## 21 Jun Chapter 1 – GGPlot for GIS Maps in R

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

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

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...

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. Nonlinear ARDL and Assymetric...

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...

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...

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...

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...

This session details on estimating Stepwise regression in R Studio as a solution to weak multicollinearity. importing libraries library(tidyverse) # for data managementlibrary(readxl) # to read excel filelibrary(olsrr) # post regression tests importing data auto <- read_excel("D:/UMT notes/MPhil - MS courses/Applied Econometrics/lectures applied econometrics/lecture 6/stepwise regression/auto.xlsx") Stepwise regression swr <- lm(mpg...

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. importing libraries library(tidyverse) # for...

https://www.youtube.com/watch?v=g4mwbExrHO0 In this session we are going through a detailed account to assess post regression diagnostics in Cross sectional OLS using R Studio. These tests include Outlier Test, Influential Observations test, Non linearity test, Non normality test, multicollinearity test, heteroskedasticity test, and autocorrelation test ...

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