One or Many Knowledge Production Functions? Mapping Innovative Activity Using Microdata This paper discusses the determinants of three alternative measures of innovative output by looking at firm’s own formal R&D activities and at the acquisition of external technology (TA) in its embodied and disembodied components. These input-output relationships are also discussed by distinguishing between ...

INTRODUCTION For pathogens such as influenza that cause many subclinical cases, serologic data can be used to estimate attack rates and the severity of an epidemic in near real time. Current methods for analysing serologic data tend to rely on use of a simple threshold or comparison of titres between pre- and post-epidemic, which may not accurately reflect actual infection rates. METHODS We p...

This paper develops a method for pricing bivariate contingent claims under General Autoregressive Conditionally Heteroskedastic (GARCH) process. As the association between the underlying assets may vary over time, the dynamic copula with time-varying parameter offers a better alternative to any static model for dependence structure and even to the dynamic copula model determined by dynamic depe...

Bivariate probit analysis was used to assess the efficacy of a South African microlender’s loan screening process. This micro-lender grants short-term cash loans to individuals who are employed and earning a fixed salary. Loan applicants with more stable incomes, who are contactable via telephone or post, who are employed in less risky business sectors, who have more disposable income relative ...

Simultaneous observation of continuous and ordered categorical outcomes for each subject is common in biomedical research but multivariate analysis of the data is complicated by the multiple data types. Here we construct a model for the joint distribution of bivariate continuous and ordinal outcomes by applying the concept of latent variables to a multivariate normal distribution. The approach ...

Spatial econometrics has few studies on multivariate ordinal responses. This study proposes a dynamic spatial ordered probit (DMSOP) model, which is the first attempt to capture temporal and dependencies simultaneously for The parameters are calculated using Bayesian inference based Markov chain Monte Carlo sampling. DMSOP model performs effectively with simulated data. Furthermore, applied two...