By Cheng Hsiao

This ebook presents a complete, coherent, and intuitive overview of panel info methodologies which are worthwhile for empirical research. considerably revised from the second one variation, it comprises new chapters on modeling cross-sectionally established facts and dynamic structures of equations. many of the extra advanced techniques were additional streamlined. different new fabric comprises correlated random coefficient versions, pseudo-panels, length and count number info types, quantile research, and substitute techniques for controlling the impression of unobserved heterogeneity in nonlinear panel facts versions.

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**Extra resources for Analysis of Panel Data**

**Example text**

5. 2 we discuss estimation methods when the specific effects are treated as fixed constants (FE). 3 discusses estimation methods when they are treated as random variables (effects) (RE). 4 discusses the pros and cons of treating the specific effects as fixed or random. 5. 6 discusses models with both individual- and time-specific effects and models with specific variables. 2. 33 a The mean of firm effects, αi , is zero is invoked. Source: Hoch (1962). heteroscedasticity and autocorrelation adjustment.

N, t = 1, . . , T , k = 1, . . , K. Conventionally, observations of y are assumed to be the random outcomes of some experiment with a probability distribution conditional on vectors of the characteristics x and a fixed number of parameters , f (y | x, ). When panel data are used, one of the ultimate goals is to use all available information to make inferences on . For instance, a simple model commonly postulated is that y is a linear function of x. Yet to run a least-squares regression with all N T observations, we need to assume that the regression parameters take value common to all cross-sectional units for all time periods.

4) N where y t = (1/N) N and ut = i=1 yit , x t = (1/N) i=1 x it N (1/N) i=1 uit . 4) now provides unbiased and consistent estimates of . Nevertheless, if we have only a single cross-sectional data set (T = 1) for the former case (zit = zi ), or a single time series data set (N = 1) for the latter case (zit = zt ), such transformations cannot be performed. We cannot get consistent estimates of  unless there exist instruments that correlate with x but do not correlate with z and u. MaCurdy’s (1981) work on the life cycle labor supply of prime age males under certainty is an example of this approach.