Economists have long been interested in decomposing observed economic outcomes drive by multiple factors into parts attributable to each. This is the case in both the growth accounting literature (Solow, 1957), and the economic history literature (Fogel, (1964), for instance). The majority of these exercises focus on identifying the separate role played by a few key factors presumed to be the m...

This paper considers tests of the null hypothesis of the ineffectiveness of a policy intervention, defined as a change in the parameters of a policy rule, in the context of a macroeconometric dynamic stochastic general equilibrium (DSGE) model. This is an ex post evaluation of an intervention in a single country, where data are available before and after the intervention. The tests are based on...

What do human beings use conditional reasoning for? A psychological consequence of counterfactual conditional reasoning is emotional experience, in particular, regret and relief. Adults’ thoughts about what might have been influence their evaluations of reality. We discuss recent psychological experiments that chart the relationship between children’s ability to engage in conditional reasoning ...

A numerical method of second order of accuracy for computing conditional Wiener integrals of smooth functionals of a general form is proposed. The method is based on simulation of Brownian bridge via the corresponding stochastic di¤erential equations (SDEs) and on ideas of the weak-sense numerical integration of SDEs. A convergence theorem is proved. Special attention is paid to integral-type f...

According to orthodox (Kolmogorovian) probability theory, conditional probabilities are by definition certain ratios of unconditional probabilities. As a result, orthodox conditional probabilities are undefined whenever their antecedents have zero unconditional probability. This has important ramifications for the notion of probabilistic independence. Traditionally, independence is defined in t...

Models for heteroskedastic data are relevant in a variety of applications ranging from financial time series to environmental statistics. However, the topic modelling variance function conditionally has not seen as much attention mean. Volatility models have been used specific applications, but these can be difficult fit Bayesian setting due posterior distributions that challenging sample effic...

In the statistical literature, the conditional density model specification is commonly used to study regression effects. One attractive model is the semiparametric density ratio model, under which the conditional density function is the product of an unknown baseline density function and a known parametric function containing the covariate information. This model has a natural connection with g...

We describe a system which ranks humanprovided paraphrases of noun compounds, where the frequency with which a given paraphrase was provided by human volunteers is the gold standard for ranking. Our system assigns a score to a paraphrase of a given compound according to the number of times it has co-occurred with other paraphrases in the rest of the dataset. We use these co-occurrence statistic...

A general notion of algebraic conditional plausibility measures is defined. Probability measures, ranking functions, possibility measures, and (under the appropriate definitions) sets of probability measures can all be viewed as defining algebraic conditional plausibility measures. It is shown that the technology of Bayesian networks can be applied to algebraic conditional plausibility measures.