By Peter Kennedy
This can be the precise (and crucial) complement for all econometrics classes--from a rigorous first undergraduate path, to a primary master's, to a PhD course.
* Explains what's going in textbooks choked with proofs and formulation
* bargains instinct, skepticism, insights, humor, and useful recommendation (dos and don’ts)
* comprises new chapters that hide instrumental variables and computational concerns
* comprises additional info on GMM, nonparametrics, and an advent to wavelets
Read or Download A Guide to Econometrics (4th Edition) PDF
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Extra info for A Guide to Econometrics (4th Edition)
We now brieﬂy review the basic theory of the convergence of Markov Chains. A Markov chain is generally characterized by its g-step transition probability, P (g ) (x, A) = Prob (g ) ∈ A| (0) =x . For a chain to have a unique equilibrium or stationary distribution, π, it must be irreducible and aperiodic. A Markov chain with invariant distribution π is irreducible if, for any initial state,it has positive probability of eventually entering any set that has π-positive probability. A chain is aperiodic if there are no portions of the state space that the chain visits at regularly spaced time intervals.
Shocks whose distributions are easy to characterize. When discretized at any ﬁnite time-interval, the models take the form of familiar time series models with normal, discrete mixtures of normals or scale mixtures of normals error distributions. This implies that the standard tools of Bayesian inference directly apply to these models. We will also later discuss the accuracy of discrete-time variants of continuous-time models. 2. MCMC is a uniﬁed estimation procedure, simultaneously estimating both parameters and latent variables.
Moreover, the distribution of the parameters given observed data and state variables, p( |X , Y ), is typically easy to simulate as it conditions on the latent states. MCMC algorithms generically consist of two different steps. If the complete conditional distribution is known in closed form and can be directly sampled, the step in the MCMC algorithm is known as a “Gibbs” step. ” In many situations, one or more of the conditionals cannot be directly sampled and methods known as “Metropolis-Hastings algorithms” apply.