This paper is concerned with methods for analysing spatial data. After initial discussion on the nature of spatial data, including the concept of randomness, we focus most of our attention on linear regression models that involve interactions between agents across space. The introduction of...

This chapter is concerned with methods for analyzing spatial data. After initial discussion of the nature of spatial data, including the concept of randomness, we focus most of our attention on linear regression models that involve interactions between agents across space. The introduction of...

This research focuses on examining why young social media users might become trapped in a "social bubble" defined as seeking information that supports only one’s existing beliefs. We use a method called Qualitative Comparative Analysis to identify various combinations of factors that either...

Forecasting the vote share for the upcoming US presidential elections involves multiple pivotal economic and non-economic factors. Critical macroeconomic forces such as the rate of economic growth, tax burden, inflation, and unemployment significantly influence the votes gained or lost by the...

Non-Gaussian state-space models arise in several applications, and within this framework the binary time series setting provides a relevant example. However, unlike for Gaussian state-space models — where filtering, predictive and smoothing distributions are available in closed form — binary...

In this paper, I explore how fiscal policy decisions relate to the business cycle and, building on that, how the effects of policy interventions may vary depending on when policy is conducted in the business cycle. To assess this, I estimate a small to medium-sized DSGE model with expressive...

The empirical saddlepoint likelihood (ESPL) estimator is introduced. The ESPL provides improvement over one-step GMM estimators by including additional terms to automatically reduce higher order bias. The first order sampling properties are shown to be equivalent to efficient two-step GMM. New...

The empirical saddlepoint likelihood (ESPL) estimator is introduced. The ESPL provides improvement over one-step GMM estimators by including additional terms to automatically reduce higher order bias. The first order sampling properties are shown to be equivalent to efficient two-step GMM. New...

This paper develops a wavelet (spectral) approach to test the presence of a unit root in a stochastic process. The wavelet approach is appealing, since it is based directly on the different behavior of the spectra of a unit root process and that of a short memory stationary process. By...