Comprehensive tutorial notes for ETC2410 Introductory Econometrics

Sketching of Data via Random Subspace Embeddings

Code to reproduce paper Adrian, Duarte and Iyer (2023), “The Market Price of Risk and Macro-Financial Dynamics”

R Code for Bayesian Inference for Structural Vector Autoregressions Identified with Markov-Switching Heteroskedasticity

Detailed implementation of various regression analysis models and concepts on real dataset.

GWAS of trait variance (C++)

R package to perform regression-based Brown-Forsythe test

Impact of macroecomonic variables on S&P 500

As part of this project, we have used Regression Analysis on top of a panel data on Guns in USA to determine the "Effect of Shall-Carry Law on Violence Rate and Incarceration Rate in United States".

An R package for time series modelling with mixture autoregressive and related models.

Repo where different methods for price regression are used (supervised machine learning)

The purpose of my application was to solve a problem many businesses (small businesses in particular) face. They do not know how much to produce, where to price, how much to spend on advertising and many other questions. Eden’s purpose was to answer these questions for them easily and with no technical acumen required by the user. Eden would model supply and demand equations using ordinary least squares (OLS) regression on the user’s data to form the best fitting supply and demand equations possible. The best fit was to be ensured by regressing each variable against demand or supply, determine the best shape via the highest adjusted R2, and then do an OLS regression and simplistically tell the user what the results mean. Eden would attempt multiple shapes like linear, logarithmic, cubic, quadratic, and inverse. The user interface would be easy to navigate and user-friendly.

Diagnostic tools for regression modeling. Julia-equivalent for diagnoser (https://github.com/robertschnitman/diagnoser).

Diagnostic tools for regression modeling.

Linear Multilinear and Logistic Regression in Machine Learning

Script used for my undergraduate thesis

OLS regression with possibility of controlling for fixed effects and robust standard errors

No description provided.

Basic methodologies of Empirical Research applied on various case studies (R language)

Econometrics_regression analysis using R language

Testing different models for the linear regression model with one estimator and heteroskedacity in data

Here I have checked and removed for heteroskedasticity .

Supplementary materials for the manuscript "Latent-class trajectory modeling with a heterogeneous mean-variance relation" by N. G. P. Den Teuling, F. Ungolo, S.C. Pauws, and E.R. van den Heuvel

Presentación de la Comunicación Oral del LII Coloquio Argentino de Estadística de la Sociedad Argentina de Estadística

Full Log-Likelihood Heteroskedastic Regression with Deep Neural Networks and Tensorflow