The Quantitative Significance of the Lucas Critique

Doan, Litterman, and Sims (DLS) have suggested using conditional forecasts to do policy analysis with Bayesian vector autoregression (BVAR) models. Their method seems to violate the Lucas critique, which implies that coefficients of a BVAR model will change when there is a change in policy rules. In this article, we attempt to determine whether the Lucas critique is important quantitatively in a BVAR macro model that we construct. We find evidence following two candidate policy rule changes of significant coefficient instability and of a deterioration in the performance of the DLS method.     

Serials and the OCLC Acquisitions Subsystem

Abstract

Academic library consortia, faced with requirements of assessment and accountability, are increasingly pressured to measure the impact of their networked electronic services. Studies on usage of electronic library resources and electronic journals, in particular, proliferate; relatively few focus on the academic library consortium. This paper gives an overview of one assessment tool, MINES for Libraries®, as implemented in the Scholars Portal service of the Ontario Council of University Libraries (OCUL). The novel contributions of this study are twofold: firstly, the creation of peer groups within an academic consortium for comparison purposes, and secondly, the use of regression analysis to explore the correlation between usage and three separate variables outside the MINES survey — library print holdings, library acquisitions budget, and sponsored research revenue.     

A Sample Size Formula for a Non-Central t Test

An elementary method of proof of the mode, median, and mean inequality is given for skewed, unimodal distributions of continuous random variables. A proof of the inequality for the gamma, F, and beta random variables is sketched.     

New Developments in Statistical Computing: New Bias Evaluation Features of EXPLOR—A Program for Assessing Experimental Design Properties

Longitudinal investigations play an increasingly prominent role in biomedical research. Much of the literature on specifying and fitting linear models for serial measurements uses methods based on the standard multivariate linear model. This article proposes a more flexible approach that permits specification of the expected response as an arbitrary linear function of fixed and time-varying covariates so that mean-value functions can be derived from subject matter considerations rather than methodological constraints. Three families of models for the covariance function are discussed: multivariate, autoregressive, and random effects. Illustrations demonstrate the flexibility and utility of the proposed approach to longitudinal analysis.     

Graphical Methods for Data Presentation: Full Scale Breaks,Dot Charts,and Multibased Logging

Experimentation with graphical methods for data presentation is important for improving graphical communication in science. Several methods—full scale breaks, dot charts, and multibased logging—are discussed. Full scale breaks are suggested as replacements for partial scale breaks, since partial breaks can fail to provide a forceful visual indication of a change in the scale. Dot charts show data that have labels and are replacements for bar charts; the new charts can be used in a wider variety of circumstances and allow more effective visual decoding of the quantitative information. Logarithms are powerful tools for data presentation; base 2 or base e is often more effective than the commonly used base 10.     

A stepwise discrete variable selection procedure

A stepwise variable selection procedure for multinomial discrimination is presented and discussed. Based upon the work of Kullback and Hills, stopping rules are proposed and illustrated for a set of data on communication buyer behavior.