A continuous time econometric modelling framework for multivariate financial market event (or `transactions`) data is developed in which the model is specified via the vector stochastic intensity. This has the advantage that the conditioning sigma-field is updated continuously in time as new...

Using village data from Tanzania, we test whether gifts and loans between households are voluntary while correcting for mis-reporting by the giving and receiving households.  Two maintained assumptions underlie our analysis: answers to a question on who people would turn to for help are good...

Functional Signal plus Noise (FSN) time series models are introduced for the econometric analysis of the dynamics of a large cross-section of prices in which contemporaneous observations are functionally related. A semiparametric FSN model is developed in which a smooth, cubic spline signal...

Functional Signal plus Noise (FSN) time series models are introduced for the econometric analysis of the dynamics of a large cross-section of prices in which contemporaneous observations are functionally related. A semiparametric FSN model is developed in which a smooth, cubic spline signal...

High-profile universities often face public criticism for undermining academic merit and promoting social elitism/engineering through their admissions-process. In this paper, we develop an empirical test for whether access to selective universities is meritocratic. We assume that students who...

Global frailty is an unobserved macroeconomic variable.  In event data contexts, this unobserved variable is assumed to impact the hazard rate of event arrivals.  Attempts to identify and estimate the path of frailty are complicated when observed macroeconomic variables also impact hazard...

Partial mean processes with generated regressors arise in several important econometric problems, such as the distribution of potential outcomes with continuous treatments and the quantile structural function in a nonseparable triangular model.  This paper proposes a fully nonparametric...

High frequency financial data allows us to learn more about volatility, volatility of volatility and jumps.  One of the key techniques developed in the literature in recent years has been bipower variation and its multipower extension, which estimates time-varying volatility robustly to...

Big Data offer potential benefits for statistical modelling, but confront problems like an excess of false positives, mistaking correlations for causes, ignoring sampling biases, and selecting by inappropriate methods.  We consider the many important requirements when searching for a data-based...

Structural vector autoregressive (SVAR) models have emerged as a dominant research strategy in empirical macroeconomics, but suffer from the large number of parameters employed and the resulting estimation uncertainty associated with their impulse responses. In this paper we propose...