Financial intermediation and real estate prices impact on business cycles: A Bayesian analysis |
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| Affiliation: | 1. Universitat Rovira i Virgili, Department of Business, Av. Universitat 1, 43204 Reus, Spain;2. Universidad del Pacífico, Lima, Peru;3. Departamento de Informática en Salud, Hospital Italiano de Buenos Aires & CONICET, C1199ABB Ciudad Autónoma de Buenos Aires, Argentina;4. Instituto de Física, Universidade Federal de Alagoas, Av. Lourival Melo Mota, s/n, 57072-970 Maceió, AL, Brazil;1. School of Business & Economics, Loughborough University, Leicestershire LE11 3TU, United Kingdom;2. Department of Economics, School of Management & Social Sciences Pan-Atlantic University, Lagos, Nigeria;1. Marmara University, Faculty of Business Administration, Business Informatics, Göztepe Campus, Fahrettin Kerim Gökay Caddesi, Kadıköy, İstanbul, Turkey;2. Istanbul Technical University, Department of Economics, Maçka, Istanbul, Turkey;1. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;2. Faculty of Business Administration, Ton Duc Thang University, Ho Chi Minh City, Viet Nam;3. Centre for Econometric & Allied Research, University of Ibadan, Nigeria;4. Department of Economics, Obafemi Awolowo University, Ile Ife, Nigeria;5. Ghana Institute of Management and Public Administration (GIMPA) Business School, Ghana |
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| Abstract: | How do financial intermediation and real estate prices impinge on the business cycle? I develop a two-sector stochastic general equilibrium model with financial intermediation and real estate collateral to assess the impact of financial conditions and land prices on aggregate fluctuations. I estimate the model with Bayesian methods using a novel data set that includes U.S. macro and financial variables during the period 1975–2010. The results from the estimated model show that financial conditions have a sizable effect on the variability of investment spending, while productivity shocks are the main source of consumption fluctuations. Specifically, on the macro side, (1) financial shocks explain about three quarters of investment spending variability and one third of the variance in hours worked. On the financial side, (2) financial shocks explain most of the variability in land prices, credit spread, and aggregate net worth of the financial sector. The model also accounts for observed unconditional moments of macro and financial variables. Our quantitative results are suggestive of the impact of diverse sources of financial instability, and as such relevant for macro prudential policy analysis. |
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| Keywords: | Financial frictions Banking Net worth Leverage Credit spread Land prices Business cycles Bayesian estimation |
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