Tjohn Data Analytics

07/06/2023

The multinomial logit model (MNL) fits a regression analysis for a categorical dependent variable with outcomes that have no natural ordering i.e. nominal dependent variable. It uses the maximum likelihood estimation (MLE) process.

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15/05/2023

Generalized Method of Moments (GMM) is a statistical method that combines observed economic data with the information in population moment conditions to produce estimates of the unknown parameters of the economic model.

GMM fits both single and multiple equation models. It uses the maximum likelihood estimation (MLE) procedure. GMM works with cross-sectional, time-series and panel data.

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30/01/2023

Endogenous Switching Regression (ESR) models are natural extensions of classical experimental designs, which allow tests of assumptions about the exogeneity of treatment effects from research/survey data. A selection equation sorts observation units over two different regimes (e.g. treated & not-treated; adopter & non-adopter) and two outcome equations that determines the outcome. ESR uses the full information maximum likelihood estimation method.

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07/12/2022

07/12/2022

FGT curves are useful distributive tools that can inter alia be used to show:
• how the level of poverty varies with different poverty lines;
• test for poverty dominance between two distributions;
• test pro-poor growth conditions

Slide into my dm if your research is focused on measuring poverty incidence; food (in)security; inequality and other related distributive analysis. You can as well reach out on:
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10/11/2022

Some , and I provide using :
• Data cleaning and manipulation if needed
• Descriptive Analysis
summary tables
frequency tables
• Cross-sectional data analysis
• Panel data analysis
fixed effect
random effect
• Time Series analysis
auto-regression models
error correction models
• Regression models for categorical dependent variables
Logit/ Probit models
Ordered Logit/Probit
Multinomial Logit models
• Distributive analysis
FGT analysis:
food security/ insecurity analysis
poverty analysis
Inequality index
• Treatment effect models
Regression adjustment
Inverse probability weighting
Inverse probability weighting
regression adjustment
• Augmented inverse probability weighting
Nearest neighbour matching
Propensity score matching

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13/09/2022

Stochastic Frontier Analysis (SFA) is the most frequently used parametric approach for efficiency estimation. SFA fits stochastic production (technical efficiency) or cost frontier (profit efficiency) models. SFA provides efficiency estimates/ efficiency scores of individual producers. Frontier fits models in which the inefficiency component is assumed to be from a half-normal, exponential or truncated-normal distribution.
Are you doing your research/project/assignment on frontier analysis? Contact me for in-depth analysis.
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23/05/2022

On a client's work. Data preparation for Multidimensional Poverty Index (MPI). Created binary indicators from domains of & living standard. Trying to examine the MPI among sweet potato farmers in S/W Nigeria. Are you struggling with your dissertation data analysis? DM for help @ affordable cost.

12/04/2022

Good data knowledge is vital to selecting the appropriate analytical model to achieve research/survey purpose.
Are you floating on what analysis method best suits your data? Reach out to us today and give your research a progressive move.

24/03/2022

Foster-Greer-Thorbecke [FGT] analysis is one of the contemporary models designed to measure poverty. The FGT poverty index is a weighted sum of the poverty gap ratios of the poor. FGT analysis is best known for its versatility, in that it is also widely used in measuring food security index.

21/03/2022

Frequency tables are a good way to present data related to a variable, as it indicates for each given variable, all the values of that variable, the number of times each value/category appears & the proportion represented by the data corresponding to a category of a variable over total number of observations.

28/02/2022

Translate your information into a visual context to make data easier to understand and pull insights from.
The main purpose of visualization is to make it easier to identify patterns, trends and outliers in large data sets.

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