We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression imputation estimator, a marginal average estimator and a (marginal) propensity score weighted estimator are defined. All the estimators are proved to be asymptotically normal,...

We propose a modification of kernel time series regression estimators that improves efficiency when the innovation process is autocorrelated. The procedure is based on a pre-whitening transformation of the dependent variable that has to be estimated from the data. We establish the asymptotic...

Theoretical models of growth and trade suggest that patterns of international specialization are dynamic and evolve endogenously over time. Initial comparative advantages are either reinforced or gradually unwound with the passage of time. This paper puts forward an empirical framework for...

We examine whether the information contained in social media (Twitter & Facebook) and web search queries (Google) influences financial markets. Using a multivariate system and focussing on Eurozone’s peripheral countries, the GIIPS (Greece, Ireland, Italy, Portugal and Spain), we show that...

Specific functional forms are often used in economic models of distributions; goodness-of-fit measures are used to assess whether a functional form is appropriate in the light of real-world data. Standard approaches use a distance criterion based on the EDF, an aggregation of differences in...

We examine whether the information contained in social media (Twitter & Facebook) and web search queries (Google) influences financial markets. Using a multivariate system and focussing on Eurozone’s peripheral countries, the GIIPS (Greece, Ireland, Italy, Portugal and Spain), we show that...

Benford's Law is used to test for data irregularities. While novel, there are two weaknesses in the current methodology. First, test values used in practice are too conservative and the test values of this paper are more powerful and hold for fairly small samples. Second, testing requires...

With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an...

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the case of distribution of unknown, nonparametric, form, where series...