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Chapter 2 – Estimating Advanced Multicollinearity Solution Models using R

When there is high multicollinearity confirmed between the independent variables and you are forced to include all of them in the model, it will lead to over estimated regression coefficients. Since they are changed, now they are biased. This is because high collinearity makes inverse of the matrix bigger. This tutorial provides guidance to estimate […]

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Chapter 1 – GGPlot for GIS Maps in R

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GGPLOT can be used to draw GIS maps in R and fill uni-variate colors to show spatial differences across countries.

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Chapter 3 – Quantile ARDL Model in R

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Quantile regression is used when the variables are not normal and there appropriate transformations are not feasible. Consider a case of a variable which can have negative values and zeros so taking a log will not help in making data normal. Following tutorial will guide you how to estimate quantile regression for non stationary variables.

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Chapter 3 – R Studio – Estimating Non-Linear / Asymmetric Effects ARDL

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The asymmetric effects ARDL or non linear ARDL is used when it is sure that the effect of increasing independent variable is not equal and opposite to effect of decreasing independent variable and the variables are in mixed order of integration.

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Chapter 3 – Structure Detection / Index Making / Confirmatory Factor Analysis in R Studio

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This tutorial explores the structure detection approach to reduce the variables into relevant sub-indices. This is a second method to reduce the dimensions to avoid multicollinearity when variables are forming theoretical groups. This method is the first step to estimate SEM model. This method can be used to constitute indexes of unequal weight-ages rather than

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Chapter 1 – Principal Factor Analysis Using R Studio

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Factor analysis method is used to reduce the dimensions of the data. Each new independent variable in the analysis is a new dimension. So if by luck you have many relevant independent variable, you man end up in a dilemma regarding which variable to keep and which one to drop. As using all of them

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Chapter 4 – Fixed and Random Effect Panel Data models in R studio

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Panel data models are used when the data is varying across time and cross-sections. In such casing using OLS will have to assume any one of the dimension as constant and other as random, but in reality we cannot assume the country differences to be constant or random. Hence this leads to the concept of

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Chapter 3 – ARIMA – ARCH/GARCH Models using R Studio

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AutoRegressive Integrated Moving Average (ARIMA) Models are used to find the unpredictable deviation from the mean portion of the series. And if the data has high frequency then AutoRegressive Conditional Heteroskedasticity (ARCH) or Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models are used to estimate the volatility of the series.

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Chapter 1 – R Studio – Stepwise Regressions

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This session details on estimating Stepwise regression in R Studio as a solution to weak multicollinearity.

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Chapter 1 – R Studio – Detailed Testing of Multicollinearity

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This session will account to a detailed testing of multicollinearity. This include generation of a scatter plot matrix with correlation values and histograms useful for descriptive statistics. Further there is a wide array of tests including VIF, Tolerence test, |X’X| test etc.

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