Techniques

Computational Techniques for Econometrics and Economic by Ravi Bansal, A. Ronald Gallant, Robert Hussey, George

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, extra than that of computational suggestions. the pc has turn into a device for acting in addition to an atmosphere within which to accomplish economics and econometrics, taking on the place conception toilets down, permitting no less than approximate solutions to questions that defy closed mathematical or analytical recommendations. projects may well 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 concepts for Econometrics and monetary Analysis is a set of contemporary reports which exemplify a lot of these parts, demonstrating the facility that the pc brings to the commercial analysts. The ebook is split into 4 components: 1 -- the pc and econometric equipment; 2 -- the pc and monetary research; three -- computational thoughts for econometrics; and four -- the pc and econometric studies.

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Extra resources for Computational Techniques for Econometrics and Economic Analysis

Sample text

We demonstrate the feasibility of the technique by computing and interpreting the estimates of a dynamic food price margin model using secondary economic time series data. ABSTRACf. 1. INTRODUCfION Several solutions to infinite time-horizon, multivariate stochastic and dynamic programming problems have recently been proposed (Baxter et a1. 1990; Christiano, 1990; Coleman, 1990; den Haan and Marcet, 1990; Gagnon, 1990; Labadie, 1990; McGratten, 1990; Tauchen, 1990; Taylor and Uhlig, 1990). However, few studies suggest ways to make correct inferences on parameter estimates in such problems.

But, just as these, Table 6 shows how easily numerically "significant" biases in the parameter estimates can offset one another to give apparently unbiased mean and variance estimates. Both are estimated with much smaller numerical biases than are p and q themselves. There is no clear tendency here for the variance to be more biased than the mean, and both biases show a stronger tendency to diminish with increasing T. Nor is there any apparent ranking of biases across estimators. Yet the general message is the same: it matters for estimation whether one focuses on particular characteristics of the distribution or its entirety.

Represents the fitted moments given Xt and the choice of {J. In many cases we do not have an analytic maintained hypothesis, so the fitted moments j(-) have to be constructed by numerical simulation with pseudo-data replicated many times through the model to generate numerical evaluations of those moments. That variant is the method of simulated moments. Now, if (1) is correct the sample moment, T gT({3) =L g(Xt, {3) / T t=1 should be close to zero when evaluated at {3 estimate {3 by choosing to minimise /3 = /3.

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