DS-optimal designs in one way ANOVA

Characterization and construction of optimal designs using the familiar optimality criteria, for example A-, D- and E-optimality are well studied in the literature. However the study of the Distance Optimality (DS-) criterion introduced by Sinha (1970) has very recently drawn attention of researchers. In the present article, we consider the singularly estimable full rank problem of estimating the full set of elementary treatment contrasts using the DS optimality criterion in the set up of a one way ANOVA model. Using a limit argument it turns out that a CRD in which difference between any two allocation numbers is at the most unity is uniquely DS-optimal. Acknowledgement. We are thankful to Prof. B. K. Sinha for suggesting the problem to us and many helpful discussions with him. We are also thankful to the referees for drawing our attention to the reference of Bischoff (1995) and many helpful comments.     

Budget deficits and real interest rates: Updated empirical evidence on causality

Using error-correction model (ECM) estimation, the paper empirically examines the causality relationship between the federal government budget deficit and the ex ante real interest rate yield on high grade long term tax free municipal bonds in the U.S. To clarify this deficit or interest rate relationship, the budget deficit is measured by the primary budget deficit, which excludes net interest payments by the Treasury. In a model that includes federal personal income tax rates and net international capital flows, as well as money supply growth, the ECM estimates strongly suggest a bi-directional relationship between the primary budget deficit and the ex ante real interest rate yield. Budget deficits apparently do matter! William Simon's concerns were justified.The author is indebted to P. A. V. B. Swamy for ideas and helpful suggestions and Will Perry for data assembly and processing.     

Time Series Concepts for Conditional Distributions*

The paper asks the question – as time series analysis moves from consideration of conditional mean values and variances to unconditional distributions, do some of the familiar concepts devised for the first two moments continue to be helpful in the more general area? Most seem to generalize fairly easy, such as the concepts of breaks, seasonality, trends and regime switching. Forecasting is more difficult, as forecasts become distributions, as do forecast errors. Persistence can be defined and also common factors by using the idea of a copula. Aggregation is more difficult but causality and controllability can be defined. The study of the time series of quantiles becomes more relevant.