Provides a detailed summary of the regression model and hypothesis test for
the first efficient release identified by the
get_first_efficient_release() function.
Usage
# S3 method for class 'lst_efficient'
summary(object, ...)Value
Returns a tibble with the following columns:
id: The identifier of the time series (if present in input data).e: The index of the first efficient release.alpha: The intercept coefficient of the regression model.beta: The coefficient of the slope.p-value: The p-value for the joint hypothesis (alpha = 0 and beta = 1).n_tested: The number of releases tested.
Details
This function prints the following information:
The index of the first efficient release.
A summary of the regression model fitted for the efficient release, which includes coefficients, R-squared values, and other relevant statistics.
The hypothesis test results for the efficient release, showing the test statistic and p-value for the null hypothesis of unbiasedness and efficiency.
The function assumes the object includes:
e: The index of the first efficient release (0-based).models: A list of linear regression models for each release.tests: A list of hypothesis test results corresponding to each release.
Examples
# Example usage
df <- get_nth_release(
tsbox::ts_pc(dplyr::filter(reviser::gdp , id=="US")),
n = 1:4
)
final_release <- get_nth_release(
tsbox::ts_pc(dplyr::filter(reviser::gdp, id=="US")),
n = 10
)
# Identify the first efficient release
result <- get_first_efficient_release(df, final_release, significance = 0.05)
summary(result)
#> Efficient release: 0
#>
#> Model summary:
#>
#> Call:
#> stats::lm(formula = formula, data = df_wide)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.88971 -0.12583 0.02686 0.12286 0.69564
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.0002137 0.0204027 0.01 0.992
#> release_1 0.9757504 0.0154917 62.98 <2e-16 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Residual standard error: 0.231 on 166 degrees of freedom
#> (9 observations deleted due to missingness)
#> Multiple R-squared: 0.9598, Adjusted R-squared: 0.9596
#> F-statistic: 3967 on 1 and 166 DF, p-value: < 2.2e-16
#>
#>
#> Test summary:
#>
#> Linear hypothesis test:
#> (Intercept) = 0
#> release_1 = 1
#>
#> Model 1: restricted model
#> Model 2: final ~ release_1
#>
#> Note: Coefficient covariance matrix supplied.
#>
#> Res.Df Df F Pr(>F)
#> 1 168
#> 2 166 2 2.2448 0.1092