Children Marriage: Alarming & Unattended Phenomenon in the Middle East

A forthcoming paper has used MICS UNICEF Survey Data for a sample of three Middle Eastern countries: Egypt, Sudan, and Palestine and employed Multilevel logistic regression to empirically investigate the impact of child marriage on a large set of women and children health-related indicators. The results showed that child marriage is generally associated with giving […]

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The budget constraints in the microeconomic approach

Following the last post which gave an example to model the Cobb-Douglas utility function regarding microeconometrics, we need to provide an important aspect related to the behavior of the consumer. That is the budget constraint (referred to as a monetary linear constraint) which limits the number of goods that the consumer can buy and use

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A brief example to model the Cobb-Douglas utility function using Stata.

Regarding microeconometrics, we can find applications that go from latent variables to model market decisions (like logit and probit models) and techniques to estimate the basic approaches for consumers and producers. In this article, I want to start with an introduction of a basic concept in microeconomics, which is the Cobb-Douglas utility function and its

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YEAR 2019

In our first YEAR “2019”, we had **Over 120,000 visits to our institute website **Over 50 completed interactive econometrics private and group training **Around 70 researchers, 10 of them successfully completed their Ph.D. degrees, have booked our methodological and statistical guidance services ** 1 annual conference, and around 5 onsite and online workshops ** 5

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Robust Modeling of Policy Changes: Difference in Difference (DiD)

The difference in Difference (DiD) is a popular method in empirical economics and has important applications in other social sciences as well. DID is a quasi-experimental design that uses panel data to estimate the effects of specific intervention or treatment (such as policy changes, new laws, social program implementation) on outcomes over time and between

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Taking Logarithms of Growth Rates and Log-based Data.

A usual practice while we’re handling economic data, is the use of logarithms, the main idea behind using them is to reduce the Heteroscedasticity -HT- of the data (Nau, 2019). Thus reducing HT, implies reducing the variance of the data. Several times, different authors implement some kind of double logarithm transformation, which is defined as

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