Analysis of Microdata by Rainer Winkelmann

By Rainer Winkelmann

The ebook offers an easy, intuitive advent to regression versions for qualitative and discrete based variables, to pattern choice types, and to occasion background versions, all within the context of extreme probability estimation. It offers quite a lot of conventional versions. The e-book thereby allows the reader to develop into a severe customer of present empirical social technology examine and to behavior personal empirical analyses. The booklet contains quite a few examples, illustrations, and routines. it may be used as a textbook for a sophisticated undergraduate, a Master`s or a first-year Ph.D. direction in microdata research, and as a reference for practitioners and researchers.

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Xn ) . 3) i=1 Seen as a function of the unknown parameter vector θ, this is a likelihood function. 4) i=1 where c > 0 is a proportionality constant. 4). Moreover, under some regularity assumptions, the maximum likelihood estimator exists, is unique, consistent and asymptotically efficient. In Chapter 3, we discuss in detail the concept of maximum likelihood estimation. 3 Interpretation In a CEF model with E(yi |xi ) = µ(xi ; β), we are interested in how the conditional expectation changes, as a function of the parameters, if an explanatory variable increases.

Yn ) . L(θ; yi ) is the likelihood contribution of the i-th observation, and L(θ; y) = L(θ; y1 , . . , yn ) is the likelihood function of the whole sample. 2) says that, for any given sample y, the likelihood of having obtained the actual sample that we are using depends on the parameter θ. As the name suggests, the basic idea of maximum likelihood estimation is ˆ such that this likelihood is maxito find a set of parameter estimates, say θ, mized. This principle is widely applicable. Whenever we can write down the joint probability function of the sample we can in principle use maximum likelihood estimation.

In microdata applications, the fundamental experiment is a draw from the underlying population in order to measure the outcome of a variable. For example, the random draw could be from the set of all young men, in order to determine whether the selected male attends university or not. In this case, the sample space given by Ω = {not attending university, attending university}. In this simple example, there are only four events (subsets of Ω): the empty set, Ω itself, “the person attends university”, and “the person does not attend university”.

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