Omitted Variables (omitted + variable)

Distribution by Scientific Domains


Selected Abstracts


OMITTED VARIABLES, CONFIDENCE INTERVALS, AND THE PRODUCTIVITY OF EXCHANGE RATES

PACIFIC ECONOMIC REVIEW, Issue 1 2007
Jonathan E. Leightner
This paper develops confidence intervals for BD-RTPLS and uses BD-RTPLS to estimate the relationship between the exchange rate (e) and gross domestic product (GDP) using annual data from 1984 to 2000 for 23 developing Asian and Pacific countries. BD-RTPLS produces estimates for the exchange rate multiplier (dGDP/de) for these countries and shows how omitted variables affected these multipliers across countries and over time. [source]


Omitted variables in longitudinal data models

THE CANADIAN JOURNAL OF STATISTICS, Issue 4 2001
Edward W. Frees
Abstract The omission of important variables is a well-known model specification issue in regression analysis and mixed linear models. The author considers longitudinal data models that are special cases of the mixed linear models; in particular, they are linear models of repeated observations on a subject. Models of omitted variables have origins in both the econometrics and biostatistics literatures. The author describes regression coefficient estimators that are robust to and that provide the basis for detecting the influence of certain types of omitted variables. New robust estimators and omitted variable tests are introduced and illustrated with a case study that investigates the determinants of tax liability. [source]


Scale and the Scale Effect in Market-based Accounting Research

JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 1-2 2003
Peter D. Easton
The nature of the data we usually encounter in market-based accounting research is such that the results of the regressions of market capitalization on financial statement variables (referred to ,price-levels' regressions) are driven by a relatively small subset of the very largest firms in the sample. We refer to this overwhelming influence of the largest firms as the ,scale effect'. This effect is more than heteroscedasticity. It arises due to the non-linearity in the relation between market capitalization and the financial statement variables. We present the case that scale is market capitalization rather than a correlated omitted variable. Since scale is market capitalization, we advocate its use as a deflator in a regression estimated using weighted least squares. This regression overcomes the scale effect and the resultant regression residuals are more economically meaningful. Christie's (1987) depiction of scale is the same as ours but he advocates the use of the returns regression specification in order to avoid scale effects. We agree that returns regressions should be used unless the research question calls for a price-levels regression. [source]


Does Private Money Buy Public Policy?

JOURNAL OF ECONOMICS & MANAGEMENT STRATEGY, Issue 3 2007
Campaign Contributions, Regulatory Outcomes in Telecommunications
To what extent can market participants affect the outcomes of regulatory policy? In this paper, we study the effects of one potential source of influence,campaign contributions,from competing interests in the local telecommunications industry, on regulatory policy decisions of state public utility commissions. Our work is unique in that we test the effects of campaign contributions on measurable policy outcomes. This stands in stark relief against most of the existing literature, which examines potentially noisier measures of policy outcomes,such as the roll-call votes of legislators, to examine how private money may influence public policy. By moving to more direct measures of policy effects, and using a unique new dataset, we find, in contrast to much of the literature on campaign contributions, that there is a significant effect of private money on regulatory outcomes. This result is robust to numerous alternative model specifications. We also assess the extent of omitted variable bias that would have to exist to obviate the estimated result. We find that for our result to be spurious, omitted variables would have to explain more than five times the variation in the mix of private money as is explained by the variables included in our analysis. We consider this to be very unlikely. [source]


Crime and Labour Market Opportunities in Italy (1993,2002)

LABOUR, Issue 4 2006
Paolo Buonanno
Using regional data over the period 1993,2002, we study the impact of wages and unemployment on different types of crime. To mitigate omitted variables bias, we control extensively for demographic and socio-economic variables. Empirical results suggest that unemployment has a large and positive effect on crime rates in southern regions. Our results are robust to model specification, endogeneity, changes in the classification of crimes, and finally, to alternative definitions of unemployment. [source]


Income, Location and Default: Some Implications for Community Lending

REAL ESTATE ECONOMICS, Issue 3 2000
Robert Van Order
This paper investigates differences in default losses across income groups and neighborhoods, in an effort to see if there are significant differences between default experience on loans to low-income households or low-income neighborhoods and other loans. We find that while defaults and losses are somewhat higher in low-income neighborhoods, default behavior is similar in the sense that responses to negative equity are similar across neighborhoods, and remaining differences are small and might be explained by omitted variables such as those measuring credit history. [source]


Omitted variables in longitudinal data models

THE CANADIAN JOURNAL OF STATISTICS, Issue 4 2001
Edward W. Frees
Abstract The omission of important variables is a well-known model specification issue in regression analysis and mixed linear models. The author considers longitudinal data models that are special cases of the mixed linear models; in particular, they are linear models of repeated observations on a subject. Models of omitted variables have origins in both the econometrics and biostatistics literatures. The author describes regression coefficient estimators that are robust to and that provide the basis for detecting the influence of certain types of omitted variables. New robust estimators and omitted variable tests are introduced and illustrated with a case study that investigates the determinants of tax liability. [source]