https://github.com/aditiisaxena/reservoir-water-level-analysis
Econometrics project that aims to analyze the relationship between Reservoir Water Level and various Power Inequalities in different states of India.
https://github.com/aditiisaxena/reservoir-water-level-analysis
anova-analysis chow-test data-transformation dataset econometrics f-statistics maximum-likelihood-estimation monte-carlo-simulation r-language regression-models statistical-analysis t-test
Last synced: about 2 months ago
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Econometrics project that aims to analyze the relationship between Reservoir Water Level and various Power Inequalities in different states of India.
- Host: GitHub
- URL: https://github.com/aditiisaxena/reservoir-water-level-analysis
- Owner: aditiisaxena
- Created: 2023-04-17T18:11:52.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-05-19T12:37:48.000Z (about 2 years ago)
- Last Synced: 2025-02-10T08:13:39.552Z (4 months ago)
- Topics: anova-analysis, chow-test, data-transformation, dataset, econometrics, f-statistics, maximum-likelihood-estimation, monte-carlo-simulation, r-language, regression-models, statistical-analysis, t-test
- Language: R
- Homepage:
- Size: 1.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Reservoir Water Level Data Analysis
The Reservoir Water Level Data Analysis project aims to examine the impact of economic output and income inequality on the Reservoir Water Level. The following steps were taken to complete the enhanced version of this project:
## Enhanced Regression Model
The original regression model was enhanced by incorporating indicators for power inequality, including literacy rate, mortality rate, per capita income, unemployment, and rainfall. These variables were added to the model to better explain the variation in the environmental quality indicator.
## Alternative Data Source
Instead of using district-level data for the Gini index, state-census-year level data provided by Pandey and Gautam (2020) was utilized to test how this substitution of variables alters the results.
## Standard Errors in Regression Analysis
The role of standard errors in regression analysis was clearly articulated for the context of this study. Standard errors were utilized to measure the precision of the estimated coefficients and to test hypotheses about population parameters.
## Structural Break Test
A Chow Test and a t-test were conducted to statistically test for a structural break in the mean environmental quality across different state-groups. The inference drawn from these tests was compared.
## Monte Carlo Simulation
The enhanced regression model was used to run a Monte Carlo simulation procedure to comment on the consistency of OLS estimates. The intercept and slope coefficient estimates obtained were treated as true population parameters.
## Maximum Likelihood Estimation
The maximum likelihood strategy was considered as a means to improve the estimation of the enhanced regression model.
## Variance in Environmental Quality Across State Groups
It was found that the average environmental quality differs significantly across state groups. A test was conducted to examine the variance in environmental quality across the state-groups. If the variance differs significantly across state groups, the assumption of OLS is violated. An alternative estimation strategy was suggested to account for such a variance structure.