This paper proposes a score-driven model for filtering time-varying causal parameters through the use of instrumental variables. In the presence of suitable instruments, we show that we can uncover dynamic causal relations between variables, even in the presence of regressor endogeneity which...

A new and rapidly growing econometric literature is making advances in the problem of using machine learning (ML) methods for causal inference questions. Yet, the empirical economics literature has not started to fully exploit the strengths of these modern methods. We revisit influential...

The econometrics literature proposed several new causal machine learning methods (CML) in the past few years. These methods harness the strength of machine learning methods to flexibly model the relationship between the treatment, outcome and confounders, while providing valid inferential...

The recent financial crisis has led to the introduction of contingent convertible instruments (CoCos) in the capital framework for banks. Although CoCos can provide benefits, such as automatic recapitalization of troubled banks, their inherent risks raise questions about whether they increase...

This paper develops a discrete-time epidemiological model for the spread of crises across sectors in the United States for the period 1952-2015. It is the first to use an epidemiological approach with macroeconomic (Flow of Funds) data. An extension of the usual one-period Markov model to a...

The paper considers the problem as to whether financial returns have a common volatility process in the framework of stochastic volatility models that were suggested by Harvey et al. (1994). We propose a stochastic volatility version of the ARCH test proposed by Engle and Susmel (1993), who...

This paper studies shock transmission across macroeconomic sectors in the UK, using data from the Bank of England's Flow of Funds statistics. We combine two different approaches to quantify the spread of shocks to assess whether sectors with large bilateral economic linkages as measured through...