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Libraries and the information community have moved rapidly into an era of powerful networked scholarly workstations, large quantities of information accessible in electronic formats, and dispersed information sources connected to regional and national networks. This rich diversity poses new challenges for the provision of appropriate reference services. The University of Texas at Austin General Libraries successfully implemented and tested a prototype solution to the problem of providing reference assistance to scholars who are accessing networked information resources and who are at locations remote from expert librarians. Librarians were able to intervene directly in information access and retrieval sessions, remotely assisting the user during the real‐time, online process. The testbed for the project was a CD‐ROM network delivering U.S. government information to DOS workstations via Ethernets connected to a routed TCP/IP wide‐area network and utilizing off‐the‐shelf remote control software. Although problems with existing technology were discovered, this mode of providing reference assistance is a valid model for future services.

Looks at contrasting views of the future of the British Library Document Supply Centre (BLDSC); that it will become obsolete or, conversely, grow in importance. Explores the problems of co‐operative collection development, its economics, politics and terminology in both theory and practice. Examines methods for evaluating the costs of interlending, and demonstrates the limitations of a single price structure. Shows how reciprocal borrowing is becoming an increasingly popular solution to collection development.

Library users continue to experience difficulty in using the online catalog, particularly in the area of subject access. This project describes a test of a new design for subject access to online catalogs. The new design requires a wide range of subject searching capabilities and search trees to govern the system's selection of searching capabilities in response to user queries. Is the performance of search trees superior to subject searching approaches chosen at random? This project is geared to make that determination.

The purpose of this paper is to discuss the need for continuing professional development for librarians in academic libraries in general and at Rhodes University Library, South Africa, in particular. It aims to describe the planning, design, implementation and evaluation of a staff‐development and training pilot programme for professional librarians at Rhodes Library.

A group of 15 librarians, drawn from the library staff complement, participated in an eight‐session training course developed by information services librarians. The course, which covered basic information‐finding skills using a variety of research databases and offered an introduction to concepts in twenty‐first century academic librarianship, was presented using course management software in a face‐to‐face environment and required homework exercises and the completion of a two‐hour final test.

Results of the project demonstrated the urgent need for such development programmes for professional library staff. Of the participants, 80 per cent completed the course. The final test results indicated below average database search skills and an inability to think laterally. An unexpected finding was a lack of IT competencies. Important lessons were learned with regard to course‐construction, content and timing.

The paper illustrates that, by testing a variety of competencies expected of professional academic librarians, this programme provided invaluable data and guidelines on which further continuing staff development courses could be based.

Applied econometric analysis is often performed using data collected from large-scale surveys. These surveys use complex sampling plans in order to reduce costs and increase the estimation efficiency for subgroups of the population. These sampling plans result in unequal inclusion probabilities across units in the population. The purpose of this paper is to derive the asymptotic properties of a design-based nonparametric regression estimator under a combined inference framework. The nonparametric regression estimator considered is the local constant estimator. This work contributes to the literature in two ways. First, it derives the asymptotic properties for the multivariate mixed-data case, including the asymptotic normality of the estimator. Second, I use least squares cross-validation for selecting the bandwidths for both continuous and discrete variables. I run Monte Carlo simulations designed to assess the finite-sample performance of the design-based local constant estimator versus the traditional local constant estimator for three sampling methods, namely, simple random sampling, exogenous stratification and endogenous stratification. Simulation results show that the estimator is consistent and that efficiency gains can be achieved by weighting observations by the inverse of their inclusion probabilities if the sampling is endogenous.

The link between the magnitude of a bandwidth and the relevance of the corresponding covariate in a regression has recently garnered theoretical attention. Theory suggests that variables included erroneously in a regression will be automatically removed when bandwidths are selected via cross-validation procedure. However, the connections between the bandwidths of the variables that are smoothed away and the insights from these same variables when properly tested for statistical significance have not been previously studied. This paper proposes a variety of simulation exercises to examine the relative performance of both cross-validated bandwidths and individual and joint tests of significance. We focus on settings where the hypothesis of interest may focus on a single data type (e.g., continuous only) or a mix of discrete and continuous variables. Moreover, we propose an extension of a well-known kernel smoothing significance test to handle mixed data types. Our results suggest that individual tests of significance and variable-specific bandwidths are very close in performance, but joint tests and joint bandwidth recognition produce substantially different results. This underscores the importance of testing for joint significance when one is trying to arrive at the final nonparametric model of interest.

When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator can severely over-reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not too small, they can either over-reject or under-reject seriously when it is. In a previous paper, we showed that procedures based on randomization inference (RI) can work well in such cases. However, RI can be impractical when the number of possible randomizations is small. We propose a bootstrap-based alternative to RI, which mitigates the discrete nature of RI p values in the few-clusters case. We also compare it to two other procedures. None of them works perfectly when the number of clusters is very small, but they can work surprisingly well.

We propose a local linear functional coefficient estimator that admits a mix of discrete and continuous data for stationary time series. Under weak conditions our estimator is asymptotically normally distributed. A small set of simulation studies is carried out to illustrate the finite sample performance of our estimator. As an application, we estimate a wage determination function that explicitly allows the return to education to depend on other variables. We find evidence of the complex interacting patterns among the regressors in the wage equation, such as increasing returns to education when experience is very low, high return to education for workers with several years of experience, and diminishing returns to education when experience is high. Compared with the commonly used parametric and semiparametric methods, our estimator performs better in both goodness-of-fit and in yielding economically interesting interpretation.

Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods, and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader, and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.

Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.