By Badi H. Baltagi

ISBN-10: 063121254X

ISBN-13: 9780631212546

A significant other to Theoretical Econometrics presents a finished connection with the fundamentals of econometrics. This spouse makes a speciality of the rules of the sphere and whilst integrates renowned subject matters frequently encountered through practitioners. The chapters are written via foreign specialists and supply updated learn in components now not often lined through usual econometric texts.

- Focuses at the foundations of econometrics.
- Integrates real-world themes encountered by means of pros and practitioners.
- Draws on up to date learn in components now not lined by means of normal econometrics texts.
- Organized to supply transparent, obtainable details and aspect to additional readings.

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**Additional info for A Companion to Theoretical Econometrics **

**Example text**

3 THE GAUSS–NEWTON REGRESSION Associated with every nonlinear regression model is a somewhat nonstandard artificial regression which is probably more widely used than any other. Consider the univariate, nonlinear regression model yt = xt (β) + ut , ut ~ iid(0, σ 2 ), t = 1, . . 2) where yt is the tth observation on the dependent variable, and β is a k-vector of parameters to be estimated. The scalar function xt(β) is a nonlinear regression function. It determines the mean value of yt as a function of unknown parameters β and, usually, of explanatory variables, which may include lagged dependent variables.

1) is an artificial regression if it satisfies the following three conditions: 1. 2. The estimator P is defined, uniquely in a neighborhood in Θ, by the k equations R(ׅP)r(P) = 0; for any root-n consistent Q, a consistent estimate of var(plim n1/2(P − θ 0)) is given by the inverse of n−1R(ׅQ)R(Q). Formally, ⎛ ⎞ var ⎜ plim n1/2 (P − θ0 )⎟ = plim (n−1R(ׅQ)R (Q))−1 ; ⎝ n→∞ ⎠ n →∞ 3. 1) with regressand and regressors evaluated at Q, then Q + c = P + op(n−1/2). 1′) R. G. MACKINNON where g(θ) denotes the gradient of the criterion function Q(θ).

Thomas, J. (1993). On testing the logistic assumption in binary dependent variable models. Empirical Economics 18, 381–92. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817–38. M. (1990). A unified approach to robust, regression-based specification tests. Econometric Theory 6, 17–43. M. (1991). On the application of robust, regression-based diagnostics to models of conditional means and conditional variances.

### A Companion to Theoretical Econometrics by Badi H. Baltagi

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