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This is the R code for several common non-parametric methods (kernel est., mean regression, quantile regression, boostraps) with both practical applications on data and simulations

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anhdanggit/non-parametric-econometrics

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Non-parametric Methods

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
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Report-2 dWADE.R
Report-2 nonparam_bootstraps.R
Report-2 qtile_reg_BG90.R

Theoretical frameworks

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

Applications

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

Simulation: Non-parametric Density Estimate

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. alt tag

Density Estimate and CI by Bootstraps

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.
alt tag

Mean Regression: Local Linear and Local Constant

To estimate the expected Food Expenditure at a given level of Income, on the data set Engel.dta alt tag

BG90 Estimator and Quantile Regression

To estimate the expected Income at a given level of Food Expenditure, on the data set Engel.dta alt tag

dWADE Estimator

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.

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This is the R code for several common non-parametric methods (kernel est., mean regression, quantile regression, boostraps) with both practical applications on data and simulations

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