## 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)
#> There is acceptable number of observations.
#> It is necessary to have 24 observations.
#> Your regression has 57 observations.
#> Warning in opt_size(model): If there is equal or close number of observations,
#> please check further.
```

Regression-model.R

Based on the output of the function, modify the model, i.e.Â 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 regressorsâ€™ 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.

Regression-model.R

### 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.

Regression-model.R

Function *reg_plot* shows actual and forecast data.
Forecasting can be performed by Arima or trending.

Regression-model.R