Publications by Arvind Sharma
HW2_template_hints
Table of contents Setup Working Directory and Data Load packages Exploratory Data Analysis (EDA) Missing Data Impute Missing Values Summary Statistics sumtable Re-coding data values Renaming variables Income, Home Value, Blue Book Stargazer Graphing Categorical Variables Car Types Jobs Visualization MODELS MODEL SELECTION Useful code fo...
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Diff_in_Diff_hurricane
Difference in Difference Dr. Arvind Sharma Introduction Difference-in-differences (DiD) is a statistical technique commonly used in Econometrics and social sciences to estimate the causal effect of an intervention or policy change (aka “treatment”) on an outcome of interest Origins of Diff-in-Diff: John Snow’s Cholera1 Hypothesis (water ...
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Diff in Diff
Table of contents EXAMPLE 1: Estimating Diff-in-Diff Getting sample data. Create a dummy variable to identify the group exposed to the treatment. Interaction Term Estimating the DID estimator Estimating the DID estimator EXAMPLE 2 Identifying Assumptions Parallel Trends Assumption: No Spillover Effects: No Other Contemporaneous Events: Suffici...
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OVB
Table of contents OVB EXAMPLE: Gender Discrimination in Wages Data Full Regression Short Regression Check for OVB Condition I. X is correlated with the omitted variable. Condition II. The omitted variable is a determinant of the dependent variable Y. Predicting Biased Coefficient Value EXAMPLE: Test Scores and Student Teacher Ratio English L...
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Residual_Plots
Table of contents Linear Regression Specification Residual Analysis Plot of Fitted vs Residual Creating Manually Normal QQ plot of the residuals Creating Manually Scale Location Standardized residuals of a linear regression model Creating Manually Residual versus leverage chart Creating Manually Linear Regression: Residual Analysis # Cl...
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variance
Table of contents mtcars dataset Multivariate Regression Examine Residuals Summary Stats on Residuals Interpretation of Coefficients Replicating t value for \(\beta_1\) Replicating p value for \(\beta_1\) Replicating residual standard error for a regression Multivariate Linear Regression with matrix algebra Point estimates Setup Derivation S...
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typing_equations
Can replace ‘html_document’ with ‘pdf_document’ in the .Rmd (Rmarkdown) file above manually to generate the output in your preferred format. However, I would strongly suggest using HTML format initially as the setup is likely to reduce math symbol issues if you do not have Latex installed locally. Also, make sure to change the settings of t...
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Types_of_Data
1 R Inbuilt Data No need to install/load any package. datasets are already pre-installed. ?datasets() library(help = "datasets") ?swiss # give information on data set 'swiss' help(USArrests) # give information on data set 'USArrests' You will find some code that can be used to create graphs in the help files....
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random_project_pipeline_simple_linear_regression
Table of contents 1 Setting Up Working directory, clearing all data and memory 1.1 Installing Package (required once only), then loading package 2 Plotting Data 3 Fitting the linear regression model 4 A more interpretable intercept 5 Changing scale 6 Predicting the price of a diamond 7 Properties of the residuals 8 Non-linear data 9 Heteroskeda...
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import_dataset
1 Data Lets try to import data into R using the base command using data() command that loads specified data sets, or list the available data sets. 1.1 Time Series Data Longley’s Economic Regression Data - a macroeconomic data set which provides a well-known example for a highly collinear regression. A data frame with 7 economical variables, obse...
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