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This article develops a new identification procedure to estimate the contemporaneous relation between monetary policy and the stock market within a vector autoregression (VAR) framework. The approach combines high-frequency data from the futures market with the VAR methodology to circumvent exclusion restrictions and achieve identification. Our analysis casts doubt on VAR models imposing a recursive structure between innovations in policy rates and stock returns. We find that a tightening in policy rates has a negative impact on stock prices and that the Federal Reserve (Fed) has responded significantly to movements in the stock market. Estimates are robust to various model specifications.     

The Value of Information to the Acidic Deposition Debates

This article is addressed to those interested in how Bayesian approaches can be brought to bear on research and development planning and management issues. It provides a conceptual framework for estimating the value of information to environmental policy decisions. The methodology is applied to assess the expected value of research concerning the effects of acidic deposition on forests. To calculate the expected value of research requires modeling the possible actions of policymakers under conditions of uncertainty. Information is potentially valuable only if it leads to actions that differ from the actions that would be taken without the information. The relevant issue is how research on forest effects would change choices of emissions controls from those that would be made in the absence of such research. The approach taken is to model information with a likelihood function embedded in a decision tree describing possible policy options. The value of information is then calculated as a function of information accuracy. The results illustrate how accurate the information must be to have an impact on the choice of policy options. The results also illustrate situations in which additional research can have a negative value.     

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Bonferroni inequalities often provide tight upper and lower bounds for the probability of a union of events. The bounds are especially useful when this probability is difficult to compute exactly. There are situations, however, in which the Bonferroni approach gives very poor results. An example is given in which the upper and lower Bonferroni bounds are far from the probability they seek to approximate and successive bounds do not converge. Even an improved first upper Bonferroni bound may not be close to the probability of the union of events.     

Statistics for Quality and Productivity: A New Graduate-Level Statistics Course

The growing popular realization that American product quality and productivity are no longer without challenge for world leadership presents an opportunity for the American statistical community to make stronger contributions to sound industrial practice than it has in the past. Management consultants, such as Deming and Juran, are promoting philosophies that contain strong statistical components and are being heard by top U.S. executives. There are thus growing opportunities for industrial statisticians. Upon reviewing the content of typical graduate-level statistical quality control courses and books in the light of the present situation, we find them to be inadequate and in some cases to suffer from inappropriate emphases. In this article we discuss our perceptions of what is needed in the way of a new graduate-level course in statistics for quality and productivity (SQP). We further offer for discussion a syllabus for such a course (which is a modification of one used at Iowa State in the 1983 spring semester), some comments on how specific topics might be approached, and also a partially annotated list of references for material that we believe belongs in a modern SQP course.     

Use of the Correlation Coefficient with Normal Probability Plots

The use of the correlation coefficient is suggested as a technique for summarizing and objectively evaluating the information contained in probability plots. Goodness-of-fit tests are constructed using this technique for several commonly used plotting positions for the normal distribution. Empirical sampling methods are used to construct the null distribution for these tests, which are then compared on the basis of power against certain nonnormal alternatives. Commonly used regression tests of fit are also included in the comparisons. The results indicate that use of the plotting position p_i = (i - .375)/(n + .25) yields a competitive regression test of fit for normality.     

Computing Marginal Likelihoods via Posterior Sampling

This article presents a novel and simple approach to the estimation of a marginal likelihood, in a Bayesian context. The estimate is based on a Markov chain output which provides samples from the posterior distribution and an additional latent variable. It is the mean of this latent variable which provides the estimate for the value of the marginal likelihood.     

A four-moment approach and other practical solutions to the behrens-f1sher problem

Fixed sample size approximately similar tests for the Behrens-Fisher problem are studied and compared with various other tests suggested in current sttistical methodelogy texts. Several fourmoment approxiamtely similar tests are developed and offered as alternatives. These tests are shown to be good practical solutions which are easily implemented in practice.     

A Bootstrap Stationarity Test for Predictive Regression Invalidity

In order for predictive regression tests to deliver asymptotically valid inference, account has to be taken of the degree of persistence of the predictors under test. There is also a maintained assumption that any predictability in the variable of interest is purely attributable to the predictors under test. Violation of this assumption by the omission of relevant persistent predictors renders the predictive regression invalid, and potentially also spurious, as both the finite sample and asymptotic size of the predictability tests can be significantly inflated. In response, we propose a predictive regression invalidity test based on a stationarity testing approach. To allow for an unknown degree of persistence in the putative predictors, and for heteroscedasticity in the data, we implement our proposed test using a fixed regressor wild bootstrap procedure. We demonstrate the asymptotic validity of the proposed bootstrap test by proving that the limit distribution of the bootstrap statistic, conditional on the data, is the same as the limit null distribution of the statistic computed on the original data, conditional on the predictor. This corrects a long-standing error in the bootstrap literature whereby it is incorrectly argued that for strongly persistent regressors and test statistics akin to ours the validity of the fixed regressor bootstrap obtains through equivalence to an unconditional limit distribution. Our bootstrap results are therefore of interest in their own right and are likely to have applications beyond the present context. An illustration is given by reexamining the results relating to U.S. stock returns data in Campbell and Yogo (2006 Campbell, J. Y. and Yogo, M. (2006), “Efficient Tests of Stock Return Predictability,” Journal of Financial Economics, 81, 27–60.[Crossref], [Web of Science ®] , [Google Scholar]). Supplementary materials for this article are available online.     

Estimation in an empirical bayes model for longitudinal and cross‐sectionally clustered binary data

Some studies generate data that can be grouped into clusters in more than one way. Consider for instance a smoking prevention study in which responses on smoking status are collected over several years in a cohort of students from a number of different schools. This yields longitudinal data, also cross‐sectionaliy clustered in schools. The authors present a model for analyzing binary data of this type, combining generalized estimating equations and estimation of random effects to address the longitudinal and cross‐sectional dependence, respectively. The estimation procedure for this model is discussed, as are the results of a simulation study used to investigate the properties of its estimates. An illustration using data from a smoking prevention trial is given.     

Between Neo-liberalism and Neo-indigenismo: Reconstructing National Identity in Mexico

Heightened integration with the US coupled with the rise of an indigenous movement have challenged and strained long-held images regarding the US and the indigenous within Mexico. As a result, Mexico finds itself facing the difficult task of re-evaluating and reconstructing its national identity. This paper explores the traditional images of the US and the Indian shaping national identity in Mexico and the challenges posed today by neo-liberalism and neo-indigenismo. It sets out a range of current issues, explores the linkages and contradictions, and examines the state of national reform. Fundamentally, the paper strives to raise important theoretical questions and hence set the stage for further research into these issues.