This note examines the stochastic behaviour of US monthly 10-year government bond yields. Specifically, it estimates a fractional integration model suitable to capture both persistence and non-linearities, these being two important properties of interest rates. Two series are analysed, one from...

This paper extends the cross sectionally augmented panel unit root test proposed by Pesaran (2007) to the case of a multifactor error structure. The basic idea is to exploit information regarding the unobserved factors that are shared by other time series in addition to the variable under...

We propose two novel methods to "bring ABMs to the data". First, we put forward a new Bayesian procedure to estimate the numerical values of ABM parameters that takes into account the time structure of simulated and observed time series. Second, we propose a method to forecast aggregate time...

This paper examines persistence, structural breaks and non-linearities in the case of five European stock market indices, namely the FTSE100 (UK), DAX30 (Germany), CAC40 (France), IBEX35 (Spain) and FTSE MIB40 (Italy), using fractional integration methods. The empirical results provide no...

This paper estimates a model of the real exchange rate including standard fundamentals as well as two alternative measures of inflation expectations for five inflation targeting countries (UK, Canada, Australia, New Zealand, Sweden) over the period January 1993-July 2019. Both a benchmark linear...

In spatial econometrics literature estimation and inference are carried out assuming that the matrix of spatial or network connections has uniformly bounded absolute column sums in the number of units, n, in the network. This paper relaxes this restriction and allows for one or more units to...

Employing an endogenous growth model with human capital, this paper explores how productivity shocks in the goods and human capital producing sectors contribute to explaining aggregate fluctuations in output, consumption, investment and hours. Given the importance of accounting for both the...

We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely...