Terry E. Dielman

An Examination of Investor Behavior during Periods of Large Dividend Changes

The “information content of dividends†hypothesis (which emanates from the early work of Lintner [17] and Miller and Modigliani [18]) states that managers use dividend announcements to signal their beliefs about the prospects of the firm. Thus, an announcement of an increase in the dividend rate reflects managements belief that the firms cash flows in the foreseeable future will be sufficiently high to sustain payment at the increased rate. Similarly, an announcement of a dividend decrease occurs only when management is extremely pessimistic about the probability that future cash flows will be sufficient to continue dividends at their present rate. The theoretical implication of the “information content†hypothesisis that the announcement of a dividend (or change in dividend) conveys information about managements assessment of the firms prospects, that this information is different from other information provided by management, and this information may cause an immediate investor reaction, including, but not limited to, price changes.

Pooled cross-sectional and time series data analysis

A review of methods for estimating multivariate relationships of individual entities in a data base and for summarizing these relationships. Focuses on methodologies such as classical pooling, error components, analysis of covariance, seemingly unrelated regressions, and random coefficient regressio

Pooled Cross-Sectional and Time Series Data: A Survey of Current Statistical Methodology

Abstract A data base that provides a multivariate statistical history for each of a number of individual entities is called a pooled cross-sectional and time series data base in the econometrics literature. In marketing and survey literature the terms panel data or longitudinal data are often used. In management science a convenient term might be management data base. Such a data base provides a particularly rich environment for statistical analysis. This article reviews methods for estimating multivariate relationships particular to each individual entity and for summarizing these relationships for a number of individuals. Inference to a larger population when the data base is viewed as a sample is also considered.

Least absolute value regression: recent contributions

This article provides a review of research involving least absolute value (LAV) regression. The review is concentrated primarily on research published since the survey article by Dielman (Dielman, T. E. (1984). Least absolute value estimation in regression models: An annotated bibliography. Communications in Statistics – Theory and Methods, 4, 513–541.) and includes articles on LAV estimation as applied to linear and non-linear regression models and in systems of equations. Some topics included are computation of LAV estimates, properties of LAV estimators and inferences in LAV regression. In addition, recent work in some areas related to LAV regression will be discussed.

A comparison of quantile estimators

Ten nonparametric estimators of quantiles are compared in small samples by Monte Carlo simulation methods. The estimators are compared by using properties such as mean square error and mean absolute deviation. Nine distributions are used for the comparisons and include long-tailed distributions (e.g., Laplace), short-tailed distributions (e.g., uniform), and skewed distributions (e.g., exponential)

Bootstrapping in least absolute value regression: an application to hypothesis testing

A Monte Carlo simulation is used to study the performance of hypothesis tests for regression coefficients when least absolute value regression methods are used. In small samples, the results of the simulation suggest that using the bootstrap method to compute standard errors will provide improved test performance

Tests of linear hypotheses and LAV estimation: a Monte Carlo comparison

A Monte Carlo simulation is used to study the performance of the Wald, likelihood ratio and Lagrange multiplier tests for regression coefficients when least absolute value regression is used. The simulation results provide support for use of the Lagrange multiplier test, especially when certain computational advantages are considered.

A further comparison of tests of hypotheses in LAV regression

Abstract This study compares five alternate test procedures for the significance of coefficients in LAV regression. The WALD and LR tests using the SECI estimator of λ proposed by McKean and Schrader (Commun. Statist. - Simulation Comput. 13 (1984) p. 751–773) were examined as well as versions of these tests using bootstrap estimates of λ (BWALD and BLR) and the LM test. There is some evidence that the empirical level of significance for the LM test more nearly conforms to the expected level (5% used in study) than the observed levels do for the other tests. The two tests that appear to perform best in the experiment are the LR test using the SECI estimator of λ and the LM test. The bootstrap version of the LR test is computationally more expensive and appears to be no better (and perhaps worse in terms of the empirical size) than the traditional version. The WALD test appears inferior in the computations in either its traditional or bootstrap versions.

A bootstrap approach to hypothesis testing in least absolute value regression

This study compares three alternative procedures for testing the significance of coefficients in least absolute value (LAV) regression, in the context of small samples. The three tests considered are: the likelihood ratio test using an estimator of the nuisance parameter proposed by McKean and Schrader (Comm. Statist. Simulation Comput. 13 (1984)), the Lagrange multiplier test, and a bootstrap test in which critical values of the test statistic are obtained by resampling. Comparisons among the tests are made by considering both observed significance levels and power. The bootstrap test used in this study performs well, compared to the other two tests. This result is in contrast to results, involving a somewhat different use of the bootstrap technique, obtained by Dielman and Pfaffenberger (Comput. Statist. Data Anal. 14 (1992)), and suggests that the use of the technique proposed in this paper has strong potential for applicability in hypothesis testing for LAV regression.

Computational Algorithms for Calculating Least Absolute Value and Chebyshev Estimates for Multiple Regression

SYNOPTIC ABSTRACTLeast absolute value (LAV) and Chebyshev estimation are two possible alternatives to least squares estimation in multiple regression models. This paper reviews the historical development of these two alternatives with special attention placed on the development of algorithms for producing LAV and Chebyshev estimates of the regression parameters. Recent algorithmic improvements are reviewed, sources for obtaining computer programs are identified and recommendations are made for the most efficient algorithm for specific cases. In the final section suggestions for future research are made for the development and refinement of computational algorithms and inference procedures.