By T. Fomby & R. Carter Hill (Editors)
This quantity is the results of an Advances in Econometrics convention held in November of 2002 at Louisiana country collage in acceptance of Halbert White's pioneering paintings released in Econometrica in 1980 and 1982 on strong variance-covariance estimation and quasi-maximum probability estimation. It comprises eleven papers on a variety of similar subject matters together with the estimation of probably misspecified errors part and glued results panel versions, estimation and inference in in all likelihood misspecified quantile regression versions, quasi-maximum chance estimation of linear regression types with bounded and symmetric mistakes and quasi-maximum probability estimation of types with parameter dependencies among the suggest vector and mistake variance-covariance matrix. different subject matters contain GMM, HAC, Heckit, uneven GARCH, Cross-Entropy, and multivariate deterministic development estimation and trying out less than quite a few attainable misspecifications.
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Extra resources for Maximum Likelihood Estimation of Misspecified Models: Twenty Years Later
When ut has a unit root, J has a well-defined asymptotic distribution while when ut is covariance stationary, J converges to zero. Bunzel and Vogelsang (2003) analyzed t-tests for ␤ using OLS estimates from model (1) and recommended the following test. Let ␤ˆ denote the OLS estimate Tests of Common Deterministic Trend Slopes Applied 33 from model (1). 02T, 2). ˆ 2 is the Daniell kernel non-parametric zero frequency spectral density estimator. Define the t-test t DAN = ␤ˆ − ␤ exp(−bJ), ˆ se(␤) ˆ = ˆ 2 ( (t − ¯t )2 )−1 with ¯t = T −1 T t and b is a pre-specified where se(␤) t=1 constant (see Bunzel & Vogelsang, 2003) that depends on the significance level of the test.
3. Robustness to Non-Normality So far, we have been assuming that the error components have been generated by the normal distribution. In this section, we check the sensitivity of our results to non-normal disturbances. In particular, we generate the i ’s and t ’s from 2 distributions and we let the remainder disturbances follow the normal distribution. In all experiments, we fix the total variance to be 20. Table 7 gives the choice of the pretest estimator under non-normality of the random effects for N = 25 and T = 10, 20 using the 5 and 10% significance levels, respectively.
For each hemisphere and, separately, for the globe, are there pairs of seasons that have equal trends? We refer to these hypotheses as “Intra-seasonal” hypotheses because they relate to the comparison of trends of seasons within separate hemispheres or the globe itself (ii) Are the trends for the four seasons the same across hemispheres? Across the hemispheres do the winters have the same trend? the springs? the summers? the autumns? We refer to these hypotheses as “Inter-seasonal” hypotheses because they relate to the comparison of trends of seasons across hemispheres.