Probabilistic forecasting using stochastic diffusion models, with applications to cohort processes of marriage and fertility
AbstractWe study prediction and error propagation in Hernes, Gompertz, and logistic models for innovation diffusion. We develop a unifying framework in which the models are linearized with respect to cohort age and predictions are derived from the underlying linear process. We develop and compare methods for deriving the predictions and show how Monte Carlo simulation can be used to estimate prediction uncertainty for a wide class of underlying linear processes. For an important special case, random walk with, we develop an analytic prediction variance estimator. Both the Monte Carlo method and the analytic variance estimator allow the forecasters to make precise the level of within-model prediction uncertainty in innovation diffusion models. Empirical applications to first births, first marriages and cumulative fertility illustrate the usefulness of these methods.
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Bibliographic InfoPaper provided by Max Planck Institute for Demographic Research, Rostock, Germany in its series MPIDR Working Papers with number WP-2010-013.
Length: 37 pages
Date of creation: Feb 2010
Date of revision:
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Web page: http://www.demogr.mpg.de/
Find related papers by JEL classification:
- J1 - Labor and Demographic Economics - - Demographic Economics
- Z0 - Other Special Topics - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-03-13 (All new papers)
- NEP-ECM-2010-03-13 (Econometrics)
- NEP-FOR-2010-03-13 (Forecasting)
- NEP-INO-2010-03-13 (Innovation)
- NEP-MIC-2010-03-13 (Microeconomics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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