By Ravi Bansal, A. Ronald Gallant, Robert Hussey, George Tauchen (auth.), D. A. Belsley (eds.)
It is not likely that any frontier of economics/econometrics is being driven swifter, additional than that of computational strategies. the pc has develop into a device for appearing in addition to an atmosphere during which to accomplish economics and econometrics, taking up the place conception bathrooms down, permitting not less than approximate solutions to questions that defy closed mathematical or analytical recommendations. projects might now be tried that have been hitherto past human strength, and all of the forces on hand can now be marshalled successfully, resulting in the fulfillment of wanted goals.
Computational ideas for Econometrics and financial Analysis is a set of contemporary stories which exemplify a lot of these components, demonstrating the ability that the pc brings to the commercial analysts. The publication is split into 4 elements: 1 -- the pc and econometric tools; 2 -- the pc and monetary research; three -- computational options for econometrics; and four -- the pc and econometric studies.
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Additional info for Computational Techniques for Econometrics and Economic Analysis
A BOOTSTRAP ESTIMATE The parameters of the model described in the previous section are estimated using a bootstrapping procedure and Bayes' Theorem. The prior density is an indicator function that is diffuse when the boundary conditions hold and 0 otherwise. The Bayesian bootstrap procedure permits valid inference on all of the parameters and response coefficients. The most convenient way to explain how the bootstrap procedure is applied here is to examine the four fundamental components of the model.
UUt UXlt+1 where '0' denotes the Kronecker product. The above expression is the orthogonality condition exploited when computing Generalized Method of Moments (GMM) estimates of the parameters of the Euler equations. In particular, for n observations, the GMM estimate is (Gallant, 1987) d = argmino S«(}, V) , where and L 00 m n «(}, x) = n- I et«(}) 0 Zt . t=1 To obtain a closed-form solution to recursive dynamic and stochastic optimization problems, one must compromise on the functional form. The Stochastic Optimal Linear Regulator specifies a quadratic objective function and linear constraints.
J? j=l This estimator has the advantage that each element in the objective function is expressed in the same units of measurement. It therefore produces a result that is independent of those units, and hence of the weighting of the moments implicit in the previous two estimators. (4) The Newey and West (1987) version of the GMM estimator introduces the weighting matrix WT = Vrl, where T VT = I: g(Xt,[3*)g'(xt,[3*)jT, t=l and where [3* is an initial estimator of [3 obtained from an unweighted method of simulated moments estimator l .