Estimating continuous-time income models
AbstractWhile earning processes are commonly unobservable income flows which evolve in continuous time, observable income data are usually discrete, having been aggregated over time. We consider continuous-time earning processes, specifically (non-linearly) transformed Ornstein-Uhlenbeck processes, and the associated integrated, i.e. time aggregated process. Both processes are characterised, and we show that time aggregation alters important statistical properties. The parameters of the earning process are estimable by GMM, and the finite sample properties of the estimator are investigated. Our methods are applied to annual earnings data for the US. It is demonstrated that the model replicates well important features of the earnings distribution. Keywords; integrated non-linearly transformed ornstein-uhlenbeck process, temporal aggregation
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Bibliographic InfoPaper provided by Economics Division, School of Social Sciences, University of Southampton in its series Discussion Paper Series In Economics And Econometrics with number 1014.
Date of creation: 01 Jul 2010
Date of revision:
Other versions of this item:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
- E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution
- J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
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