Econometric Analysis. SOLUTIONS by Lord Greene

By Lord Greene

Show description

Read Online or Download Econometric Analysis. SOLUTIONS PDF

Best econometrics books

Stochastic Limit Theory: An Introduction for Econometricicans (Advanced Texts in Econometrics)

This significant new econometrics textual content surveys fresh advancements within the speedily increasing box of asymptotic distribution conception, with a unique emphasis at the difficulties of time dependence and heterogeneity. Designed for econometricians and complicated scholars with constrained mathematical education, the ebook sincerely lays out the required math and likelihood thought and makes use of quite a few examples to make its info beneficial and understandable.

Forecasting Non-Stationary Economic Time Series

Economies evolve and are topic to unexpected shifts induced by means of legislative adjustments, financial coverage, significant discoveries, and political turmoil. Macroeconometric versions are a truly imperfect device for forecasting this hugely complex and altering technique. Ignoring those components ends up in a large discrepancy among idea and perform.

Economics of Insurance

The idea of coverage is gifted during this publication, mentioned from the perspective of the idea of economics of uncertainty. the primary of top rate calculation which the publication makes use of is predicated on financial equilibrium concept and differs from the various top rate structures mentioned by way of actuaries. Reinsurance is constructed within the framework of normal fiscal equilibrium thought lower than uncertainty.


This is often an excerpt from the 4-volume dictionary of economics, a reference publication which goals to outline the topic of economics this present day. 1300 topic entries within the whole paintings conceal the large subject matters of financial concept. This extract concentrates on econometrics.

Additional resources for Econometric Analysis. SOLUTIONS

Sample text

Under the null hypothesis of equal variances, the ratio F = [e1′e1/(n1 - 2)]/[e2′e2/(n2 - 2)] (or vice versa for the subscripts) is the ratio of two independent chi-squared variables each divided by their respective degrees of freedom. Although it might seem so from the discussion in the text (and the literature) there is nothing in the test which requires that the coefficient vectors be assumed equal across groups. 8 to the F table. 61, so the hypothesis of equal variances is rejected. ∧ The two step estimator is β = [(1/s12)X1′X1 + (1/s22)X2′X2]-1[(1/s12)X1′y1 + (1/s22)X2′y2].

60, respectively. 99, so the second and third are statistically significant while the first is not. The disturbance variance underlying these data is, in fact, σi2 = σ2(1 + γXi22) so the Goldfeld-Quandt and Glejser tests have given the right diagnosis. For the Glejser test, the finding that the linear model is inappropriate makes sense since X2 takes negative values. 44 6. Using the data of Exercise 5, reestimate the parameters, using a two step feasible GLS estimator. Try (12-19), (12-20), and (12-21).

Consider the simple errors in variables model, y = α + βx* + ε, x = x* + u, where u and ε are uncorrelated, and x is the erroneously measured, observed counterpart to x*. (a) Assume that x*, u, and ε are all normally distributed with means µ*, 0, and 0, variances σ*2, σu2, 2 and σε and zero covariances. Obtain the probability limits of the least squares estimates of α and β. (b) As an alternative, consider regressing x on a constant and y, then computing the reciprocal of the estimate. Obtain the probability limit of this estimate.

Download PDF sample

Rated 4.42 of 5 – based on 30 votes