Masato Shimokawa, Kou Fujimori
arXiv 16 Mar 2025 · Econometrics
arXiv:2503.12378 · PDF · Extracted main text
Structural vector autoregressive (SVAR) models are widely used to analyze the simultaneous relationships between multiple time-dependent data. Various statistical inference methods have been studied to overcome the identification problems of SVAR models. However, most of these methods impose strong assumptions for innovation processes such as the uncorrelation of components. In this study, we relax the assumptions for innovation processes and propose an identification method for SVAR models under the zero-restrictions on the coefficient matrices, which correspond to sufficient conditions for LU decomposition of the coefficient matrices of the reduced form of the SVAR models. Moreover, we establish asymptotically normal estimators for the coefficient matrices and impulse responses, which enable us to construct test statistics for the simultaneous relationships of time-dependent data. The finite-sample performance of the proposed method is elucidated by numerical simulations. We also present an example of an empirical study that analyzes the impact of policy rates on unemployment and prices.
appendix boundary found by appendix_command · 70% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Rubio-Ramirez, Waggoner and Zha (2010) Structural vector autoregressions: Theory of identification and algorithms for inference | 0.644 | 2 | 2 | 100% |
| 2 | Hamilton (1994) Time series analysis | 0.511 | 2 | 2 | 50% |
| 3 | Sims (1980) Macroeconomics and reality | 0.511 | 2 | 2 | 50% |
| 4 | Lütkepohl (1990) Asymptotic distributions of impulse response functions and forecast error variance decompositions of vector autoregressive models | 0.511 | 2 | 1 | 100% |
| 5 | Belloni and Oliveira (2018) A high dimensional central limit theorem for martingales, with applications to context tree models | 0.405 | 1 | 1 | 100% |
| 6 | Bernanke (1986) Alternative explanations of the money-income correlation | 0.405 | 1 | 1 | 100% |
| 7 | Blanchard and Quah (1988) The Dynamic Effects of Aggregate Demand and Supply Disturbances | 0.405 | 1 | 1 | 100% |
| 8 | Canova and De Nicolo (2002) Monetary disturbances matter for business fluctuations in the G-7 | 0.405 | 1 | 1 | 100% |
| 9 | Chernozhukov, Chetverikov, Kato and Koike (2023) High-dimensional data bootstrap | 0.405 | 1 | 1 | 100% |
| 10 | Fang, Koike, Liu and Zhao (2023) High-dimensional Central Limit Theorems by Stein's Method in the Degenerate Case | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 20 scored citations.