/display/inv16189

Economic activity and climate change
--