By Badi H. Baltagi

ISBN-10: 047099830X

ISBN-13: 9780470998304

ISBN-10: 063121254X

ISBN-13: 9780631212546

ISBN-10: 140510676X

ISBN-13: 9781405106764

ISBN-10: 1405142073

ISBN-13: 9781405142076

A better half to Theoretical Econometrics offers a finished connection with the fundamentals of econometrics. This better half makes a speciality of the rules of the sector and whilst integrates well known themes usually encountered by way of practitioners. The chapters are written by means of overseas specialists and supply updated learn in parts now not often coated by means of usual econometric texts. makes a speciality of the principles of econometrics. Integrates real-world subject matters encountered through execs and practitioners. attracts on up to date study in components no longer coated by means of normal econometrics texts. equipped to supply transparent, available details and element to additional readings.

**Read Online or Download A Companion to Theoretical Econometrics (Blackwell Companions to Contemporary Economics) PDF**

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**Additional resources for A Companion to Theoretical Econometrics (Blackwell Companions to Contemporary Economics)**

**Example text**

In the special case in which the original model is linear, x(β) = Xβ, where X is the matrix of independent variables. Since X(β) is equal to X for all β in this special case, the GNR will simply be a regression of the vector y − Xβ on the matrix X. 8) R. G. MACKINNON where Zt1 and Zt2 are independent variables. The regression function here is nonlinear and has the form of a Cobb–Douglas production function. In many cases, of course, it would be reasonable to assume that the error term is multiplicative, and it would then be possible to take logarithms of both sides and use ordinary least squares.

This tradeoff is between model approximation error and model estimation error. This leads naturally to considering information criteria that select among competing models. Stock also discusses prediction intervals and forecast comparison methods. Next, this chapter provides a glimpse at some of the relevant empirical features of five monthly macroeconomic time series data for the USA. These include the rate of inflation, output growth, the unemployment rate, a short-term interest rate and total real manufacturing and trade inventories.

It can also be shown that it satisfies conditions (2) and (3), and thus it has all the properties of an artificial regression. 48) (Davidson and MacKinnon, 1985a). The latter application has recently been extended to models with AR(1) errors by Baltagi (1999). An accessible discussion of the DLR may be R. G. MACKINNON found in Davidson and MacKinnon (1988). When both the OPG regression and the DLR are available, the finite-sample performance of the latter always seems to be very much better than that of the former.

### A Companion to Theoretical Econometrics (Blackwell Companions to Contemporary Economics) by Badi H. Baltagi

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