data1

US production, captal, labour in natrual logs for the year 2005

datasets

Calculates the optimal level of significance based on a decision-theoretic approach.
The optimal level is chosen so that the expected loss from hypothesis testing is minimized.
A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model.
The details are covered in Kim, Jae H. and Choi, In, 2020, Choosing the Level of Significance: A Decision-Theoretic Approach, Abacus.
See also Kim, Jae H., 2020, Decision-theoretic hypothesis testing: A primer with R package OptSig, The American Statistician.

Jae H Kim

OptSig

Optimal Level of Significance for Regression and Other
Statistical Tests

data1 function

A data frame with 51 observations on the following 3 variables.<dl class="dl-horizontal">
 <dt><code>lnoutput</code></dt><dd>natrual log of output</dd>
 <dt><code>lnlabor</code></dt><dd>natrual log of labor</dd>
 <dt><code>lncapital</code></dt><dd>natrual log of capital</dd>
 </dl>

Format

Data for the U.S. production function estimation — data1

A data frame with 51 observations on the following 3 variables.<dl class='dl-horizontal'>
 <dt><code>lnoutput</code></dt><dd>natrual log of output</dd>
 <dt><code>lnlabor</code></dt><dd>natrual log of labor</dd>
 <dt><code>lncapital</code></dt><dd>natrual log of capital</dd>
 </dl>

data1: Data for the U.S. production function estimation

Description

Usage

Arguments

Format

Details

References

Examples