| Title: | Econometrics Model Building |
| Version: | 0.1.1 |
| Description: | An intuitive and user-friendly package designed to aid undergraduate students in understanding and applying econometric methods in their studies, Tailored specifically for Econometrics and Regression Modeling courses, it provides a practical toolkit for modeling and analyzing econometric data with detailed inference capabilities. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| Imports: | car, forecast, ggplot2, insight, lmtest, moments, stats, tibble, tseries |
| Depends: | R (≥ 2.10) |
| LazyData: | true |
| Suggests: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-01-24 05:15:02 UTC; Mutua Sam |
| Author: | Mutua Kilai  [aut,
cre] |
| Maintainer: | Mutua Kilai <kilaimutua@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-01-28 16:30:06 UTC |
Plots ACF of a univariate time series
Description
Plots ACF of a univariate time series
Usage
ACF_PLOT(x, lag.max = NULL)
Arguments
x |
numeric vector |
lag.max |
maximum lag to calculate the acf |
Value
a plot of the acf vs lag
Author(s)
Mutua Kilai
Examples
data(keconomy)
attach(keconomy)
ACF_PLOT(UR)
Plots PACF of a univariate time series
Description
Plots PACF of a univariate time series
Usage
PACF_PLOT(x, lag.max = NULL)
Arguments
x |
a numeric vector |
lag.max |
maximum lag to calculate pacf |
Value
a plot of the pacf vs lag
Author(s)
Mutua Kilai
Examples
data(keconomy)
attach(keconomy)
PACF_PLOT(UR)
Check model for residual independence
Description
Checks model for independence of residuals
Usage
autocorrelation_assumption(model)
Arguments
model |
A lm object |
Value
returns the p-value for the test
Author(s)
Mutua Kilai
Examples
model <- lm(pi ~ hs + ps, data = eduperform)
autocorrelation_assumption(model)
Select Optimal Model based on BIC
Description
Select Optimal Model based on BIC
Usage
best_arima(data, max_p = 5, max_d = 2, max_q = 5)
Arguments
data |
A univariate ts object |
max_p |
Maximum AR order |
max_d |
Maximum differencing order |
max_q |
Maximum MA order |
Value
A list containing the optimal model results and the BIC value
Examples
data(keconomy)
attach(keconomy)
best_arima(UR, max_p = 5, max_d = 2, max_q = 5)
Checking Overall Model Significance
Description
Checking Overall Model Significance
Usage
check_model_sig(data, y, x)
Arguments
data |
A data frame containing the variables to use |
y |
The dependent variable |
x |
A set of independent variables |
Value
p-value with a statement on whether the model is significant or not
Author(s)
Mutua Kilai
Examples
check_model_sig(data = eduperform, "pi", c("hs", "ps"))
Check Series for Weak Stationarity
Description
Check Series for Weak Stationarity
Usage
check_stationarity(x)
Arguments
x |
A numeric vector or time series object |
Value
p-value of the test
Author(s)
Mutua Kilai
Examples
data(keconomy)
attach(keconomy)
check_stationarity(UR)
Student Performance Data
Description
Student performance dataset is a dataset designed to examine the factors influencing academic student performance.
Usage
eduperform
Format
eduperform
A data frame with 10000 rows and 6 columns:
- hs
hours studied
- ps
previous score
- ea
extracurricula activities
- sh
sleep hours
- sqpp
sample question paper practiced
- pi
performance Index
...
Source
Fit ARIMA models to univariate data
Description
Fit ARIMA models to univariate data
Usage
fit_arima(data, p, d, q)
Arguments
data |
a univariate class object or a vector |
p |
AR order |
d |
differencing order |
q |
MA order |
Value
A tibble containing the estimate, SE and p-value
Examples
data(keconomy)
attach(keconomy)
fit_arima(UR, p=2,d=0,q=3)
Get variance of the model coefficients
Description
Get variance of the model coefficients
Usage
get_coefficients_variance(data, y, x)
Arguments
data |
A data frame containing the variables to use |
y |
The dependent variable |
x |
A set of independent variables |
Value
Tibble containing the variances
Author(s)
Mutua Kilai
Examples
get_coefficients_variance(data = eduperform, "pi", c("hs", "ps"))
Confidence Intervals of Model Parameters
Description
Confidence Intervals of Model Parameters
Usage
get_confint(data, y, x, level = 0.95)
Arguments
data |
A data frame containing the variables to use |
y |
The dependent variable |
x |
A set of independent variables |
level |
level of significance can be 0.95, 0.90 etc. default is 0.95 |
Value
tibble containing the lower and upper confidence intervals
Author(s)
Mutua Kilai
Examples
get_confint(data = eduperform, "pi", c("hs", "ps"))
Obtaining only significant predictors from a model
Description
Obtaining only significant predictors from a model
Usage
get_significant_predictors(data, y, x, alpha = 0.05)
Arguments
data |
A data frame containing the variables to use |
y |
The dependent variable |
x |
A set of independent variables |
alpha |
desired alpha level. default is 0.05 |
Value
A tibble containing the significant predictors
Author(s)
Mutua Kilai
Examples
get_significant_predictors(data = eduperform, "pi", c("hs", "ps"))
Checking heteroscedasticity assumption
Description
Checking heteroscedasticity assumption
Usage
heteroscedasticity_assumption(model)
Arguments
model |
A lm model object |
Value
The p-value of the test statistic.
Author(s)
Mutua Kilai
Examples
model <- lm(pi ~ hs + ps, data = eduperform)
heteroscedasticity_assumption(model)
Kenya Unemployment Rate and GDP Growth rate for 1999-2023
Description
Annual Time Series data for Kenyan Economy showing the unemployment rate and GDP Growth Rate.
Usage
keconomy
Format
keconomy
A data frame with 25 rows and 3 columns:
- Year
Year; from 1999 to 2023
- UR
Unemployment Rate
- GR
GDP Growth Rate
Source
Multicollinearity Assumption
Description
Multicollinearity Assumption
Usage
multicollinearity_assumption(model)
Arguments
model |
A lm object |
Value
A tibble containing the VIF and Tolerance values
Author(s)
Mutua Kilai
Examples
model <- lm(pi ~ hs + ps, data = eduperform)
multicollinearity_assumption(model)
Checking normality of residuals
Description
Checking normality of residuals
Usage
normality_assumption(model)
Arguments
model |
A lm model object |
Value
The p-value of the test statistic.
Author(s)
Mutua Kilai
Examples
model <- lm(pi ~ hs + ps, data = eduperform)
normality_assumption(model)
Fitting a simple or multiple linear regression
Description
Fitting a simple or multiple linear regression
Usage
ols_model(data, y, x)
Arguments
data |
A data frame containing the variables to use |
y |
The dependent variable |
x |
Set of independent variables |
Value
A tibble of the coefficients, standard errors, t-statistics and p-value
Author(s)
Mutua Kilai
Examples
ols_model(data = eduperform, "pi", c("hs", "ps"))
F-statistic attributes
Description
F-statistic attributes
Usage
ols_model_sig(data, y, x)
Arguments
data |
A data frame containing the variables to use |
y |
The dependent variable |
x |
Set of independent variables |
Value
A tibble containing the number of observations, F-Statistic, degrees of freedom and p-value
Author(s)
Mutua Kilai
Examples
ols_model_sig(data = eduperform, "pi", c("hs", "ps"))
Model Summary Statistics
Description
Model Summary Statistics
Usage
ols_model_stats(data, y, x)
Arguments
data |
A data frame containing the variables to use |
y |
The dependent variable |
x |
The independent variables |
Value
A tibble containing model summary stats: R-Squared, Adjusted R-Squared, AIC and BIC
Author(s)
Mutua Kilai
Examples
ols_model_stats(data = eduperform, "pi", c("hs", "ps"))
Prediction from new observations
Description
Prediction from new observations
Usage
predict_dep_var(model, new_data, level = 0.95)
Arguments
model |
an lm object |
new_data |
data frame containing the new set of predictors |
level |
confidence level, default 0.95 |
Value
A tibble containing the predicted value and the upper and lower CI
Author(s)
Mutua Kilai
Examples
model <- lm(pi ~ hs + ps, data = eduperform)
newdata <- data.frame(hs =c(2,3,4),ps = c(23,24,12))
predict_dep_var(model, new_data = newdata, level = 0.95)
Choosing Best Model Based on AIC, BIC and Adjusted R Squared
Description
Choosing Best Model Based on AIC, BIC and Adjusted R Squared
Usage
select_optimal_model(models, criterion = "AIC")
Arguments
models |
a list of models |
criterion |
The criterion to select optimal model. Default AIC |
Value
list of the results and best model
Author(s)
Mutua Kilai
Examples
data(eduperform)
model1 <- lm(pi ~ hs, data = eduperform)
model2 <- lm(pi ~ hs + ps, data = eduperform)
model3 <- lm(pi ~ hs + ps + sh, data = eduperform)
models <- list(model1, model2, model3)
select_optimal_model(models, criterion= "AIC")