Publications by Arvind Sharma
HW3_template
1 Setup 1.0.1 Setup - empty variables and functions in the environment tab/window # Clear the workspace rm(list = ls()) # Clear environment gc() # Clear unused memory ## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb) ## Ncells 531196 28.4 1183070 63.2 NA 669265 35.8 ## Vcells 974736 7.5 8388608 64.0 ...
1169 sym R (14333 sym/62 pcs)
IV_JEC_example
Please try reading the paper below: https://www.jasandford.com/porter.pdf Porter, Robert H. “A Study of Cartel Stability: The Joint Executive Committee, 1880-1886.” The Bell Journal of Economics, vol. 14, no. 2, 1983, pp. 301–14. JSTOR, https://doi.org/10.2307/3003634. Accessed 29 Apr. 2023. Essentials of Econometrics: Empirical Exercise...
12212 sym R (23835 sym/40 pcs) 1 img 1 tbl
MLE
1 OLS is a special case of MLE Ordinary Least Squares (OLS) can be viewed as a special case of Maximum Likelihood Estimation (MLE) under certain assumptions. The relationship between OLS and MLE is particularly evident in the context of the simple linear regression model. In a simple linear regression model with a normally distributed error term, t...
7836 sym R (1914 sym/14 pcs) 1 img
Count_Regressions_Implementation
1 Data We use the data set warpbreaks. This data set gives the number of warp breaks per loom, where a loom corresponds to a fixed length of yarn. remove(list=ls()) ?warpbreaks df <- warpbreaks library(visdat) vis_dat(df) head(df) ## breaks wool tension ## 1 26 A L ## 2 30 A L ## 3 54 A L ## 4 25 ...
5791 sym R (10857 sym/47 pcs) 6 img
HW2_suggested_solution
1 Setup 1.1 Empty variables and functions in the environment tab/window First, empty the environment so that we can uplaod the clean data. # Clear the workspace rm(list = ls()) # Clear environment-remove all files from your workspace gc() # Clear unused memory ## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb) ## Ncells...
14623 sym R (64733 sym/72 pcs) 18 img
Lasso_Ridge_LM
Table of contents Overview Comparison of OLS, Lasso and Ridge Set Up Data Independent Variables Standardization: Scaling for Zeros and Ones Normalization: Mapping to a Common Range The decision to standardize or normalize depends on the characteristics of the dataset and the requirements of the chosen machine learning algorithm. Here are som...
11728 sym Python (15525 sym/48 pcs) 1 img
cross_validation
Table of contents Cross-Validation Cross_Validation Cross-Validation - LOOV - h step forward...
113 sym
step_AIC
1 Setup # Set working directory and path to data cd <- "/Users/arvindsharma/Dropbox/WCAS/Econometrics/" setwd(cd) # Clear the workspace rm(list = ls()) # Clear environment gc() # Clear unused memory ## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb) ## Ncells 551441 29.5 1225424 65.5 NA 700242 37.4 ## Vc...
160 sym R (25627 sym/23 pcs)
PCA
Table of contents Setup Empty variables and functions in the environment tab/window Load packages Load raw data Missing Observations Correlation Plot Graphs PCA (prcomp) Batting Step 1: Load the Data Step 2: Preprocess Your Data: Step 3: Perform PCA Step 4: Explore PCA Results: Graph of Variables Alternative Commands PCA (princomp) PCA (Fac...
9296 sym 9 img
RDD
Table of contents 1. Introduction 2. Theory 2.1 Estimation methods 2.2 Advantage of RDD 3. Replication 3.1 The dataset 3.2 Estimation: same slopes Model Model via rddtools Scatterplot 3.3 Estimation: different slopes Model Model via rddtools Scatterplot 3.4 Modifying the functional form Model Model via rddtools Scatterplot 3.5 Sensitivi...
7432 sym Python (13329 sym/63 pcs) 5 img