A Companion to Theoretical Econometrics by Badi H. Baltagi

By Badi H. Baltagi

A better half to Theoretical Econometrics offers a complete connection with the fundamentals of econometrics. This better half specializes in the rules of the sector and even as integrates well known issues usually encountered via practitioners. The chapters are written by way of overseas specialists and supply updated study in parts no longer frequently lined through ordinary econometric texts.

  • Focuses at the foundations of econometrics.
  • Integrates real-world issues encountered by way of pros and practitioners.
  • Draws on updated learn in parts now not lined by means of commonplace econometrics texts.
  • Organized to supply transparent, available info and aspect to additional readings.

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Extra resources for A Companion to Theoretical Econometrics

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Therefore, naive application of classical inference may give misleading results. Bierens considers in details the Gaussian AR(1) case without an intercept; the Gaussian AR(1) case with an intercept under the alternative of stationarity; the general AR(p) process with a unit root and the Augmented Dickey–Fuller test; the general ARIMA processes and the Phillips–Perron test; and the unit root with drift versus trend stationarity. Chapter 30 by Dolado, Gonzalo, and Marmol discusses how the important concept of cointegration in econometrics bridged the gap between economic theorists who had much to say about equilibrium but relatively little to say about dynamics and the econometricians whose models concentrated on the short-run dynamics disregarding the long-run equilibrium.

We will refer to this regression as the heteroskedasticity-robust Gauss–Newton regression, or HRGNR. 30 R. G. MACKINNON In order to derive the HRGNR, it is convenient to begin with a linear regression model y = Xβ + u, and to consider the criterion function Q(β) = 1 2 (y − Xβ)‫ׅ‬X(X‫ׅ‬ΩX)−1X‫(ׅ‬y − Xβ). 39) of which the inverse is the HCCME if we replace Ω by V. Equating the gradient to zero just yields the OLS estimator, since X‫ׅ‬X and X‫ׅ‬ΩX are k × k nonsingular matrices. Let V be an n × n diagonal matrix with tth diagonal element equal to ω t ; thus V2 = Ω.

Next, the analysis is extended to the multivariate setting where the estimation and testing of more general system based approaches to cointegration are considered. ” The latter is based on the dual relationship between the number of cointegrating vectors and the number of common trends in the system. Several extensions of this literature are discussed including (i) higher order cointegrated systems; (ii) fractionally cointegrated systems; (iii) nearly cointegrated systems; (iv) nonlinear error correction models; and (v) structural breaks in cointegrated systems.

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