Browsing by Author "Sun, Xiaojin"

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    Essays on Housing Markets and Monetary Policy
    Sun, Xiaojin (Virginia Tech, 2015-06-01)
    This dissertation consists of three essays on housing markets and monetary policy. The first essay focuses on the impact of monetary policy on U.S. local housing markets and finds that monetary policy has uneven impacts on local housing markets, and that the magnitude of the impacts are correlated with housing supply regulations. The second essay studies the optimal interest rate rule in a DSGE model with housing market spillovers and finds that the optimal interest rate rule responds to house price inflation even when the stabilization of house price is not among the objectives of the policymaker. The third essay is the core of this dissertation. I construct a dynamic stochastic general equilibrium (DSGE) model in this paper to study the fluctuations in the U.S. housing markets. The model features a market for newly built houses, a secondary market for old houses, and an endogenous term structure of nominal interest rates. Negative technological progress in the housing sector explains the upward trend in house prices over the past four decades. Housing preference and technology innovations explain about 80% of the volatility of housing investment, real price of new houses, and the old-to-new house price ratio. Monetary factors explain about 15% of the volatility of housing investment, but do not significantly contribute to the price fluctuations of either new or old houses. The preference innovation to old houses is the leading determinant of the run-up in the price of old houses relative to the price of new houses during the 10-year period before the Great Recession. The term structure is endogenous in this paper, and the intertemporal preference innovation makes a non-negligible contribution to the variations in nominal interest rates. Housing market conditions do not contribute much to the fluctuations of interest rates, but significantly affect the shape of the yield curve.
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    Subset-Continuous-Updating GMM Estimators for Dynamic Panel Data Models
    Ashley, Richard A.; Sun, Xiaojin (MDPI, 2016-11-30)
    The two-step GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) for dynamic panel data models have been widely used in empirical work; however, neither of them performs well in small samples with weak instruments. The continuous-updating GMM estimator proposed by Hansen, Heaton, and Yaron (1996) is in principle able to reduce the small-sample bias, but it involves high-dimensional optimizations when the number of regressors is large. This paper proposes a computationally feasible variation on these standard two-step GMM estimators by applying the idea of continuous-updating to the autoregressive parameter only, given the fact that the absolute value of the autoregressive parameter is less than unity as a necessary requirement for the data-generating process to be stationary. We show that our subset-continuous-updating method does not alter the asymptotic distribution of the two-step GMM estimators, and it therefore retains consistency. Our simulation results indicate that the subset-continuous-updating GMM estimators outperform their standard two-step counterparts in finite samples in terms of the estimation accuracy on the autoregressive parameter and the size of the Sargan-Hansen test.