Comparing the traditional sVAR model with a modified two-step msVAR model

The vector autoregressive (VAR) model has been used in multivariate time series. However, this model comes with drawbacks such as noisy estimates and unstable predictions. So, to combat these issues, the structural vector autoregressive (sVAR) model was introduced. The sVAR model addresses these drawbacks by introducing structural assumptions. We assessed the efficiency of the VAR and sVAR models by analyzing the speed and accuracy of both models.

Aksheytha Chelikavada

Aksheytha Chelikavada (she/her) is a senior majoring in data science and mathematics. She is deeply involved as a research assistant for Dr. Hugo Panzo, focusing on probability theory, combinatorics, computer science, and their applications. In her free time, Aksheytha enjoys being outdoors, playing Assassin’s Creed, and eating chocolate!

Henry Koeller

Henry is a third-year senior, majoring in data science and math with a computer science minor. He was born in St. Louis and has lived there his whole life. His future plans involve another semester of classes prior to graduating in December 2024.

Nithi Vickraman

Nithi Vickraman is a senior from St. Louis, Missouri studying data science and mathematics. At SLU, she writes for Her Campus, works with the Madrid Peer Mentor Program, and is involved in the business fraternity- Alpha Kappa Psi. Outside of school and work, she enjoys traveling, trying new restaurants, and reading horror novels. After graduation, Nithi plans to work in St. Louis and pursue a Master of Arts in Teaching part time.

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