I provide the R code for 02 take-home projects in non-parametric course (Master 2, EEE, TSE). This repository includes the following code files, corresponding to [Mai-Anh Dang] Report-2.pdf and [Mai-Anh Dang] Report-1.pdf
| Report | Code files |
|---|---|
| Report-1 | mean_regression.R |
| Report-1 | simulation_kernel.R |
| Report-1 | density_estimate.R |
| ---------------------- | ---------------------------- |
| Report-2 | dWADE.R |
| Report-2 | nonparam_bootstraps.R |
| Report-2 | qtile_reg_BG90.R |
The theoretical and formal equations behind each methods and code files could be found in the associated reports. The methods covered include:
- Kernel Estimator
- Mean Regression Functions: Local Constant, Local Linear
- Density-weighted Average Derivative Estimator (dWADE)
- Bootstraps to construct Confident Interval for non-parametric estimates
- Bhattacharya and Gangopadhyay (1990) estimator
- Quantile Regressions
These mentioned methods are conducted and assessed through both simulations and pratical applications, using the below data:
- GDP 2005 and 2016
data/GDP.xlsx - Annual Household Income and Food Expenditure in Belgium
data/Engel.dta - House Price and Other Charactersitics
data/anglin.gencay.1996.csv
The nonparametric kernel estimators do not fit the true density perfectly, but performing quite well, even for the small sample of n = 100. When n increase, order of error decrease. For the large sample n = 1000, the estimated density is closer to the true curve.
We use the Pivotal Bootstraps approach to construct the CIs for density estimates on GDP.xlsx. Based on these interval, we test the null hypotheses visually.
To estimate the expected Food Expenditure at a given level of Income, on the data set Engel.dta
To estimate the expected Income at a given level of Food Expenditure, on the data set Engel.dta
This method is applied n the data set anglin.gencay.1996.csv for a hedonic analysis, describing the relationship between housing price and observed characteristics.