SMU SOE LKC Seminar in Econometrics (February 8, 2023): We Modeled Long Memory with Just One Lag!

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ABSTRACT We build on two contributions that have found conditions for large dimensional networks or systems to generate long memory in their individual components, and provide a multivariate methodology for modeling and forecasting series displaying long range dependence. We model long memory properties within a vector autoregressive system of order 1 and consider Bayesian estimation or ridge regression. For these, we derive a theory-driven parametric setting that informs a prior distribution or a shrinkage target. Our proposal significantly outperforms univariate time-series long-memory models when forecasting a daily volatility measure for 250 U.S. company stocks over twelve years. This provides an empirical validation of the theoretical results showing long memory can be sourced to marginalization within a large dimensional system. Keywords: Bayesian estimation, Ridge regression, Vector autoregressive model, Forecasting. JEL Codes: C10, C32, C58. Click here to view the CV. Click here to view the paper.
		PRESENTER Guillaume Chevillon ESSEC
		RESEARCH FIELDS Econometrics Empirical Macroeconomics Forecasting
		DATE: 8 February 2023 (Wednesday)
		TIME: 9.30am - 11am
		VENUE: Interactive Learning Room School of Economics Singapore Management University 90 Stamford Road Singapore 178903