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The Journal of Real Estate Finance and Economics

Erschienen in:

01.04.2015

Time-Varying Effects of Housing and Stock Returns on U.S. Consumption

verfasst von: Beatrice D. Simo-Kengne, Stephen M. Miller, Rangan Gupta, Goodness C. Aye

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 3/2015

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Abstract

This paper applies a time-varying parameter vector autoregressive approach to estimate the relative effects of housing and stock returns on the growth rate of US consumption over time. We use annual data from 1890 to 2012 and find that at the 1- and 2-year horizons and over time, generally the housing return positively affects consumption growth while the stock return negatively affects it. For the 3- to 6-year horizons, the two return shocks generally exert a negative, but small, effect on consumption growth. These opposite responses to changes in housing and stock returns suggest different mechanisms through which wealth affects consumption. Further, the housing return effect generally increases after 1980. The sub-period from 1980 to 2012 includes the 1997/2002 asset price boom/bust where house prices continued to rise moderately as stock prices fell. These findings suggest that the magnitude of the relative return effects differs with both time and horizons and also depends on whether prices increase or decrease.

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This discussion in Ludwig and Sløk (2001) considers the effects of changes in stock and housing prices on consumption. Our empirical analysis examines the effects of house and stock returns, or the logarithmic differences in prices, on the growth rate of per capita real consumption.

See Nakajima (2011) and Primiceri (2005) for further details on the TVP-VAR methodology.

See Aye et al. (2013) for a detailed discussion in this regard.

http://www.econ.yale.edu/~shiller/data.htm. A referee suggested that the Shiller data may exhibit some reliability issues. As a robustness check, we also collected annual data on the Standard and Poor’s 500 and Dow Jones Industrial Average indexes as well as per capita consumption data from the Federal Reserve Bank of St. Louis FRED® database from 1929 to 2012, where all data are deflated by the CPI. We redid our analysis with this shorter annual sample and report the results in the text. The findings differ between the two different stock price measures.

We use standard unit-root tests, namely, Augmented Dickey and Fuller (1981), Phillips and Perron (1988) Dickey and Fuller with Generalised Least Squares detrending (DF-GLS), and the Ng and Perron (2001) modified version of the PP (NP-MZt) to confirm that the log-levels of the three variables under consideration are integrated of order 1, i.e., I (1). Given nonstationary data, we also conducted the Johansen (1988, 1991) tests of cointegration. Both the Trace and Maximum Eigenvalue tests, however, do not reject the null of no cointegration, which, in turn, implies that our VAR in first differences does not need to account for error correction, and hence, is not misspecified. The unit-root and cointegration tests are available on request from the authors.

We find that all roots of the constant parameter VAR lie within the unit circle, implying stability.

The MCMC method assesses the joint posterior distributions of the parameters of interest based on certain prior probability densities that are set in advance. This paper adopts the following priors as found in Nakajima (2011): Σ _β ∼ IW(25, 0.01I), (Σ _a) _i ^− 2 ∼ G(4, 0.02), (Σ _h) _i ^− 2 ∼ G(4, 0.02), where (Σ _a) _i ^− 2 and (Σ _h) _i ^− 2 are the i^th diagonal of elements of Σ _a and Σ _h, respectively. IW and G denote the inverse Wishart and the Gamma distributions, respectively. We use flat priors to set initial values of time-varying parameters such that: \( {\mu}_{\beta_0}={\mu}_{a_0}={\mu}_{h_0}=0 \) and \( {\varSigma}_{\beta_0}={\varSigma}_{a_0}={\varSigma}_{h_0}=10\times I. \)

Geweke (1992) suggests comparing the first n ₀ draws to the last n ₁ draws, dropping out the middle draws, to check for convergence in the Markov chain. We compute the CD statistics as follows: \( CD=\left({\overline{x}}_0-{\overline{x}}_1\right)/\sqrt{{\widehat{\sigma}}_0^2/{n}_0+{\widehat{\sigma}}_1^2/{n}_1} \), where \( {\overline{x}}_j=\left(1/{n}_j\right){\displaystyle {\sum}_{i={m}_j}^{m_j+{n}_j-1}{x}^{(i)}} \), x ⁽ⁱ⁾ is the i ^th draw, and \( {\widehat{\sigma}}_j^2/{n}_j \) is the standard error of \( {\overline{x}}_j \) respectively for j = 0, 1. If the sequence of the MCMC sampling is stationary, it converges to a standard normal distribution. We set m ₀ = 1, n ₀ = 10,000, m ₁ = 50,001, and n ₁ = 50,000. \( {\widehat{\sigma}}_j^2 \) is computed using a Prazen window with bandwidth (B _m) = 500. The inefficiency factor is defined as 1 + 2 \( {\displaystyle {\sum}_{s=1}^{B_m}{\rho}_s} \), where ρ _s is the sample autocorrelation at lag s and is computed to measure how well the MCMC chain mixes.

Case et al. (2005) and, more recently, Zhou and Carroll (2012) do document a significant positive housing wealth effect and an insignificant negative stock wealth effect in some of their econometric specifications. We consider the robustness of our findings in the next sub-section.

Aoki, K., Proudman, J., & Vlieghe, G. (2004). House Prices, Consumption and Monetary Policy: A Financial Accelerator Approach. Journal of Financial Intermediation, 13, 414–435.CrossRef

Aye, G. C., Gupta, R., Kaninda, A., Nyakabawo, W. V., & Razak, A. (2013). House Price, Stock Price and Consumption in South Africa: A Structural VAR Approach. Corporate Ownership and Control. http://web.up.ac.za/sitefiles/file/40/677/WP_2013_09.pdf.

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Brady, R., & Stimel, D. (2011). How the housing and financial wealth effects have changed over time. The B.E. Journal of Macroeconomics. 11, 1 (Topics), Article 28.

Carroll, C. D., Otsuka, M., & Slacalek, J. (2006). How large is the housing wealth effect? A new Approach. Working Paper No. 12746, National Bureau of Economic Research.

Case, K., Shiller, R., & Quigley, J. (2005). Comparing Wealth Effects: The Stock Market Versus the Housing Market. Advances in Macroeconomics, 5, 1–32.CrossRef

Dickey, D., & Fuller, W. (1981). Likelihood Ratio Statistics for Autoregressive Time Series With a Unit Root. Econometrica, 49, 1057–1072.CrossRef

Dvornak, N., & Kohler, M. (2007). Housing Wealth, Stock Market Wealth and Consumption: A Panel Analysis for Australia. The Economic Record, 83, 117–130.CrossRef

Geweke, J. (1992). Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. In J. M. Bernado, J. O. Berger, A. P. Dawid, & A. F. M. Smith (Eds.), Bayesian statistics (pp. 169–188). New York: Oxford University Press.

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Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59, 1551–1580.CrossRef

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Titel: Time-Varying Effects of Housing and Stock Returns on U.S. Consumption
verfasst von: Beatrice D. Simo-Kengne
Stephen M. Miller
Rangan Gupta
Goodness C. Aye
Publikationsdatum: 01.04.2015
Verlag: Springer US
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 3/2015
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-014-9470-3

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