University of Leeds Featured PhD Programmes
University of Liverpool Featured PhD Programmes
University of Glasgow Featured PhD Programmes
Catalysis Hub Featured PhD Programmes
University of Bristol Featured PhD Programmes

Housing Market Spillovers

  • Full or part time
    Dr C Liu
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

The housing market has been argued as the main driving force of the 2008-2009 financial crisis. In U.S., the past decade initially saw a rapid growth in housing prices and residential investment, followed by the collapse of housing market and frozen credit market. This led many economists to raise the issue that housing market is not only a passive reaction of other economic and financial activities but the causing factor of business fluctuations. Therefore, we wish to expand this study further by undertaking empirical research, firstly by modelling housing market either in a dynamic stochastic general equilibrium (DSGE) framework or a reduced form VAR model. Secondly, we will examine the sources and consequences of housing market

The earlier literature attempts to incorporate the housing market are Iacoviello (2005) and Iacoviello and Neri (2010). Building a dynamic stochastic general equilibrium (DSGE) model, they found that housing market spillovers cannot be ignored. Over the business cycle, housing demand andhousing technology shocks explain one-quarter each of the volatility of housing market. Similar versions of this model have been used in different central banks and economic organisations, such as Riksbank (Sellin and Walentin, 2015), European Commission (Roeger and in ’t Veld, 2009), Bank of Canada (Christensen, et al., 2009), and IMF (Kannan, Rabanal and Scott, 2009).

Proposed Methods

The method can be two directions. If we focus on the reduced form model, we need to build a VAR model based on the DSGE framework of Iacoviello (2005). The impulse response functions can explain the housing market responses after a shock. The variance decomposition allows us to see how much each shock contributes to the volatility of the housing market. If we need to build a DSGE framework, we can use Bayesian estimation method. All the analysis will be based on these estimation results.

Funding Notes

This is a self-funded PhD opportunity.

References

Iacoviello, M., and Neri, S., 2010. Housing Market Spillovers: Evidence from an Estimated DSGE Model. American Economic Journal: Macroeconomics, 2(2), 125-64.
Iacoviello, M., 2005. House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle. American Economic Review, 95(3): 739-764.
Sterk, V., 2015. Home Equity, Mobility, and Macroeconomic Fluctuations, Journal of Monetary Economics, 74, 16-32.
Roeger, W., and in ’t Veld, J., 2009. Fiscal Policy with Credit Constrained Households, European Commission, DG ECFIN, Working paper.
Christensen, I., et al., 2009. Consumption, Housing Collateral, and the Canadian Business Cycle, Bank of Canada, Working Papers, 09-26.
Kannan, et al., 2009. Monetary and Macroprudential Policy Rules in a Model with House Price Booms, IMF, Working paper.

Related Subjects

How good is research at Nottingham Trent University in Business and Management Studies?

FTE Category A staff submitted: 23.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully





FindAPhD. Copyright 2005-2019
All rights reserved.