CV - Job Market Paper  
 

Zhang, Donghai

Job market candidate

Contact information

Tel. +34 93 542 2672

donghai.zhang@upf.edu

 

Personal Web Page:

http://www.donghaizhang.com

 

Available for Interviews at :

Simposio de la Asociación Española de Economía (SAEe), December 14-16, Barcelona, Spain

Allied Social Science Associations (ASSA), January 5-7, Philadelphia, US

 

 

Research interests

Primary Fields: Macroeconomics, Monetary Economics, Time Series Econometrics.

Secondary Fields: Financial Economics, Asset Pricing

Placement officer

Filippo Ippolito
filippo.ippolito@upf.edu
 

References

Davide Debortoli (Advisor)
davide.debortoli@upf.edu

Jordi Galí
jgalí@crei.cat

Barbara Rossi (Advisor)
barbara.rossi@upf.edu

 

Research

"Term Structure, Forecast Revision and the Information Channel of Monetary Policy"(Job Market Paper)
Monetary policy shocks affect interest rates at long horizons (10 years or more). Furthermore, the private sector’s real GDP forecasts are revised upward in response to a monetary tightening. These facts challenge the prevailing theories in academic and policy circles, which are based on the paradigm that monetary policy has limited long-run effects and a monetary policy tightening should depress agents’ beliefs. In this paper, I propose a micro-founded model to rationalize those facts, based on the information channel of monetary policy. I consider a framework where the central bank has private information about future economic conditions. Agents update their beliefs according to the Bayes' rule. Policy actions play a signaling role, and may thus have an impact on both short and long- term interest rates. Moreover I provide novel facts that the aforementioned responses are stronger when monetary shocks are expansionary. An extension of the model with ambiguity averse agent and ambiguous signals rationalizes such an asymmetry. Finally, I discuss the implications of information friction for the design of optimal simple rule.

"The Random Walk Beats Professional Forecast: Facts, Puzzles and Explanations”
For short horizon exchange rate predictability the simple random walk model outperforms professional forecasts. A new puzzle arises: why do professional forecasters not adopt the simple random walk model to provide a more accurate estimate? This paper provides an explanation. In this framework, the forecaster faces model uncertainty and she reports the forecast that minimizes the forecast error under the worst-case scenario. Therefore, professional forecasters provide suboptimal forecast intentionally. Estimation results show that the model matches the empirical puzzle and in addition, the model predicts that the forecaster under-reacts to current news substantially for exchange rate predictability. The latter is consistent with empirical facts provided in this paper. Moreover, the null of "rationality" is rejected using simulated data confirming existing findings, even though forecasters in the model perform optimally.

 

Research in Progress

“The Time Varying Effect of Unconventional Monetary Policy” with Francesca Loria, Carlos Montes Galdón and Shengliang Ou
We investigate whether forward guidance has a time-varying effect on economic fundamentals, and whether forward guidance shocks at different horizons have different impacts on economic activities. First, we argue that high-frequency identified monetary policy surprises are contaminated by asymmetric information between the central bank and the private sector. Second, we propose a strategy to overcome this issue. Third, we study the time-varying effect of forward guidance shocks using a time-varying coefficients and the stochastic volatility VARX model, in which our cleaned measure is integrated as an exogenous variable. Finally, within the context of a particular application, we show how to impose sign restrictions on the impact impulse responses to exogenous variables by using a constrained Kalman filter in the estimation of our empirical model.

 

“Disentangling the Source of Information Rigidity”, preliminary draft available upon request.
Professional forecasters face model uncertainty and therefore resort to reporting the forecast that minimizes the forecast error under the worst-case scenario. In such a framework, the degree of information rigidity estimated by Coibion and Gorodnichenko (AER, 2015) can be decomposed into: first the part originating from information friction and second the part arising from model uncertainty. Estimation results support the presence of model uncertainty. I provide a new estimate of Kalman gain purely originating from information friction, which is more suitable for the calibration of standard information friction models.

 

“Optimal Monetary Instruments for an Uncertain World” with Isaac Baley
We develop a multi-sector model in which firms have less information about fundamentals than the central bank. In this economy, we ask: Which measure of inflation should the central bank target to maximize welfare, and should it be made public? Should the central bank disclose fundamental shocks? We discover that the interaction between information frictions and cross-sector heterogeneity generates interesting policy trade-offs between transparency and efficiency.