Molin Zhong
Biographic Data
| ID | 8920282 |
|---|---|
| NAME | Molin Zhong |
| GIVEN NAMES | Molin |
| FAMILY NAME | Zhong |
| SIGNATURE | ZHONG M |
| AFFILIATIONS | Federal Reserve Board Washington D.C. USA |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Macroeconomic forecasting in times of crises
We propose a parsimonious semiparametric method for macroeconomic forecasting. Based on ideas of clustering and similarity, we partition the series into blocks, search for the closest blocks to the latest block of observations, and forecast with the matched blocks. In a real‐time forecasting exercise, we show that our approach does especially well for labor market and other key macro variables. Our method outperforms parametric linear, nonlinear,…
A New Approach to Identifying the Real Effects of Uncertainty Shocks
This article introduces the use of the sign restrictions methodology to identify uncertainty shocks. We apply our methodology to a class of vector autoregression models with stochastic volatility that allow volatility fluctuations to impact the conditional mean. We combine sign restrictions on the conditional mean and conditional second moment impulse responses to identify financial and macro uncertainty shocks. On U.S. data, we find stronger evi…
No prominent works on this page.
A New Approach to Identifying the Real Effects of Uncertainty Shocks
This article introduces the use of the sign restrictions methodology to identify uncertainty shocks. We apply our methodology to a class of vector autoregression models with stochastic volatility that allow volatility fluctuations to impact the conditional mean. We combine sign restrictions on the conditional mean and conditional second moment impulse responses to identify financial and macro uncertainty shocks. On U.S. data, we find stronger evi…
Macroeconomic forecasting in times of crises
We propose a parsimonious semiparametric method for macroeconomic forecasting. Based on ideas of clustering and similarity, we partition the series into blocks, search for the closest blocks to the latest block of observations, and forecast with the matched blocks. In a real‐time forecasting exercise, we show that our approach does especially well for labor market and other key macro variables. Our method outperforms parametric linear, nonlinear,…
Computer Science (2 works) · Econometrics (2 works) · Economics (2 works) · Macro (2 works) · Macroeconomics (2 works) · Market Dynamics and Volatility (2 works) · Mathematics (2 works) · Monetary Policy and Economic Impact (2 works) · Artificial Intelligence (1 work) · Cluster analysis (1 work)