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Distribution-free techniques of statistical inference are developed for the cumulative coefficients of variation of an income distribution, thus allowing one to test for inequality dominance when Lorenz curves cross. The full covariance structure of the cumulative sample means and variances is...
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In this paper, a new method is forwarded of estimating the effect of short-run macroeconomic fluctuations on concentration in the size distribution of personal income. In particular, the impacts of changes in unemployment and participation rates and in the level of wage and salary income upon...
Persistent link: https://www.econbiz.de/10005653039
Simple techniques for the graphical display of simulation evidence concerning the size and power of hypothesis tests are developed and illustrated. Three types of figures - called P value plots, P value discrepancy plots, and size-power curves - are discussed. Some Monte Carlo experiments on the...
Persistent link: https://www.econbiz.de/10005490218
We consider several issues related to what Hausman (1978) called "specification tests", namely tests designed to verify the consistency of parameter estimates. We first review a number of results about these tests in linear regression models, and present some new material on their distribution...
Persistent link: https://www.econbiz.de/10005653049
Methods based on linear regression provide a very easy way to use the information in control and antithetic variates to improve the efficiency with which certain features of the distributions of estimators and test statistics are estimated in Monte Carlo experiments. We propose a new technique...
Persistent link: https://www.econbiz.de/10005653213
We develop simple procedures to test for omitted variables and perform other tests in regression directions, which are asymptotically valid in the presence of heteroskedasticity of unknown form. We examine the asymptotic behaviour of these tests, and use Edgeworth approximations to study their...
Persistent link: https://www.econbiz.de/10005653228
Associated with every popular nonlinear estimation method is at least one "artificial" linear regression. We define an artificial regression in terms of three conditions that it must satisfy. Then we show how artificial regressions can be useful for numerical optimization, testing hypotheses,...
Persistent link: https://www.econbiz.de/10005653239