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.
Chapter 3 – Quantile ARDL Model in R Read More »
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.
Chapter 3 – R Studio – Estimating Non-Linear / Asymmetric Effects ARDL Read More »
Panel data is used when the data is varying across time and cross section, within which when your panel data has years per cross section more than 20. Then the data becomes long panel data thus simple OLS (FE / RE ) may not work. This tutorial provides the illustration of how to install xtpmg
Chapter 4 – Panel ARDL / PMG model in STATA Read More »
Panel Non Linear Autoregressive Distributed Lagged Model is used for the case of Panel data where number of years per cross section are more than 20 and the variables are non stationary and there is asymmetric effects in at least one of the independent variable. Asymmetric effects mean that the effect of increasing component of
Chapter 4 – Non Linear Panel ARDL model in STATA Read More »
When the independent variables are collinear with each other, and there is no option to reduce them using step wise regression or factor analysis as all of them are important. Then in this case ridge regression can be used to estimate the model by force and adjust the biasness caused by the collinearity
Chapter 1 – Ridge Regression for Collinear Estimates in STATA Read More »
Python Jupyter Notebook platform can be used to estimate simple regression on a data set. Python is one of the leading programming language with its application is data science.
Chapter 1 – Python Jupyter – Estimating Simple Regression Read More »
This tutorial will guide you to importing your data in SPSS Software for your data analysis purposes. This video specifically talks about importing the secondary data from excel.
Chapter 1 – Importing data in SPSS Read More »
When you have too many independent variables which are expected to be collinear too, the you have to reduce the number of the variables from the model so that the biasness from multicollinearity is removed. This tutorial provides you demonstration on one of the method which forms subgroups of variables based on their similarity in
Chapter 3 – Structure Detection Using SPSS Read More »
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
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
Chapter 1 – Principal Factor Analysis Using R Studio Read More »