Regression using mixed annual and quarterly data

By consideration of either Generalised Least-Squares of Theil-Goldberger estimates of equations for which data on the dependent variable is available for a subset of the sample period on only an annual basis, it is shown that best estimates of the equation coefficients may be obtained by Ordinary Least-Squares estimation over the complete set of quarterly observations with quarterly averages substituted for the missing quarterly data, and symmetrically for the corresponding observations on the regressor variables. The approach is extended to the Two-Stage Least-Squares case, and to the (structurally or stochastically) autoregressive case.     

Forecasting first births

This study is concerned with one aspect of the family cycle, namely, the transition from young married to young married with small children. The focus is on developing models to forecast entries into this latter stage for the purpose of marketing research. "Using ordinary least squares, forecasting models were estimated for (1) total number of first births, (2) number of white first births, and (3) number of nonwhite first births." Models are estimated for both the United States and California using data from official sources.     

Method in Experiment: Rhetoric and Reality

The methodological ideal of experimentalists, E, is easily stated: derive a testable hypothesis, H, from a well-specified theory, T; implement experiments with a design; implicitly in the latter are auxiliary hypotheses, A, that surface in the review/discussion of completed research reports (payoffs are 'adequate,' Ss are 'relevant,' instructions, context are 'clear,' etc.). We want to be able to conclude, if statistical test outcomes support not-H, that T is 'falsified.' But this is not what we do; rather we ask if there is a flaw in the test, i.e. not-A is supported, and we do more experiments. This is good practice—much better than the statistical rhetoric of falsificationism. Undesigned social processes allow E to accumulate technical and instrumental knowledge that drive the reduction of experimental error and constitute a more coherent methodology than falsificationism.     

The relationship between technology and science: Some historical and philosophical reflections. Part I

This essay offers a detailed review of the literature on the relationship between technology and science. It is in two parts. Part I begins by describing science and technology, and the differences between them. It then discusses the commonly-held technology-as-applied-science (TAS) view; the origins of this view, the support for it, and the strong historical and philosophical challenges to it, beginning more than half a century ago, are explored. The development of the steam engine is then offered as a brief case study to illustrate that science-technology relations are more complex than implied by the TAS view. Part I concludes with a consideration of ontological arguments supporting the reverse view, namely that technology is often a necessary precursor to science.Part II, to be published in a following issue, explores some of the consequences of the TAS view. One consequence is that it has generated a story-line in which scientific ideas are emphasised and other factors necessary for technological innovation have been down-played. Another consequence is that, even in cases where technology does apply scientific knowledge, the process of application is often considered obvious; the difficulties of translating ideas into artefacts may not be appreciated. The essay argues for the telling of a more complex story of science-technology relations, one which recognises their historical independence in the past, and their mutual, two-way interaction in many modern fields of endeavour. It concludes with a consideration of some economic and educational implications.