Arvai, Z, Krznar, I, and Y Ustyugova (2017). “Macroprudential Tools at Work in Canada”, Selected Issues Papers: Canada, International Monetary Fund.
Angelini, P, Neri, S, and F Panetta (2014). “The Interaction between Capital Requirements and Monetary Policy,” Journal of Money, Credit and Banking, 46(6), 1075-1112.
Funke, M and M. Paetz (2018). “Dynamic Stochastic General Equilibrium-Based Assessment of Non-Linear Macroprudential Policies: Evidence from Hong Kong,” Pacific Economic Review. Forthcoming.
Funke, M, Kirkby, R, and P Mihaylovski. (2017). “House Prices and Macroprudential Policy in an Estimated DSGE Model of New Zealand,” Working Papers in Economics and Finance, Victoria University, 9/2017.
Gali, J (2003). New Perspectives on Monetary Policy, Inflation, and the Business Cycle, In: Dewatripont, M, Hansen L, and S Turnovsky (Eds), Advances in Economic Theory, vol. III. University Press, Cambridge, 151-197.
Woodford (2003), Interest and Prices Foundations of a Theory of Monetary Policy, Princeton University Press, Princeton, New Jersey.
See, for example, 14th Annual Demographia International Housing Affordability Survey (third quarter, 2017).
The shock captures changes in social and institutional norms that shift preferences toward housing
Note, the optimality condition can be interpreted as equating the marginal rate of substitution between housing and non-housing consumption to the user cost of housing.
The superscripts for borrowers and savers have been dropped because all arguments hold for borrowers, savers, and aggregates.
For simplicity, ϵ is assumed to be the same in each sector.
The Metropolis-Hastings algorithm is used to draw 500,000 sets of parameters from the posterior distribution. The first half of the draws is discarded to ensure convergence.
The standard deviations in historical data are estimated using a vector-autoregressive model (VAR) that uses the same sample period and data as the DSGE model. The VAR includes 4 lags and is simulated 1000 times using bootstrapping methods. In the chart, the uncertainty around the model and data estimates reflects both parameter and shock uncertainty.
See, for example, Angelini and others (2014). Optimized simple policy rules have also been examined and the results are qualitatively very similar to those found in the context of optimal rules. These results are available from the author on request.
Other loss is simply the weighted average of the macroprudential and tax authorities’ loss functions.