Zhenjiang Lin
Biographic Data
| ID | 8920050 |
|---|---|
| NAME | Zhenjiang Lin |
| GIVEN NAMES | Zhenjiang |
| FAMILY NAME | Lin |
| SIGNATURE | LIN Z |
| AFFILIATIONS | School of Economics University of Nottingham Ningbo China Ningbo Zhejiang China |
| ORCID | 0000-0002-1224-9327 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Finite‐Sample Identification‐Robust Inference for Nonlinear DSGE Models
We develop identification‐robust likelihood‐free simultaneous confidence sets for dynamic stochastic general equilibrium models, without relying on linear approximations. Our methodology integrates simulation estimation methods using auxiliary statistics with Monte Carlo test principles. Results cover deep parameters and impulse responses. Auxiliary statistics include coefficients of linear and nonlinear vector autoregressions and local projectio…
Identification-Robust Inference With Simulation-Based Pseudo-Matching
We develop a general simulation-based inference procedure for partially specified models. Our procedure is based on matching auxiliary statistics to simulated counterparts where nuisance parameters are calibrated neither assuming identification of parameters of interest nor a one-to-one binding function. The conditions underlying the asymptotic validity of our (pseudo-)simulators in conjunction with appropriate bootstraps are characterized beyond…
No prominent works on this page.
Identification-Robust Inference With Simulation-Based Pseudo-Matching
We develop a general simulation-based inference procedure for partially specified models. Our procedure is based on matching auxiliary statistics to simulated counterparts where nuisance parameters are calibrated neither assuming identification of parameters of interest nor a one-to-one binding function. The conditions underlying the asymptotic validity of our (pseudo-)simulators in conjunction with appropriate bootstraps are characterized beyond…
Finite‐Sample Identification‐Robust Inference for Nonlinear DSGE Models
We develop identification‐robust likelihood‐free simultaneous confidence sets for dynamic stochastic general equilibrium models, without relying on linear approximations. Our methodology integrates simulation estimation methods using auxiliary statistics with Monte Carlo test principles. Results cover deep parameters and impulse responses. Auxiliary statistics include coefficients of linear and nonlinear vector autoregressions and local projectio…
Artificial Intelligence (2 works) · Computer Science (2 works) · Econometrics (2 works) · Inference (2 works) · Advanced Statistical Methods and Models (1 work) · Control Systems and Identification (1 work) · Dynamic stochastic general equilibrium (1 work) · Economics (1 work) · Energy, Environment, and Transportation Policies (1 work) · Macroeconomics (1 work)