By Paul A. Ruud

This is often one other stable, glossy textbook on parametric, cross-sectional econometrics (don't search for non/semi-parametric or time-series econometrics in here). it really is, i feel, within the comparable league as Wooldridge, that's despite the fact that much less technical and spends extra time describing empirical purposes. i believe Ruud is a truly great addition to an econometric shelf. The notation is sweet, and the math/stat appendix is likely one of the top i've got ever visible (the part on multivariate differentiation particularly is phenomenal and extremely useful). total, to be able to have three *relatively* uncomplicated books on parametric cross-section econometrics, i feel it is a reliable significant other to Wooldridge and Cameron and Trivedi (a great compendium of utilized instruments, which additionally contains a few non-parametrics, for which the simplest creation is probably going Pagan and Ullah). If time-series is necessary to you, Hayashi is an effective selection. As you've got guessed, i'm really not an enormous fan of Greene, which I do personal yet by no means examine.

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2 Estimation In this section we brieﬂy discuss parameter estimation in the standard Linear Regression model. We ﬁrst discuss the Ordinary Least Squares (OLS) method, and then we discuss the Maximum Likelihood (ML) method. 1). The ML method will also be used in later chapters as it is particularly useful for nonlinear models. For the standard Linear Regression model it turns out that the OLS and ML methods give the same results. As indicated earlier, the reader who is interested in this and the next section is assumed to have some prior econometric knowledge.

This chapter serves to review a few issues which should be useful for later chapters. 1 we discuss the representation of the standard Linear Regression model. 2 we discuss Ordinary Least Squares and Maximum Likelihood estimation in substantial detail. Even though the Maximum Likelihood method is not illustrated in detail, its basic aspects will be outlined as we need it in later chapters. 3, diagnostic measures for outliers, residual autocorrelation and heteroskedasticity are considered. Model selection concerns the selection of relevant variables and the comparison of non-nested models using certain model selection criteria.

This test concerns the extent to which the restrictions are satisﬁed by the unrestricted estimator ^ itself, comparing it with its conﬁdence region. Under the null hypothesis one has r ¼ 0, where the r is a g Â ðK þ 1Þ to indicate g speciﬁc parameter restrictions. The Wald test is now computed as W ¼ ðr^ À 0Þ0 ½rI^ ð^ÞÀ1 r 0 À1 ðr^ À 0Þ; ð3:48Þ and it is asymptotically distributed as ðgÞ. Note that the Wald test requires the computation only of the unrestricted ML estimator, and not the one under the null hypothesis.