By Sbihi A.
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Additional resources for A best first search exact algorithm for the Multiple-choice Multidimensional Knapsack Problem
31) where φ2 is the ratio of the within to the between error variances φ2 = σ2/σ2, , and wi = T−1Wiι. Thus can be regarded as a matrix-weighted average of the within-group and between-group estimators (cf. Maddala, 1971). 17 For feasible GLS, φ2 is replaced by the ratio of the within to the between sample residual variances . For maximum likelihood, φ2 corresponds to the ratio of residual variances evaluated at the 18 MLE itself. 3 Testing for Correlated Unobserved Heterogeneity Sometimes correlated unobserved heterogeneity is a basic property of the model of interest.
Empirical work on earnings mobility often assumed a normal distribution for the components of log earnings errors, but the distributions can also be nonparametrically estimated using deconvolution techniques. 17): together with the assumption that vit is iid and independent of ηi, and both are continuous random variables. Let hu(τ), hΔ(τ), hη(τ), and hv(τ) denote the characteristic functions (cf) of u, Δ u, η, and v, respectively. 57) where (when not used as a subscript for individuals) and |ϑ| denotes the modulus of the complex variable ϑ.
In either case, the worry is that the variable to which agents respond does not coincide with the one that is entered as a regressor in the model. 5) Clearly, the observed regressor xi will be correlated with ui even if the latent variable is not. 6) This problem is often of practical signiﬁcance, specially in regression analysis with micro data, since the resulting biases may be large. Note that the magnitude of the bias does not depend on the absolute magnitude of the measurement error variance but on the “noise to signal” ratio λ.
A best first search exact algorithm for the Multiple-choice Multidimensional Knapsack Problem by Sbihi A.