On the power of durbin-watson statistic against fractionally integrated processes

This paper provides the theoretical explanation and Monte Carlo experiments of using a modified version of Durbin-Watson ( D W ) statistic to test an 1 ( 1 ) process against I ( d ) alternatives, that is, integrated process of order d, where d is a fractional number. We provide the exact order of magnitude of the modified D W test when the data generating process is an I ( d ) process with d E (0. 1.5). Moreover, the consistency of the modified DW statistic as a unit root test against I ( d ) alternatives with d E ( 0 , l ) U ( 1 , 1.5) is proved in this paper. In addition to the theoretical analysis, Monte Carlo experiments show that the performance of the modified D W statistic reveals that it can be used as a unit root test against I ( d ) alternatives.     

THE CHOICE BETWEEN SETS OF REGRESSORS

This paper examines the choice of critical values for testing both non-sequential and nested sequential sets of constraints in the standard linear regression model. Modest increases in (e.g.) t-ratio critical values relative to their one-off values are often sufficient to maintain proper size. A Bayesian decision-theoretic approach, highlighted by the Schwarz (1978) criterion, provides a framework for deriving consistency and asymptotic local power properties of both forms of testing (data mining) algorithms.     

Estimating conditional tail expectation with actuarial applications in view

We develop statistical inferential tools for estimating and comparing conditional tail expectation (CTE) functions, which are of considerable interest in actuarial science. In particular, we construct estimators for the CTE functions, develop the necessary asymptotic theory for the estimators, and then use the theory for constructing confidence intervals and bands for the functions. Both parametric and non-parametric approaches are explored. Simulation studies illustrate the performance of estimators in various situations. Results are obtained under minimal assumptions, and the general Vervaat process plays a crucial role in achieving these goals.     

Lower bounds on expectations of positive L-statistics from without-replacement models

We consider samples drawn without replacement from finite populations. We establish optimal lower non-negative and upper non-positive bounds on the expectations of linear combinations of order statistics centered about the population mean in units generated by the population central absolute moments of various orders. We also specify the general results for important examples of sample extremes, Gini mean differences and sample range. The paper completes the results of Papadatos and Rychlik [2004. Bounds on expectations of L-statistics from without replacement samples. J. Statist. Plann. Inference 124, 317–336], where sharp negative lower and positive upper bounds on the expectations of the combinations were presented for the without-replacement samples.     

Estimating ordered parameters by linear programming

There are many models that require the estimation of a set of ordered parameters. For example, multivariate analysis of variance often is formulated as testing for the equality of the parameters versus an ordered alternative. This problem, referred to as isotonic inference, constrained inference, or isotonic regression, has led to the development of general solutions, not often easy to apply in special models. In this expository paper, we study the special case of a separable convex quadratic programming problem for which the optimality conditions lead to a readily solved linear complementarity problem in the Lagrange multipliers, and subsequently to an equivalent linear programming problem, whose solution can be used to recover the solution of the original isotonic problem. The method can be applied to estimating ordered correlations, ordered binomial probabilities, ordered Poisson parameters, ordered exponential scale parameters, or ordered risk differences.     

On the distribution of quadratic functionals of the ordinary and fractional Brownian motions

In this paper we discuss some distributional properties of quadratic functionals of the ordinary and fractional Brownian motions (fBms). As far as the ordinary Brownian motion (Bm) is concerned, those properties have been established extensively. A transition from the Bm to the fBm is not straightforward. Some difficulties associated with dealing with the fBm are explained, and a way to solving the problem is indicated, and some conjectures are given.     

No mood effects in the field: The case of car inspections

We study mood effect in the field to measure its economic impact and address shortcomings in the existing literature, which typically uses one single mood proxy and ignores selection effects. Using over 50 million car inspections in Sweden and England and multiple mood proxies, we study whether car inspectors are more lenient on good mood days and if car owners self-select into those days. We find evidence of a “Friday effect” in England and a small selection bias, but no support for consistent mood effect. Our findings highlight the importance of considering the expectations of rational actors who may exploit mood effect and the need to study mood in the field using different settings and multiple proxies to avoid hasty conclusions.     

‘Born this Way’? Prenatal exposure to testosterone may determine behavior in competition and conflict

Fetal exposure to sex hormones can have long lasting effects on human behavior. The second-to-fourth digit ratio (DR) is considered a putative marker for prenatal exposure to testosterone (vs estrogens), with higher exposure resulting in lower DR. Even though testosterone is theoretically related to competition, the role of DR in human behavior is debated; and in situations such as bilateral conflict is unknown. We investigate this through a laboratory experiment using a repeated 2-person Tullock contest played in fixed same-gender pairs. Based on a previously obtained large sample of student subjects, we selectively invited participants to the laboratory if their right-hand DR was in the top (High-DR) or bottom (Low-DR) tercile for their gender. Unbeknownst to the subjects, we performed a controlled match of the DR types (Low-Low, Low-High, High-High). This novel methodology allows us to analyze the causal effect of DR on behavior for the first time in the literature. We find that Low-DR (vs High-DR) males compete more aggressively regardless of the counterpart’s type. For females’ conflict behavior, the counterpart’s type matters more than the decision-maker’s type: Low-DRs are non-significantly more aggressive but every-one is more aggressive against High-DRs. Limitations due to sample size are discussed.