Applied Econometrics With R

Again it assumed that the reader will have some basic knowledge, i.e., basic knowledge of linear regression, but this moves quickly into more complicated forms, e.g., semiparametric models (called partially linear models), systems of regression equations using the ? Three different approaches are co...

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Bibliographic Details
Published inTechnometrics Vol. 51; no. 4; pp. 484 - 485
Main Author Myers, Donald E.
Format Book Review
LanguageEnglish
Published Alexandria The American Society for Quality and The American Statistical Association 01.11.2009
American Society for Quality
Subjects
Confidence intervals
Econometrics
Generalized linear models
Nonfiction
Open source software
Regression analysis
Software
Statistical methods
Time series
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Summary:Again it assumed that the reader will have some basic knowledge, i.e., basic knowledge of linear regression, but this moves quickly into more complicated forms, e.g., semiparametric models (called partially linear models), systems of regression equations using the ? Three different approaches are considered; regression diagnostics, i.e., jackknifing the data and comparing various statistics obtained from the full dataset versus those obtained by systematically omitting one data point, the second is diagnostic tests with alternative hypotheses such as heteroscedasticity, autocorrelation and misspecification of the functional form, the third is "robust" covariance matrix estimators. The authors note that many econometric packages also incorporate these, but require separate software packages as contrasted with R which subsumes them under the heading of generalized linear models.
ISSN:0040-1706
1537-2723