Kshitij Khare - Colloquium Speaker

Associate Professor, Department of Statistics, University of Florida
Thursday, September 30, 2021 - 3:15pm
Colloquium Title: 
Bayesian Inference in High-Dimensional Mixed Frequency Regression and VAR Models


Technological advancements in recent years have enabled organizations to collect, organize, store and analyze very large amounts of data from variables that are available at different temporal frequencies, e.g., monthly, weekly, daily. Such data is commonly referred to as mixed frequency time series data. In the first part of the talk, we will focus on mixed frequency regression, where the response variable and the covariates are available at different frequencies (for example, quarterly vs. monthly). We will present novel Bayesian methodology for (sparse) estimation of the regression coefficients and of the (autoregressive) lag length using a nested spike-and-slab framework. In the second part, we will focus on mixed frequency vector autoregressive (VAR) models, which aim to capture linear temporal interdependencies among multiple time series observed at different frequencies. The issue of overparameterization in a VAR model becomes more acute in high-dimensional settings where the number of variables is more than or comparable to the sample size. We present a Bayesian approach which achieves parameter reduction through a combination of sparsity and simple algebraic relationships between appropriate parameters. We will illustrate the efficacy of the proposed approach on simulated data and on real data from macroeconomics, and establish posterior consistency under high-dimensional scaling where the dimension of the VAR system grows with the sample size. The talk is based on joint work with Nilanjana Chakraborty, Satyajit Ghosh and George Michailidis.


Topic: Colloquia: Department of Statistics and Actuarial Science, The University of Iowa

Time: September 30, 2021 03:15 PM Central Time (US and Canada)

Join Zoom Meeting


Meeting ID: 989 2869 3758

One tap mobile

+13126266799,,98928693758# US (Chicago)

+16468769923,,98928693758# US (New York)

Dial by your location

        +1 312 626 6799 US (Chicago)

        +1 646 876 9923 US (New York)

        +1 301 715 8592 US (Washington DC)

        +1 346 248 7799 US (Houston)

        +1 669 900 6833 US (San Jose)

        +1 253 215 8782 US (Tacoma)

Meeting ID: 989 2869 3758

Find your local number: https://uiowa.zoom.us/u/adodl1V2PF

Join by SIP


Join by H.323 (US West) (US East) (India Mumbai) (India Hyderabad) (Amsterdam Netherlands) (Germany) (Australia Sydney) (Australia Melbourne) (Brazil) (Canada Toronto) (Canada Vancouver) (Japan Tokyo) (Japan Osaka)

Meeting ID: 989 2869 3758