Research related to high dimensional econometrics

This dissertation consists of three chapters related to high dimensional econometrics dealing with the estimation of nonlinear panel data models and networks models. The first chapter proposes a fixed effects expectation-maximization estimator for a class of nonlinear panel data models with unobserved heterogeneity modeled as individual and/or time effects or an arbitrary interaction of the two. The estimator is obtained through a computationally simple iterative two-step procedure, both steps having a closed form solution. I show that the estimator is consistent in large panels, derive the asymptotic distribution for a probit model with interactive effects, and develop analytical bias corrections to deal with the incidental parameter problem. The second chapter considers estimation and inference for semiparametric nonlinear panel single index models with interactive effects. These include static and dynamic probit, logit, and Poisson models. An iterative two-step procedure to maximize the likelihood is proposed. The estimator is consistent, but has bias due to the incidental parameter problem. Analytical and jackknife bias corrections are developed to remove the bias without increasing variance. The third chapter proposes Quantile Graphical Models (QGMs) to characterize predictive and conditional dependence relationships within a set of random variables in non-Gaussian settings. These characterize the best linear predictor under asymmetric losses and the conditional dependence at each quantile. Estimators based on high-dimensional techniques are proposed. Each QGM represents the tail interdependence and the associated tail risk network and can be used to measure systemic risk contributions for the study of financial contagion and hedging under a market downturn

Bayesian nonparametric sparse VAR models

High dimensional vector autoregressive (VAR) models require a large number of parameters to be estimated and may suffer of inferential problems. We propose a new Bayesian nonparametric (BNP) Lasso prior (BNP-Lasso) for high-dimensional VAR models that can improve estimation efficiency and prediction accuracy. Our hierarchical prior overcomes overparametrization and overfitting issues by clustering the VAR coefficients into groups and by shrinking the coefficients of each group toward a common location. Clustering and shrinking effects induced by the BNP-Lasso prior are well suited for the extraction of causal networks from time series, since they account for some stylized facts in real-world networks, which are sparsity, communities structures and heterogeneity in the edges intensity. In order to fully capture the richness of the data and to achieve a better understanding of financial and macroeconomic risk, it is therefore crucial that the model used to extract network accounts for these stylized facts.Comment: Forthcoming in "Journal of Econometrics" ---- Revised Version of the paper "Bayesian nonparametric Seemingly Unrelated Regression Models" ---- Supplementary Material available on reques

Volatility co-movements and spillover effects within the Eurozone economies: A multivariate GARCH approach using the financial stress index

The Eurozone crisis is one the most important economic event in recent years. At its peak, the effects of the crisis have put at serious risk the outcome of the euro project, exposing the inherent weaknesses and vulnerabilities of the monetary union. As the degree of economic and financial integration of these countries is significant, we aim to investigate in details the potential cross-covariance and spillover effects between the Eurozone economies and financial markets. In order to do this, we employ financial stress indexes, as systemic risk metrics in a multivariate GARCH model. This method is able to capture markets’ dependencies and volatility spillovers and is employed on a single market level as well as on the full spectrum of Eurozone markets. The empirical results have shown the important and intensive stress transmission on banking and money markets. Moreover, the role of peripheral countries as stress transmitter is verified, but only for particular periods. The significant spillover effects from core countries are also evident, indicating their important role in the Euro Area and its overall financial stability. The “decoupling” hypothesis is empirically verified, underling the gradually decreasing intensity of spillovers between Euro Area countries. Overall, this paper exhibits the complex structure of spillover effects for Eurozone, along with a clustering effect in the most recent times