By A.H. Studenmund
Combining single-equation linear regression research with intuitive real-world examples and workouts is vital to the good fortune of utilizing Econometrics. transparent writing and a realistic method of econometrics that eschews using advanced matrix algebra and calculus proof this crucial text's accessibility. because the subtitle, a pragmatic consultant, implies, this booklet is aimed not just at starting econometrics scholars, but additionally at regression clients trying to find a refresher and at skilled practitioners who need a handy reference.
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Extra info for Using Econometrics: A Practical Guide (5th Edition)
Sample text
We now look at certain aspects of nonlinear processes which may indicate how we should construct useful nonlinear models. 4 Sensitive Dependence on Initial Conditions, Lyapunov Exponents The most striking feature of nonlinear processes is the strong dependence on initial conditions and the noise amplification. Let us start with deterministic difference equations, representing some dynamic system in discrete time. xn 1 / defines a deterministic difference equation, for some function f . f . x0 //// be the value of the system after n iterations.
1 Parametric Models for the Conditional Mean These models represent the conditional mean function of the process Xt as a nonlinear function of the past observations, keeping the conditional variance constant. d. sequence and  is an unknown parameter vector to be estimated. In some cases, the function f may also depend on other external processes. Several different forms of f give different classes of nonlinear models. One important subclass is the regime models or regime switching models. Models in this class are typically made up of several piecewise linear processes and the generating process switches from one linear model to another, depending on the value of an indicator.
Pij / for i; j D 1; 2. d. sequences. pj ; qj /. Hence the process will alternate among various linear models in accordance with the transient behavior of the unobserved process St . The estimation, identification and diagnostics for these models are complicated although not impossible, due to the fact that the process St is not observed. Note that if the residual process is made to depend on the unobserved Markov chain then the variance of the process also changes from one regime to another. In Fig.