Read e-book online Analysis of Panel Data (Econometric Society Monographs) PDF

By Cheng Hsiao


ISBN-13: 1397805110614


ISBN-13: 1397805215227

ISBN-10: 0521335612

ISBN-13: 9780521335614

ISBN-10: 0521338255

ISBN-13: 9780521338257

ISBN-10: 0521818559

ISBN-13: 9780521818551

This publication covers panel info econometrics thought rather well. it is most likely the simplest during this box. even if, this ebook lacks information and extra importantly laptop codes on the way to enforce panel facts versions. the writer did not offer an internet site the place you may get the knowledge within the ebook, which makes it tough for utilized econometricians to profit and enforce. it should much more beneficial if the writer grants a few help on tips on how to enforce his versions in both applications (e.g., Stata or SAS) or programming languages (e.g., Gauss or Matlab). i discovered biometricians and social scientists do an improved task in either examine and implementation of panel information types than econometricians, even supposing they've got assorted names for a similar factor (e.g., combined results, multilevel, or HLM, etc.)

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Extra info for Analysis of Panel Data (Econometric Society Monographs)

Example text

Xi T ) and can be characterized by an independently identically distributed random variable with mean zero and variance σu2 . 1) is also called the analysis-of-covariance model. Without attempting to make the boundaries between regression analysis, analysis of variance, and analysis of covariance precise, we can say that regression model assumes that the expected value of y is a function of exogenous factors x, while the conventional analysis-of-variance model stipulates that the expected value of yit depends only on the class i to which the observation considered belongs and that the value of the measured quantity, y, satisfies the relation yit = αi∗ + u it , where the other characteristics, u it , are random and are in no way dependent on the class this individual belongs.

The properties of various estimators we have discussed thus far depend on the existence and extent of the relations between the X ’s and the effects. Therefore, we have to consider the joint distribution of these variables. However, αi are unobservable. Mundlak (1978a) suggested that we approximate E(αi | X i ) by a linear function. He introduced the auxiliary regression αi = xit at + ωi , t ωi ∼ N 0, σω2 . 3a) is to let αi = x¯ i a + ωi , ωi ∼ N 0, σω2 . 3b) Clearly, a will be equal to zero (and σω2 = σα2 ) if (and only if) the explanatory variables are uncorrelated with the effects.

5). 2) is called the analysis-ofcovariance model, the LSDV estimator of ␤ is sometimes called the covariance estimator. 4 The covariance estimator βˆ CV is unbiased. It is also consistent when either N or T or both tend to infinity. Its variance–covariance matrix is −1 N Var(␤CV ) = σu2 . 4), although unbiased, is consistent only when T → ∞. 1) is to introduce a “mean intercept,” µ, so that yit = µ + ␤ xit + αi + u it . 10) Because both µ and αi are fixed constants, without additional restriction, they are not separately identifiable or estimable.

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Analysis of Panel Data (Econometric Society Monographs) by Cheng Hsiao

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