Regression-model
In AFR: Toolkit for Regression Analysis of Kazakhstan Banking Sector Data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(AFR)
library(olsrr)
library(stats)

Introduction

As regressors are chosen for a linear regression model, AFR package recommends to check for:

1. Optimal size of the time-series data

Function opt_size assess whether time-series data has enough observations for the chosen model.

model<-lm(real_gdp~imp+exp+usdkzt+eurkzt, macroKZ)
opt_size(model)

Based on the output of the function, modify the model, i.e. remove or add regressor(s).

2. Choose the best regression model

From the initially built linear regression model regsel_f function allows to choose the best regressors by Akaike Information criterion (AIC) and Adjusted R-squared (Adj R2) parameters. These parameters are set by default, but other parameters can be added too.

To dive into details, check_betas function demonstrates all models with regressors' betas based on which regsel_f function gives the result. A user can export the output of all models into Excel document for more representative format by using function write_xlsx of writexl package.

check_betas(model)

3. Analysis of the model

As regsel_f gave the best regression model, it can be analysed by diagnostic tests for the compliance with Gauss-Markov theorem for a multiple regression model.

Graphically, the regression model can be visualized for decomposition and forecasting. Function dec_plot demonstrates a contribution of each regressor in a form of stacked bar plot.