estimating the parameters of the markov probability model from aggregate time series data

By: Contributor(s): Language: English Series: contributions to economic analysis ; 65 | johnston, j. (ed.) | sandee, j. (ed.) | strotz, r.h. (ed.) | et alPublication details: amsterdam, london : north-holland publishing company 1970Edition: 1. edDescription: 254 pp
Contents:
from the table of contents: introduction to the series; preface; list of symbols; introduction; the estimation of transition probabilities from micro data; the estimation of transition probabilities from macro data; the sampling experiment and some initial results; weighted inequality restricted least squares estimators; a generalized least squares estimator; the minimum chi-square estimator; the macro maximum likelihood estimator; bayesian analysis of the 'macro' model; the minimum absolute deviations estimator; prediction and the chi-square goodness-of-fit test; comparisons of the alternative estimators; concluding remarks; appendix a: the generalized inverse method; appendix b: the general linear probability model; appendix c: estimation of variable transition probabilities; appendix d: the fortran programming of classical and bayesian transition probability estimators; references; author index; subject index;
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Loanable Institute for Advanced Studies (IHS) Book 4257-A Available IHS101169905

from the table of contents: introduction to the series; preface; list of symbols; introduction; the estimation of transition probabilities from micro data; the estimation of transition probabilities from macro data; the sampling experiment and some initial results; weighted inequality restricted least squares estimators; a generalized least squares estimator; the minimum chi-square estimator; the macro maximum likelihood estimator; bayesian analysis of the 'macro' model; the minimum absolute deviations estimator; prediction and the chi-square goodness-of-fit test; comparisons of the alternative estimators; concluding remarks; appendix a: the generalized inverse method; appendix b: the general linear probability model; appendix c: estimation of variable transition probabilities; appendix d: the fortran programming of classical and bayesian transition probability estimators; references; author index; subject index;

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