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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The Development Impact of Foreign Aid: Story Re-told

There has been an intense debate in the literature over the reasons behind the loose developmental effects of foreign aid. Away from the straightforward reason that majority of aid flows follow political rather than development objectives (Kanbur et al., 1999; Dreher, et al. 2009). Further, several reasons have been introduced in the literature see for …

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Can cointegration analysis solve spurious regression problem?

The efforts to avoid the existence of spurious regression has led to the development of modern time series analysis (see How Modern Time Series Analysis Emerged? ). The core objective of unit root and cointegration procedures is to differentiate between genuine and spurious regression. However, despite the huge literature, the unit root and cointegration analysis …

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The impact of functional form over the normality assumption in the residuals

A discussed solution in order to accomplish the normality assumption in regression models relates to the correct specification of a Data Generating Process (Rodríguez Revilla, 2014), the objective here is to demonstrate how functional form might influence the distribution of the residuals in a regression model using ordinary least squares technique. Let’s start with a …

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