The book provides a comprehensive overview of the latest econometric methods for studying the dynamics of macroeconomic and financial time series. It examines alternative methodological approaches and concepts, including quantile spectra and co-spectra, and explores topics such as non-linear and non-stationary behavior, stochastic volatility models, and the econometrics of commodity markets and globalization. Furthermore, it demonstrates the application of recent techniques in various fields: in the frequency domain, in the analysis of persistent dynamics, in the estimation of state space models and new classes of volatility models.
The book is divided into two parts: The first part applies econometrics to the field of macroeconomics, discussing trend/cycle decomposition, growth analysis, monetary policy and international trade. The second part applies econometrics to a wide range of topics in financial economics, including price dynamics in equity, commodity and foreign exchange markets and portfolio analysis. The book is essential reading for scholars, students, and practitioners in government and financial institutions interested in applying recent econometric time series methods to financial and economic data.

This paper assesses the effectiveness of risk sharing mechanisms in Europe by breaking down the factor income components into their sub-components, and aims to further examine whether financial integration and international portfolio diversification boosts or dampens risk sharing. Using a panel of European countries, we compare the years before and after the 2008 financial crisis. We extend the literature by properly taking into account the heterogeneity (in both country and time dimensions) in the panel through new econometric models. Our results show that financial income has become a major channel of risk sharing in recent years and that a higher integration in the bond and equity markets significantly improves risk sharing in the long term.

We specify unconventional monetary policy reaction functions for the Fed using linear and nonlinear econometric frameworks. We find that nonstandard policy measures are largely driven by the dynamics of inflation and the output gap, with the effect being particularly strong during QE rounds. Moreover, we uncover the presence of asymmetry and regime dependence in central bank’s actions since the global financial crisis, especially concerning the response of the term spread and the shadow short rate to the growth rate of central bank reserves. From a policy perspective and given the lack of a systematic response of monetary policy to asset price growth in nonstandard times, our findings seem to corroborate the view that concerns about asset price bubbles, financial sector pro-cyclicality and systemic risk should be part of the macro-prudential policy toolkit.

This paper provides evidence that the external debt-to-fiscal revenue ratio in emerging countries follows a power-law distribution. Such a distribution reflects the fact that external debt distress or debt crises correspond to extreme events that have been found to happen fairly often. We formally test the hypothesis of a power-law, going further than the usual visual inspection of the distribution of the variable of interest on a doubly logarithmic scale. We also show that such a distribution can be derived from a theoretical model in which uncertainty comes from tax evasion and corruption. Using the framework of an optimal stochastic growth model, we model the external debt-to-fiscal revenue ratio as a diffusion process for which the stochastic steady state distribution is derived using the properties of Itô diffusion processes.