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Land-price dynamics and macroeconomic fluctuations with general household preferences

Published online by Cambridge University Press:  25 September 2023

Been-Lon Chen*
Affiliation:
Institute of Economics, Academia Sinica, Taipei, Taiwan
Zheng-Ze Lai
Affiliation:
Department of Economics, National Chengchi University, Taipei, Taiwan
Shian-Yu Liao
Affiliation:
Department of Economics, Fu Jen Catholic University, New Taipei City, Taiwan
*
Corresponding author: Been-Lon Chen; Email: bchen@econ.sinica.edu.tw

Abstract

Through the collateral channel for entrepreneurs, a positive housing demand shock in Liu et al. [(2013) Econometrica 81, 1147–1184.] increases land prices and business investment, but consumption decreases on impact and there is thus a comovement problem. This paper improves Liu et al. [(2013) Econometrica 81, 1147–1184.] by adding general household preferences with broader intratemporal and intertemporal substitutions Bayesian estimation of our structural model based on aggregate US data suggests that the intratemporal substitution is larger than unity and the intertemporal substitution is smaller than unity. Our impulse responses show that a positive housing demand shock increases land prices, business investment, and consumption, which resolves the comovement problem. Moreover, the strength of the collateral channel linking land prices and business investment in our Bayesian DSGE model is larger than that in Liu et al. [(2013) Econometrica 81, 1147–1184.]. Housing demand shocks explain 39−43% of the variance of output and 41−47% of the variance of investment in our model, but the same shocks explain only 17−31% of the variance of output and 30−41% of the variance of investment in Liu et al. [(2013) Econometrica 81, 1147–1184.]. Variance decomposition reveals that housing demand shocks account for a larger share of the fluctuations in land prices, investment, employment, and output than other shocks. Using the marginal data density as the measure of fit for models, we find that our model can better explain the same US aggregate data.

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Articles
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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