An econometrics vector autoregression model (VAR) for analysis of multivariate time series of macroeconomics phenomena. Python Jupyter notebook based model is presented here although other packages like R statistical programming language with R Studio could also be used.

Multivariate time series Vector Autoregression Model (VAR) on real world GDP and DPI (and some other indexes). Bayesian Structured Time Series (BSTS).

I investigate the Asymmetric Volatility Spillover Effects within and across six major International stock markets. United States, Canada, France, Germany, Italy & Japan

Implementation of High-dimensional vector autoregression time series modeling via tensor decomposition, Di Wang, Yao Zheng, Heng Lian, Guodong Li. Written in JAX.

Applied paper using VAR and VEC models on the Nielsen cookies dataset. This was the final assignment for the Advanced Econometrics course at the São Paulo School of Economics, Getulio Vargas Foundation, with a focus on time series analysis.

Time Series Analysis of Macro Economic Parameters using Vector Auto Regression Model

Current repository depicts R usability for time series modeling. Number of scripts represents preprocessing time series, modeling AR, MA, ARIMA with seasonality, ARCH, GARCH, VAR, VECM including statistical testing process and robust check.

GitHub repo for wealth and inequality modeling using VAR models as part of the Econometric Projects course at HU Berlin

Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters.

Masters Thesis Project on Media Prejudice

Forecasting superstore sales using advanced time series models in R.

Time series preprocessing. (G)ARCH, VECM, VAR modeling on stock data.

Investment Analysis and Asset Mgmt, Time Series Analysis & Forecasting, Machine Learning in Finance & Causal Inference Methods

An R package for Bayesian Estimation of Structural Vector Autoregressive Models

A Novel Methodology of Domain Wise feature selection approach which is capable of identifying the interrelationships by focusing on Domain-Wise feature selection. It ensures that correlated and similar features are considered together by grouping them in similar domains based on correlation values

Forecasting monthly sales witNh LSTM, Forecasting with RNN, Vector-autoregressive model

Predicting the Air Quality Index of 100+ counties across the USA

ML Estimation for Discrete Multivariate Vasicek Processes