scholarly journals The Quasi Maximum Likelihood Approach to Statistical Inference on a Nonstationary Multivariate ARFIMA Process

2013 ◽  
Vol 2 (1) ◽  
Author(s):  
Amadou KAMAGATE ◽  
Ouagnina HILI
Keyword(s):  
Maximum Likelihood ◽  
Statistical Inference ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach ◽  
Quasi Maximum Likelihood

Estimating a model of sportfishing trip expenditures using a quasi-maximum likelihood approach

2016 ◽  
Vol 23 (2) ◽  
pp. 448-459 ◽  
Author(s):  
Richard T. Melstrom
Keyword(s):  
Maximum Likelihood ◽  
Exponential Model ◽  
Parameter Estimates ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach ◽  
Quasi Maximum Likelihood ◽  
Species Preferences ◽  
The Impact ◽  
Consistent Parameter

This article presents an exponential model of tourist expenditures estimated by a quasi-maximum likelihood (QML) technique. The advantage of this approach is that, unlike conventional OLS and Tobit estimators, it produces consistent parameter estimates under conditions of a corner solution at zero and heteroscedasticity. An application to sportfishing evaluates the role of socioeconomic demographics and species preferences on trip spending. The bias from an inappropriate estimator is illustrated by comparing the results from QML and OLS estimation, which shows that OLS significantly overstates the impact of trip duration on trip expenditures compared with the QML estimator. Both sets of estimates imply that trout and bass anglers spend significantly more on their fishing trips compared with other anglers.

scholarly journals Standard-error Correction in Two-stage Optimization Models: A Quasi–maximum Likelihood Estimation Approach

2018 ◽  
Vol 18 (1) ◽  
pp. 206-222 ◽  
Author(s):  
Fernando Rios-Avila ◽  
Gustavo Canavire-Bacarreza
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Error Correction ◽  
Likelihood Estimation ◽  
Optimization Models ◽  
Standard Errors ◽  
Two Stage ◽  
Maximum Likelihood Approach ◽  
Likelihood Approach ◽  
Quasi Maximum Likelihood

Following Wooldridge (2014, Journal of Econometrics 182: 226–234), we discuss and implement in Stata an efficient maximum-likelihood approach to the estimation of corrected standard errors of two-stage optimization models. Specifically, we compare the robustness and efficiency of the proposed method with routines already implemented in Stata to deal with selection and endogeneity problems. This strategy is an alternative to the use of bootstrap methods and has the advantage that it can be easily applied for the estimation of two-stage optimization models for which already built-in programs are not yet available. It could be of particular use for addressing endogeneity in a nonlinear framework.

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