Barriers to the use of program theory

There are numerous advantages to conducting an evaluation that is based on a strong program theory. However, a careful examination of the field of evaluation indicates that few evaluations are theory based. It is suggested that this discrepancy is caused by significant barriers such as cost, knowledge, motivation, and role confusion. Suggestions for increasing the use of program theory include improving our education and training of evaluators, changing journal policies, and augmenting our methodology for theory development.     

CRITICAL VALUE APPROXIMATIONS FOR TESTS OF LINEAR REGRESSION DISTURBANCES

Two important classes of tests for non-spherical disturbances in the linear regression model involve test statistics whose null distributions and hence critical values depend on the regressors. This paper investigates the accuracy of the normal, two moment beta and four moment beta approximations to the critical values of such tests. An empirical experiment aimed at evaluating the accuracy of the approximations for a variety of tests against autocorrelation and heteroscedasticity is conducted. Overall the approximations are found to provide reasonably accurate critical values with skewness being a factor determining the degree of accuracy.     

A dynamic analysis of stock markets using a hidden Markov model

This paper proposes a framework to detect financial crises, pinpoint the end of a crisis in stock markets and support investment decision-making processes. This proposal is based on a hidden Markov model (HMM) and allows for a specific focus on conditional mean returns. By analysing weekly changes in the US stock market indexes over a period of 20 years, this study obtains an accurate detection of stable and turmoil periods and a probabilistic measure of switching between different stock market conditions. The results contribute to the discussion of the capabilities of Markov-switching models of analysing stock market behaviour. In particular, we find evidence that HMM outperforms threshold GARCH model with Student-t innovations both in-sample and out-of-sample, giving financial operators some appealing investment strategies.     

Bayesian inferences given a type-2 censored sample from a burr distribution

A Bayesian approach is used to make inferences given a random sample of observations from a Burr distribution. Complete and type-2 censored samples are considered and inferences are made on the unknown parameters and the reliability function. In the case of a type-2 censored sample prediction bounds are derived for the unobserved sample values.     

Panel discussion by journal editors

The smooth PL quantile estimator is proposed and the analog of Bahadur-Kiefer type process is constructed based on the PL estimator and the smooth PL quantile estimator when the data are subject to censorship. Pointwise and uniform strong limit theorems for this process are established, The theorems are sharp and give the exact convergent rates of Bahadur type representation for the smooth PL quantile estimator.