Statistical Inference for Measures of Income Mobility / Statistische Inferenz zur Messung der Einkommensmobilität

1999 ◽  
Vol 218 (3-4) ◽  
Author(s):  
Mark Trede
Keyword(s):  
Panel Data ◽  
Confidence Intervals ◽  
Transition Matrix ◽  
Empirical Work ◽  
Confidence Regions ◽  
Statistical Techniques ◽  
Earnings Mobility ◽  
Accounting Period ◽  
Asymptotic Sampling ◽  
Empirical Illustration

SummaryThis paper reviews various mobility measures and establishes their asymptotic sampling distribution. The focus is on both transition matrix mobility measures and mobility measures which are based on the reduction in inequality occurring when the accounting period is extended. Statistical techniques are used to show the asymptotic normality of these measures and their variances. The empirical illustration examines the development of earnings mobility for both sexes in Germany between 1983 and 1992 using the Socio-Economic Panel data. It turns out that male earnings mobility fell during the eighties whereas the development of female earnings mobility is less clear. Comparing the levels of mobility females are more mobile than males, at least when inequality reduction mobility measures are employed. Considering the rather large number of observations the confidence intervals are often surprisingly wide. Therefore, confidence regions for mobility measures ought to be reported in empirical work whenever possible.

Trajectories of Sport Participation Among Children and Adolescents Across Different Socio-Economic Categories: Multilevel Findings From the National Longitudinal Survey of Children and Youth

2020 ◽  
Vol 37 (3) ◽  
pp. 264-268
Author(s):  
Tom Perks
Keyword(s):  
Panel Data ◽  
Children And Adolescents ◽  
Sport Participation ◽  
Economic Status ◽  
Empirical Work ◽  
Longitudinal Survey ◽  
Children And Youth ◽  
National Longitudinal Survey ◽  
Socio Economic Status ◽  
Over Time

Building upon prior theoretical and empirical work, this study explores the sport participation trajectories of children across different socio-economic status (SES) categories to assess the possibility of changes in the SES-sport participation relationship as children age. Using representative panel data from the National Longitudinal Survey of Children and Youth, a multilevel analysis of 4,858 children aged 6 to 9 suggests that as children age the SES effect on sport participation persists over time. However, the SES effect on sport participation appears to have relatively small predictive import compared to other factors.

A Note on Confidence Intervals in Cost-Effectiveness Analysis

1998 ◽  
Vol 14 (3) ◽  
pp. 467-471 ◽  
Author(s):  
Magnus Tambour ◽  
Niklas Zethraeus ◽  
Magnus Johannesson
Keyword(s):  
Cost Effectiveness ◽  
Confidence Interval ◽  
Confidence Intervals ◽  
Cost Effectiveness Analysis ◽  
Statistical Techniques ◽  
Ratio Estimation ◽  
Costs And Benefits ◽  
Effectiveness Analysis ◽  
Ratio Of Random Variables ◽  
Net Benefits

AbstractHow to obtain confidence intervals for cost-effectiveness ratios is complicated by the statistical problems of obtaining a confidence interval for a ratio of random variables. Different approaches have been suggested in the literature, but no consensus has been reached. We propose an alternative simple solution to this problem. By multiplying the effectiveness units by the price per effectiveness unit, both costs and benefits can be expressed in monetary terms and standard statistical techniques can be used to estimate a confidence interval for net benefits. This approach avoids the ratio estimation problem and explicitly recognizes that the price per effectiveness unit has to be known to provide cost-effectiveness analysis with a useful decision rule.

Modelling hierarchical systems by a continuous-time homogeneous Markov chain using two-wave panel data

1999 ◽  
Vol 36 (3) ◽  
pp. 644-653 ◽  
Author(s):  
Philippe Carette
Keyword(s):  
Markov Chain ◽  
Panel Data ◽  
Continuous Time ◽  
Transition Matrix ◽  
Infinitesimal Generator ◽  
Ordered Set ◽  
Homogeneous Markov Chain ◽  
Hierarchical Systems ◽  
Manpower System ◽  
Totally Ordered Set

An open hierarchical (manpower) system divided into a totally ordered set of k grades is discussed. The transitions occur only from one grade to the next or to an additional (k+1)th grade representing the external environment of the system. The model used to describe the dynamics of the system is a continuous-time homogeneous Markov chain with k+1 states and infinitesimal generator R = (rij) satisfying rij = 0 if i > j or i + 1 < j ≤ k (i, j = 1,…,k+1), the transition matrix P between times 0 and 1 being P = expR. In this paper, two-wave panel data about the hierarchical system are considered and the resulting fact that, in general, the maximum-likelihood estimated transition matrix cannot be written as an exponential of an infinitesimal generator R having the form described above. The purpose of this paper is to investigate when this can be ascribed to the effect of sampling variability.

Trends in Intergenerational Earnings Mobility in the U.S.

2003 ◽  
Author(s):  
Angela R. Fertig
Keyword(s):  
Transition Matrix ◽  
Panel Study ◽  
Ordinary Least Squares ◽  
Income Dynamics ◽  
Earnings Mobility ◽  
The Poor ◽  
Matrix Coefficients ◽  
The Rich ◽  
The Difference ◽  
The U.S

This paper examines the trend in intergenerational earnings mobility by estimating ordinary least squares, quantile regression, and transition matrix coefficients using five cohorts from the Panel Study of Income Dynamics. The results indicate that mobility in real earnings increased for sons with respect to fathers and remained constant for all other parent-child pairs. The findings from the father-son sample also suggest that the difference between the mobility levels of the rich and the poor narrowed over this period. These results suggest that a rise in equality of opportunity for sons accompanied the recent growth in inequality.

Statistical techniques and Doppler satellite positioning

1980 ◽  
Vol 294 (1410) ◽  
pp. 365-375
Keyword(s):  
Confidence Regions ◽  
Satellite Networks ◽  
Satellite Observations ◽  
Statistical Techniques ◽  
Satellite Positioning ◽  
Final Development ◽  
Station Coordinates ◽  
Bonferroni's Inequality ◽  
Observation Space ◽  
Detection Of Outliers

Two problems are addressed: ( a ) the detection of outliers in (Doppler satellite) observations, and ( b ) the testing of coordinates in (Doppler satellite) networks. In both problems, confidence regions of the ‘out of context’ and ‘within context’ varieties are developed, and it is shown that the latter are in general about 1 1/2 times larger than the former (conventional) confidence regions. On the basis of this comparison, it is speculated that good data and results are being erroneously rejected. Also it is demonstrated, through the use of Bonferroni’s inequality, that discarding covariances among residuals and discarding cross-covariances among station coordinates each results in a confidence level being greater than 1 — x, the conventionally chosen level. As a final development, a link is made not only between univariate and multivariate testing for outliers among observations but also between testing in observation space and testing in parameter space. The implications of these developments for Doppler satellite positioning are given.

scholarly journals Optimal bandwidth choice for robust bias-corrected inference in regression discontinuity designs

2019 ◽  
Vol 23 (2) ◽  
pp. 192-210 ◽  
Author(s):  
Sebastian Calonico ◽  
Matias D Cattaneo ◽  
Max H Farrell
Keyword(s):  
Confidence Intervals ◽  
Regression Discontinuity ◽  
Empirical Work ◽  
Monte Carlo Experiment ◽  
Optimal Bandwidth ◽  
Coverage Error ◽  
Point Estimator ◽  
Local Polynomial Estimation ◽  
Interval Estimators ◽  
Polynomial Estimation

Summary Modern empirical work in regression discontinuity (RD) designs often employs local polynomial estimation and inference with a mean square error (MSE) optimal bandwidth choice. This bandwidth yields an MSE-optimal RD treatment effect estimator, but is by construction invalid for inference. Robust bias-corrected (RBC) inference methods are valid when using the MSE-optimal bandwidth, but we show that they yield suboptimal confidence intervals in terms of coverage error. We establish valid coverage error expansions for RBC confidence interval estimators and use these results to propose new inference-optimal bandwidth choices for forming these intervals. We find that the standard MSE-optimal bandwidth for the RD point estimator is too large when the goal is to construct RBC confidence intervals with the smaller coverage error rate. We further optimize the constant terms behind the coverage error to derive new optimal choices for the auxiliary bandwidth required for RBC inference. Our expansions also establish that RBC inference yields higher-order refinements (relative to traditional undersmoothing) in the context of RD designs. Our main results cover sharp and sharp kink RD designs under conditional heteroskedasticity, and we discuss extensions to fuzzy and other RD designs, clustered sampling, and pre-intervention covariates adjustments. The theoretical findings are illustrated with a Monte Carlo experiment and an empirical application, and the main methodological results are available in R and Stata packages.

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