Estimating U.S. Housing Price Network Connectedness: Evidence from Dynamic Elastic Net, Lasso, and Ridge Vector Autoregressive Models

David Gabauer, Rangan Gupta, Hardik Marfatia and Stephen Miller

No 2020-08, Working papers from University of Connecticut, Department of Economics

Abstract: This paper investigates the dynamic connectedness of random shocks to housing prices between the 50 U.S. states and the District of Columbia. The paper implements a standard vector autoregressive (VAR) model as well as three VAR models with shrinkage effects – Elastic Net, Lasso, and Ridge VAR models. The transmission of random shocks on a regional basis flows from Southern states to Western states to Midwestern states to Northeastern states. Since VAR models generally confront parameter values between zero and one, the Elastic Net and Lasso VAR models perform the best since the penalty involves the absolute value rather than he squared value as in the Ridge VAR model. Our results have important implications for investors and policymakers.

Keywords: Dynamic Connectedness; Elastic Net VAR; Lasso VAR; Ridge VAR; U.S. Housing (search for similar items in EconPapers)
JEL-codes: C32 C52 R31 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2020-08
New Economics Papers: this item is included in nep-ore and nep-ure
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Citations: View citations in EconPapers (13)

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Related works:
Journal Article: Estimating U.S. housing price network connectedness: Evidence from dynamic Elastic Net, Lasso, and ridge vector autoregressive models (2024)
Working Paper: Estimating U.S. Housing Price Network Connectedness: Evidence from Dynamic Elastic Net, Lasso, and Ridge Vector Autoregressive Models (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:uct:uconnp:2020-08

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