This project introduces some of the basic Bayesian VAR models and provides a demonstration based on psychological data
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Updated
Jul 23, 2024
This project introduces some of the basic Bayesian VAR models and provides a demonstration based on psychological data
Codes for BVHAR Research
Wheat Forecasting using LSTM and VAR
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…
Analysis scripts and randomly generated data for Suicide and Life-Threatening Behavior paper: 'Identifying person-specific coping responses to suicidal urges: A case series analysis and illustration of the idiographic method'
Tanulmányomban az egy főre eső GDP és munkanélküliség teljes termékenységi arányszámra gyakorolt hatását elemzem. A választott eszközök között szerepel az Engel-Granger kointegrációs teszt, amellyel megerősítettem a hipotézist, hogy szomszédos országok termékenységi rátájának alakulása általában nagyobb egyezőséget mutat, melynek magyarázata leh…
Python software & data for analyzing the City of Philadelphia's Five Year Plan
Codebase for Term Paper of course ECON F244: Economics of Growth and Development at BITS Pilani, Pilani campus (Spring '21)
This repository contains a research paper I completed for my Time Series Econometrics class.
Project on Foreign Exchange Forecasting, for the Μ401 - Deep Neural Networks course, NKUA, Fall 2022.
An R package for Bayesian Estimation of Structural Vector Autoregressive Models
VAR and Local Projections
Manipulation of time series data and forecast CAD/USD currency using commodities
Utilized sentiment-based features to predict cryptocurrency returns, models used: Random Forest Classifier, Random Forest Regressor, and VAR time-series model
Forecasting with VAR
Time Series Final Project
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.
Forecasting the stock market is difficult. I sought to observe the relationship between Apple's stock price and others in the S&P500. In doing this, I was able to conclude that stocks in the tech industry can help predict a trend in Apple's Percent change.
Interactive Notebook demonstrating the R-library bigtime
Forecasting the stock market is difficult. I sought to observe the relationship between Apple's stock price and others in the S&P500. In doing this, I was able to conclude that stocks in the tech industry can help predict a trend in Apple's Percent change.
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