Presentation slides available on SLDS Google Drive: https://drive.google.com/file/d/1MGTx...

American Statistical Association (ASA), Section on Statistical Learning and Data Science (SLDS)
January webinar: Statistical models for mixed frequency data and their applications in forecasting economic indicators

Record: January 26, 2023

Presenter: George Michailidis did his undergraduate training in economics at the University of Athens, Greece and his graduate training in mathematics at UCLA. He is a Fellow of ASA, IMS and ISI. He served as Editor-in-Chief of the Electronic Journal of Statistics and as Associate Editor of many statistical journals including JASA,JCGS, Technometrics, J of Nonparametric Statistics. His research interests are in high-dimensional statistics, change point analysis, large scale networks, machine learning, stochastic control and their applications to biomedical, engineering and financial data.

Abstract: In this talk, we discuss the problem of modeling and analysis of time series data that evolve at different frequencies (e.g., quarterly-monthly). Initially, we focus on forecasting a single variable measured at a low frequency based on a regression model that includes past lags of the response variable and other high and low frequency predictors and their lagged valued. We first provide a brief survey of available approaches in the literature and subsequently introduce the Bayesian Nested Lasso (BNL) that leads to principled selection of the lag of the predictors, reduces the effective number of model parameters through sparsity induced by the lasso component and finally incorporates desirable decay patterns over time lags in the magnitude of the corresponding regression coefficients. Further, it is easy to obtain samples from the posterior distribution due to the closed form expressions for the conditional distributions of the model parameters. Theoretical properties of the method are established and numerical results obtained from synthetic and macroeconomic data illustrate the good performance of the proposed Bayesian framework in parameter selection and estimation, and in the key task of GDP forecasting.

Subsequently, we briefly present multivariate time series models aiming to forecast a set of low frequency variables leveraging their past lags, as well as past lags of other time series sampled at higher frequency. We discuss suitable models and illustrate their performance in forecasting tasks of key macroeconomic indicators.

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