Use Of Dummy Variables Research Articles

Determinants of Senior Center Attendance and Participation

This study determined factors associated with two major dimensions of senior center involvement ; (a) frequency and duration of attendance and (b) use of activities and services, using participants (N = 623) from 15 centers located in a county in a Midwestern state. Results of multiple regression, which controlled for the effect of centers through use of dummy variables, showed that those who felt that the meal was important to daily food intake and who lived closer to the center attended more freguently, and those who were older had attended longer. Respondents who participated in activities had higher educational levels, and those who used services were less mobile, made friends at the senior center, and had hrgher life satisfaction. The multiple regression as well as subsequent analyses, however, demonstrated that the variability among senior centers rather than participant characteristics had the larger effect on the dimensions of senior center involvement.

A Continuously Varying Parameter Model of Downstream Hydraulic Geometry

Few studies have attempted to examine explicitly the factors that produce variation in hydraulic geometry parameters. A continuously varying parameter model of downstream hydraulic geometry is formulated to address this issue. Parameter variation is viewed as a function of channel sediment characteristics and flood magnitude. Results for the Missouri River basin show that these factors are strongly associated with variation in hydraulic geometry coefficients and exponents. The results also indicate that bivariate relationships are biased by these associations. The analysis yields site‐specific parameters that uniquely describe the relationship between active channel form and mean discharge at a particular location within the basin. The continuously varying parameter approach has several advantages: (1) it isolates the separate influences of each explanatory variable on each hydraulic geometry parameter, (2) it maintains the precision of parameter estimates by including the entire data set in the analysis, and (3) it allows testing of the conception that hydraulic geometry parameters vary continuously rather than discretely, yet permits analysis of discrete change through the use of dummy variables.

The use of dummy variables in consumption models

In this paper the consumption model in Winder and Palm [1989] is subjected to a sensitivity analysis. Small and reasonable change in several dummy variables provide that the original model with a moving planning horizon becomes observationally equivalent with a random walk specification.

Taking the con out of conservation program evaluation

A crucial step in determining the cost-effectiveness of a utility conservation program is to measure the energy savings that can be directly attributed to the program. A number of alternative methods have been proposed to measure this energy savings. The alternative methods differ in terms of their complexity, implementation cost and their ability to address important methodological issues arising during program evaluation. The important methodological issues include the need to control for factors influencing energy conservation other than program participation and the need to test for self-selection bias, a problem inherent in the evaluation of most voluntary programs. This paper focuses on three methods: (1) the simple comparison of the average conservation levels of program participants and non-participants; (2) more advanced regression techniques that differentiate between participants and non-participants through the use of dummy variables; and (3) the most advanced techniques, self-selection models, which explicitly analyze the interrelated decisions to participate in a program and to consequently reduce energy consumption. These three methods are described and critiqued at the theoretical level. In order to assess their empirical implications, the methods are then applied to the residential-conservation programs offered by a particular utility, Portland General Electric Company.

Prediction tests in limited dependent variable models

This paper discusses the problem of assessing the predictive power of a model in the class of limited dependent variable models. The use of dummy variables in this context in standard regression models has been discussed in Salkever (1976), however problems emerge if such an approach is taken when the dummy appears in a limit of an integral that has to be evaluated. The paper proposes tests of predictive power which circumvent these problems.

The inherent subjectivity of the apparently objective in research on ethnicity and class

Policy making in the area of immigration and affairs relies increasingly on the findings of social science research. A new conventional wisdom is emerging in sociological and economic studies. Its claim to objectivity is based on the use of a methodology which emphasises the use of sophisticated statistical methods, especially multiple regression models. The present paper suggests that this apparently objective approach is actually based on a set of of subjectivist and voluntaristic assumptions on: the nature of society, the character of the groups being examined, and on the practice and logic of scientific research. This type of analysis leads to the reconstitution of the ethnic group through the use of dummy variables, which mask many of the specific structural features of labour migrants. Moreover, the emphasis on multiple regression models makes it necessary to quantitify the variables to be examined. This involves a subjective decision by the researcher: either to give a numerical value to something for which there is no single correct form of quantification, or to exclude it altogether from the analysis. The approach is based on a human capital model, in which migrants are seen only as economic subjects in a rational market system. This blocks historical understanding of the way labour relations function in capitalist societies, in particular of the significance of labour migration, and of the role of segmentation of the labour market according to ethnciity and gender in class relations. This report is available at Research Online: http://ro.uow.edu.au/cmsocpapers/8 THE CENTRE FOR M ULTIC U LTU RAL STUDIES UNIVERSITY OF W O LLO NG O NG The Inherent Subjectiv ity

A Simultaneous Equations Model of Trade and Development with Dynamic Policy Simulations

SUMMARYThis article seeks to help resolve the serious theoretical controversy about the contribution of international trade to economic development by constructing a simultaneous‐equations model that captures the most important quantitative aspects of the relationship between trade and development, and testing it by pooling data for 52 nations (representing all of the developing nations for which the required data are available) from 1961 to the present. The model is estimated by Full Information Maximum Likelihood, validated by dynamic simulation and utilized for policy and other counterfactual simulations. By the use of dummy variables and Chow tests, shifts of the structural relationships and slope coefficients over time are also tested for. The study finds strong empirical support for trade being very important for development, but to be more in the nature of a handmaiden than of an engine of growth.

Regression analysis in health services research: the use of dummy variables.

Dummy variables frequently are used in regression analysis but often in an incorrect fashion. A brief review of examples in the medical care literature showed that the interpretation of dummy variable regression coefficients and their significance was often incorrect or unclear. This article shows how dummy variables can be used and assessed properly. The importance of testing for the joint effect of a group of dummy variables is stressed. It also gives a standard and useful extension of the dummy variable technique to testing for the effect of collections of variables.

Use of dummy variables in water resources studies

Dummy variables 1 and 0 used as an alternative to analyze the variance and covariance to compare the relationship among groups of observations were introduced. Seven-year data of organic soil subsidence in Everglades Agricultural Area related to water table depth and crop were used to demonstrate the techniques of application. Comparisons of the performance of this dummy variable technique with the standard technique were made. The results showed that the dummy variable regression not only can perform a good result as performed by the standard technique, but also can simultaneously test the difference between two regression lines whether due to slope coefficients or intercept terms.

The use of grafted polynomials and dummy variables in analyzing time series data.

Describes an innovative statistical method for analyzing single-S time series data. The S and data in the paper were selected from a larger study which applied operant conditioning to ambulation problems of severely handicapped patients and used positive verbal reinforcement. The method described is a variation of traditional multiple regression analysis described elsewhere as "grafted polynomials" (Draper & Smith, 1967; Fuller, Note 1). An extensive explanation of the use of dummy variables also is provided.

The use of econometric methods in the analysis of centrally planned economies: The SRI-WEFA experience

This paper outlines the macroeconomic conception of the centrally planned, economy and the econometric methodology which have guided the development of the SRI-WEFA Soviet Econometric Model. Following a brief description of the structure and properties of the model, the role of theory and the use of dummy variables are considered as central methodological issues in the application of econometric methods to centrally planned economies.

Estimating Models of Seasonal Market Response Using Dummy Variables

Researchers often suggest the usefulness of regression models of market response as aids to market decision making. Commonly, models which involve monthly, bimonthly, or quarterly data explicitly account for seasonal variations by the use of dummy variables. This note critically examines the use of dummy variables in seasonal models. The results of the examination indicate that although the commonly used seasonal shift model (seasonal dummy variables which influence only the intercept term of the model) may be successful in explaining the variance in market response, it may not explain it in a manner indicative of market behavior, and therefore may result in misleading implications for marketing decisions. Alternate seasonal specifications are discussed and statistical methods for selecting the appropriate model specification are considered.

A Note on Market Power and Returns to Stockholders

Brown, F. Stewart, Chief, Bureau of Power of Federal Power Commission, correspondence to the author dated July 29, 1969. Federal Power Commission, Statistics of Publicly Owned Electric Utilities in the United States (Washington, D.C., various years). , National Power Survey, 1964 Part I (Washington, D.C., 1964). Johnston, J., Statistical Cost Analysis (New York: McGraw-Hill Book Company, Inc., 1960). Leibenstein, Harvey, Allocative Efficiency vs. 'X-Efficiency,' American Economic Review 56 (June 1966), 392-413. Meyer, John R., Merton J. Peck, John Stenason, and Charles Zwick, Economics of Competition in the Transportation Industries (Cambridge, Mass.: Harvard University Press, 1959). Primeaux, Walter J., Jr., A Reexamination of the Monopoly Market Structure for Electric Utilities, in Almarin Phillips (ed.), Promoting Competition in Regulated Markets (Washington, D.C.: Brookings Institution, 1975), 175-200. Suits, Daniel B., The Use of Dummy Variables in Regression Equations, Journal of the American Statistical Association 52 (Dec. 1957), 548-551.

Quantitative structure-activity relationships. 2. A mixed approach, based on Hansch and Free-Wilson Analysis.

Based on the theoretical and numerical equivalence of Hansch's linear multiple regression model and the modified Free-Wilson model a mixed approach is developed. The mixed approach is a combination of both models which makes use of the advantages of each model and widens the applicability of Hansch and Free-Wilson analysis. The Free-Wilson approach now is applicable also in the case of parabolic dependence of biological activity on a particular physical property, e.g., log P or pi. A rational explanation is given for the use of dummy variables in Hansch equations and the derivation of Hansch correlations for de novo group contributions obtained from Free-Wilson analysis. Some examples illustrate the mixed approach and demonstrate its usefulness to establish biologically meaningful structure-activity relationships.

A note on colinearity in dummy variables

A recently published article in this journal, by Marcus Felson, questioned the use of dummy variables on coded data, in structure-seeking models. While the point he makes is a valid one, he tends to overstate the case, as he presented what is almost the worst of all possible results. This is illustrated in this note.

PIECEWISE APPROXIMATION FUNCTIONS AN EDUCATIONAL NOTE*

This paper gives a survey of piecewise approximation functions. Aside from the use of dummy variables, little research has employed such functions. Piecewise functions permit the dependent variable of a functional relationship to take on different forms with respect to the independent variable on various subintervals of the domain of the independent variable.Three types of piecewise approximation functions are considered: dummy variables, piecewise linear regressions and cubic splines. The theoretical and empirical properties of such functions are discussed within the context of an illustrative example involving the increase in ridership over time on urban mass transit systems. Dummy variables take account of shifts in the structural relation in a discontinuous fashion. Piecewise regressions and cubic splines allow for structural shifts without discontinuities arising.Tests are performed to determine whether a linear, quadratic, or cubic piecewise continuous function should be employed. In addition a test is employed to see if structural change occurs in the functional relation at the shift points.

TEMPORAL CROSS‐SECTION SPECIFICATIONS OF THE DEMAND FOR DEMAND DEPOSITS

OVER A DECADE has passed since Gurley and Shaw [5] alerted the economics profession to the importance of considering the implications of the growth of non-bank financial liabilities for monetary theory and policy. The empirical question at the heart of the issue concerning the effects of non-bank financial intermediaries focused on the degree of substitutability and the stability of the substitution relationship between demand deposits and other liquid assets. A large number of empirical studies were subsequently undertaken to examine the substitution relationships and the purpose of this paper is to inquire whether some consensus emerges from the empirical literature which examines demand functions for demand deposits utilizing pooled temporal cross-section data. Section I reviews and evaluates the existing cross-section evidence and suggests that while a fairly broad consensus emerges on the substitution relationships between demand deposits and other liquid assets, the differential use of dummy variables in the model specifications appears to be an important factor in explaining substantive variations between studies. Section II considers a covariance model of the demand for demand deposits which permit testing of alternative dummy specifications. Section III reports results based on a random coefficient model which is more restrictive than the covariance model with respect to temporal dummy variables but allows a richer specification of variation in behavior across states.

On the Dummy Variable Technique and Covariance Analysis in Testing Equality Among Sets of Coefficients in Linear Regressions: An Expository Note

Econometric research in the past two decades has vitnessed a considerable use of dummy variables in regression analysis. The analysis of covariance has long been a standard statistical technique to test the equality of coefficients in linear regressions. While students and researchers are generally aware of the close relationship between the two methods, they are often frustrated at choosing one method instead of the other in practice and wonder whether the two methods lead to the same test results. This note shows that the two methods are equivalent from the point of view of hypothesis testing.

How to rig your results with a fancy coding scheme

Two types of multivariate methods are distinguished from one another: structure-seeking methods and causal modeling. The former is particularly susceptible to coding artifacts based on improper use of dummy variables or of ranking procedures. Solutions for both problems are suggested.

DISCRETE VARIATIONS ACROSS SUBSETS OF PARAMETERS IN SIMULTANEOUS EQUATIONS MODELS

AbstractDummy variables have proven to be an analytically convenient and empirically productive means of handling econometric problems relating to seasonality, structural change and time series and cross section data pooling. Despite the advantageous of the approach there are few results regarding its use in simultaneous equation estimation. In this paper the problem of discrete parameter variations in simultaneous equation estimation is systematically investigated. The estimation procedures proposed are based on extensions of existing methods for seemingly unrelated regressions and two and three‐strage least squares. In addition to obtaining the desired estimators and their properties a number of convenient advantages resulting from the use of dummy variables to treat parameter variations across subsets of simultaneous equations are indicated.