Land Rights and Improvement Investments Among Crop Farmers in Southwest, Nigeria

2021 ◽  
Vol 16 (1) ◽  
pp. 1-12
Author(s):  
Lawrence Olusola Oparinde
Keyword(s):  
Maximum Likelihood ◽  
Regression Model ◽  
Land Rights ◽  
Multivariate Probit ◽  
Probit Regression ◽  
Two Stage ◽  
Conditional Maximum Likelihood ◽  
Farm Productivity ◽  
Southwest Nigeria ◽  
Conditional Maximum

This study examined the effects of land rights on investment decisions of farmers and determinants of land rights as well as the impact of land rights on farm productivity in Southwest Nigeria. Multistage sampling process was used in the selection of 320 respondents for the study. Two-stage conditional maximum likelihood (2SCML) approach of multivariate probit regression model and Endogenous switching regression (ESR) model were used for the analysis of the collected data. The results of Average Treatment Effect on the Treated (ATT) estimates from ESR revealed that land rights increase farm productivity. This study further confirmed the importance of land rights in facilitating investment in soil-improving measures which subsequently leads to higher productivity. One of the policy implications emanating from this study suggests that having use and transfer rights as against use only rights goes a long way in enhancing investment in soil-improving or conservation measures. Keywords: Crop, Investment, Land, Multivariate Probit Regression Model, Rights, Two-stage Conditional Maximum Likelihood.

scholarly journals Evaluation of direct and indirect methods for modelling the joint distribution of tree diameter and height data with the bivariate Johnson’s SBB function to forest stands

2019 ◽  
Vol 28 (1) ◽  
pp. e004 ◽  
Author(s):  
Jose Javier Gorgoso-Varela ◽  
Friday Nwabueze Ogana ◽  
Rafael Alonso Ponce
Keyword(s):  
Maximum Likelihood ◽  
Direct Method ◽  
Suitable Method ◽  
Two Stage ◽  
Indirect Methods ◽  
Conditional Maximum Likelihood ◽  
Significant Difference ◽  
Height Prediction ◽  
Conditional Maximum ◽  
Direct And Indirect Methods

Aim of study: In this study, both the direct and indirect methods by conditional maximum likelihood (CML) and moments for fitting Johnson’s SBB were evaluated. To date, Johnson’s SBB has been fitted by either indirect (two-stage) method using well-known procedures for the marginal diameter and heights, or direct methods, where all parameters are estimated at once. Application of bivariate Johnson’s SBB for predicting height and improving volume estimation requires a suitable fitting method.Area of study: E. globulus, P. pinaster and P. radiata stands in northwest Spain.Material and methods: The data set comprised of 308, 184 and 96 permanent sample plots (PSPs) from the aforementioned species. The suitability of the method was evaluated based on height and volume prediction. Indices including coefficient of determination (R2), root mean square Error (RMSE), model efficiency (MEF), Bayesian Information Criterion (BIC) and Hannan-Quinn Criterion (HQC) were used to assess the model predictions. Significant difference between observed and predicted tree height and volumes were tested using paired sample t-test at 5% level for each plot by species.Main results: The indirect method by CML was the most suitable method for height and volume prediction in the three species. The R2 and RMSE for height prediction ranged from 0.994 – 0.820 and 1.454 – 1.676, respectively. The percentage of plot in which the observed and predicted heights were significant was 0.32%. The direct method was the least performed method especially for height prediction in E. globulus.Research highlights: The indirect (two-stage) method, especially by conditional maximum likelihood, was the most suitable method for the bivariate Johnson’s SBB distribution.Keywords: conditional maximum likelihood; moments; two-stage method; direct method; tree volume.

THE INFORMATION BOUND OF A DYNAMIC PANEL LOGIT MODEL WITH FIXED EFFECTS

2001 ◽  
Vol 17 (5) ◽  
pp. 913-932 ◽  
Author(s):  
Jinyong Hahn
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Fixed Effects ◽  
Dynamic Panel Data ◽  
Consistent Estimator ◽  
Likelihood Estimator ◽  
Dynamic Panel ◽  
Conditional Maximum Likelihood ◽  
Information Bound ◽  
Conditional Maximum

In this paper, I calculate the semiparametric information bound in two dynamic panel data logit models with individual specific effects. In such a model without any other regressors, it is well known that the conditional maximum likelihood estimator yields a √n-consistent estimator. In the case where the model includes strictly exogenous continuous regressors, Honoré and Kyriazidou (2000, Econometrica 68, 839–874) suggest a consistent estimator whose rate of convergence is slower than √n. Information bounds calculated in this paper suggest that the conditional maximum likelihood estimator is not efficient for models without any other regressor and that √n-consistent estimation is infeasible in more general models.

Hidden Neural Networks

1999 ◽  
Vol 11 (2) ◽  
pp. 541-563 ◽  
Author(s):  
Anders Krogh ◽  
Søren Kamaric Riis
Keyword(s):  
Neural Networks ◽  
Maximum Likelihood ◽  
Graphical Model ◽  
Markov Models ◽  
Compact Representation ◽  
Conditional Maximum Likelihood ◽  
The Neural Networks ◽  
Maximum Likelihood Criterion ◽  
Performance Gains ◽  
Conditional Maximum

A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear performance gains compared to standard HMMs tested on the same task.

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