A non-nested test of level-differenced versus log-differenced stationary models

This paper considersthe applicationof the simulated Cox test procedure developed in Pesaran and Pesaran (1993) to test linear versus log-linear models. The test procedure can also be applied to other generalized linear regression models such as level-difference stationary models versus the log-difference stationary models. In order to compare the small sample performanceof the proposed test with other tests extant in the literature, the paper also reports the resultsof a numberof Monte Carlo experiments using the experimental framework of Godfrey et al. (1988). The Monte Carlo results provide strong support for a simplified version of the simulatedCox test over the PE and the BM tests, but suggest that there is little to choose between the simulated Cox test and the DL test.     

Cointegration and direct tests of the rational expectations hypothesis

The paper is concerned with direct tests of the rational expectations hypothesis (REH) in the presence of stationary and non-stationary variables. Alternative methods of converting qualitative survey responses into quantitative expectations series are examined. Testing of orthogonality and the issue of generated regressors for models estimated by two step methods are re-evaluated when the variable to be explained is stationary. A methodological approach for testing the REH is provided for models using qualitative response data when there are unit roots and cointegration, and alternative reasons are examined for rejecting the null hypothesis of orthogonality. The usefulness of cointegration analysis for both the probability and regression conversion procedures is also analysed. Cointegration is found to be directly applicable for the probability conversion approach with uniform, normal and logistic distributions of expectations and for the linear regressicn conversion approach. In the light of new techniques, an existing empirical example testing the REH for British manufacturing firms is re-examined and tested over an extended data set.     

Learning, Structural Instability, and Present Value Calculations

Present value calculations require predictions of cash flows both at near and distant future points in time. Such predictions are generally surrounded by considerable uncertainty and may critically depend on assumptions about parameter values as well as the form and stability of the data generating process underlying the cash flows. This paper presents new theoretical results for the existence of the infinite sum of discounted expected future values under uncertainty about the parameters characterizing the growth rate of the cash flow process. Furthermore, we explore the consequences for present values of relaxing the stability assumption in a way that allows for past and future breaks to the underlying cash flow process. We find that such breaks can lead to considerable changes in present values.     

A New Test of Randomness for Lehmer Generators Based on the Manhattan Distance Between Pairs of Consecutive Random Numbers

This article considers the Marsaglia effect by proposing a new test of randomness for Lehmer random number generators. Our test is based on the Manhattan distance criterion between consecutive pairs of random numbers rather than the usually adopted Euclidian distance. We derive the theoretical distribution functions for the Manhattan distance for both overlapping (two dimensional) as well as non-overlapping cases. Extensive goodness-of-fit testing as well as empirical experimentation provides ample proof of the merits of the proposed criterion.     

A new lifetime distribution for a series-parallel system: properties,applications and estimations under progressive type-II censoring

A compound class of zero truncated Poisson and lifetime distributions is introduced. A specialization is paved to a new three-parameter distribution, called doubly Poisson-exponential distribution, which may represent the lifetime of units connected in a series-parallel system. The new distribution can be obtained by compounding two zero truncated Poisson distributions with an exponential distribution. Among its motivations is that its hazard rate function can take different shapes such as decreasing, increasing and upside-down bathtub depending on the values of its parameters. Several properties of the new distribution are discussed. Based on progressive type-II censoring, six estimation methods [maximum likelihood, moments, least squares, weighted least squares and Bayes (under linear-exponential and general entropy loss functions) estimations] are used to estimate the involved parameters. The performance of these methods is investigated through a simulation study. The Bayes estimates are obtained using Markov chain Monte Carlo algorithm. In addition, confidence intervals, symmetric credible intervals and highest posterior density credible intervals of the parameters are obtained. Finally, an application to a real data set is used to compare the new distribution with other five distributions.     

A change point method for Phase II monitoring of generalized linear profiles

In this article, we adopt the change point approach to monitor the generalized linear profiles in phase II Statistical process control (SPC). Generalized linear profiles include a large class of profiles defined in one framework. In contrast to the conventional change point approach, we adopt the Rao score test rather than the likelihood ratio test. Simulated results show that our approach has a good performance over any possible single step change in process parameters for two special cases of generalized linear profiles, namely Poisson and binomial profiles. Some diagnostic aids are also given and a real example is introduced to shed light on the merits of our approach in real applications.     

The Deterrent Effect of Capital Punishment: Evidence from a "Judicial Experiment"

We use panel data for 50 states during the 1960–2000 period to examine the deterrent effect of capital punishment, using the moratorium as a "judicial experiment." We compare murder rates immediately before and after changes in states' death penalty laws, drawing on cross-state variations in the timing and duration of the moratorium. The regression analysis supplementing the before-and-after comparisons disentangles the effect of lifting the moratorium on murder from the effect of actual executions on murder. Results suggest that capital punishment has a deterrent effect, and that executions have a distinct effect which compounds the deterrent effect of merely (re)instating the death penalty. The finding is robust across 96 regression models. (JEL C1, K1)     

Cointegration and direct tests of the rational expectations hypothesis

The paper is concerned with direct tests of the rational expectations hypothesis (REH) in the presence of stationary and non-stationary variables. Alternative methods of converting qualitative survey responses into quantitative expectations series are examined. Testing of orthogonality and the issue of generated regressors for models estimated by two step methods are re-evaluated when the variable to be explained is stationary. A methodological approach for testing the REH is provided for models using qualitative response data when there are unit roots and cointegration, and alternative reasons are examined for rejecting the null hypothesis of orthogonality. The usefulness of cointegration analysis for both the probability and regression conversion procedures is also analysed. Cointegration is found to be directly applicable for the probability conversion approach with uniform, normal and logistic distributions of expectations and for the linear regressicn conversion approach. In the light of new techniques, an existing empirical example testing the REH for British manufacturing firms is re-examined and tested over an extended data set.     

An Adaptive Exploration-Exploitation Algorithm for Constructing Metamodels in Random Simulation Using a Novel Sequential Experimental Design

In this article, we propose a novel algorithm for sequential design of metamodels in random simulation, which combines the exploration capability of most one-shot space-filling designs with the exploitation feature of common sequential designs. The algorithm continuously maintains a balance between the exploration and the exploitation search throughout the search process in a sequential and adaptive manner. The numerical results indicate that the proposed approach is superior to one of the existing well-known sequential designs in terms of both the computational efficiency and speed in generating efficient experimental designs.