Dunpei Gan
Texas A&M University
Department of Economics
Job Market Paper
This article challenges the conventional wisdom that government spending is effective and labor tax cuts are not effective, when interest rate is at lower bound (LB). I use a dynamic stochastic general equilibrium (DSGE) New Keynesian model with labor search and matching frictions to evaluate the effectiveness of fiscal policy. Two cases of monetary policy are considered: the non-binding situation when the interest rate can be set freely; and the lower-bound situation when the interest rate is fixed. Labor search and matching frictions generate a steep aggregate supply curve, which changes the comparative curvature to the aggregate demand curve at the lower bound. The fiscal policy multipliers are strikingly different compared to Eggertsson (2011) in the lower-bound scenario. Labor tax cuts are expansionary rather than contractionary, while government spending is not effective. The policy implication is that firms' supply side factors should be considered when conducting fiscal policy.
Working Paper
After the Financial Crisis in 2008, the Gross Domestic Product (GDP) of the United States recovered only slowly to its pre-crisis level. There are two notable phenomena after the Great Recession: the fall in the labor force participation rate and the decline of the growth rate of Total Factor Productivity (TFP). I build a Dynamic Stochastic General Equilibrium (DSGE) model that includes endogenous growth and dynamic labor market components (including labor force participation), the first of its kind in the literature. A Bayesian estimation is applied to this model. I find four shocks (a monetary policy shock, a government spending shock, a financial related shock, and a labor productivity shock) can explain most of the variation in GDP that occurred after the financial crisis.
I evaluate and backtest commonly used Value-at-Risk(VaR) methods in practice, for three portfolios of assets: equities, bonds and currencies. Specifically, I compute one-day-ahead forecasts for the time period from 2001 to 2018, and compare them to the realized daily profits and losses of these portfolios. Portfolio-based methods like Variance-Covariance produce very conservative estimates in general. The performances of VaR methods are related to the VaR confidence interval, underlying portfolio and the sample periods. The underlying properties of the distribution of a portfolio should be taken into consideration when choosing the VaR method.