Basic methodologies of Empirical Research applied on various case studies (R language)
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Updated
Apr 5, 2024
Basic methodologies of Empirical Research applied on various case studies (R language)
Detailed implementation of various regression analysis models and concepts on real dataset.
Code to reproduce paper Adrian, Duarte and Iyer (2023), “The Market Price of Risk and Macro-Financial Dynamics”
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
Linear Multilinear and Logistic Regression in Machine Learning
OLS regression with possibility of controlling for fixed effects and robust standard errors
Impact of macroecomonic variables on S&P 500
Here I have checked and removed for heteroskedasticity .
R package to perform regression-based Brown-Forsythe test
GWAS of trait variance (C++)
Sketching of Data via Random Subspace Embeddings
R Code for Bayesian Inference for Structural Vector Autoregressions Identified with Markov-Switching Heteroskedasticity
Diagnostic tools for regression modeling.
Script used for my undergraduate thesis
An R package for time series modelling with mixture autoregressive and related models.
Full Log-Likelihood Heteroskedastic Regression with Deep Neural Networks and Tensorflow
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 mod…
Econometrics_regression analysis using R language
Repo where different methods for price regression are used (supervised machine learning)
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