By Badi H. Baltagi
A significant other to Theoretical Econometrics offers a complete connection with the fundamentals of econometrics. This spouse makes a speciality of the principles of the sphere and while integrates well known issues frequently encountered via practitioners. The chapters are written through foreign specialists and supply up to date examine in components now not often coated via typical econometric texts. makes a speciality of the principles of econometrics. Integrates real-world subject matters encountered via pros and practitioners. attracts on up to date study in components now not lined by means of normal econometrics texts. prepared to supply transparent, available details and element to additional readings.
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Additional info for A Companion to Theoretical Econometrics (Blackwell Companions to Contemporary Economics)
Example text
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. Modelling seasonality has progressed from the traditional view that seasonal patterns are a nuisance which need to be removed to the current view, see Ghysels (1994), that they are an informative feature of economic time series that should be modeled explicitly.
Applied econometricians have been worrying about spurious regressions only with nonstationary series. Usually, testing for unit roots before entering a regression. Granger warns that one should also be worried about this problem with stationary series and recommends care in the proper specification of one’s model using lags of all variables. Chapter 27 by Stock provides an introduction to the main methods used for forecasting economic time series. Throughout the chapter, Stock emphasizes the responsible production and interpretation of economic forecasts which have entered into many aspects of economic life.
The regressand and regressors in (1) can be evaluated at any point θ ʦ Θ, and the properties of the artificial regression will depend on the point at which they are evaluated. In many cases, we will want to evaluate (1) at a vector of estimates Q that is root-n consistent. This means that, if the true parameter vector is θ0 ʦ Θ, then Q approaches θ0 at a rate proportional to n−1/2. One such vector that is of particular interest is P, the vector of estimates which minimizes the criterion function Q(θ).