Uncertain Short‐Run Restrictions and Statistically Identified Structural Vector Autoregressions
Bibliographic Data
| ID | 21653422 |
|---|---|
| Authors | Sascha A Keweloh (0000-0002-0041-6052, Department of Economics TU Dortmund University Dortmund Germany), Shu Wang (0009-0009-1730-2316, Department of Economics University of Göttingen Göttingen Germany) |
| Year | 2026 |
| Volume | 41 |
| Issue | 1 |
| Pages | 12-25 |
| Publication date | 2026-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Applied Econometrics (JOURNAL) |
| Journal identifiers | ISSN: 1099-1255 • E-ISSN: 0883-7252 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jae.70012 |
| OpenAlex | W4414796484 |
| Language | EN |
| References cited | 48 |
This study proposes a combination of a statistical identification approach with potentially invalid short‐run zero restrictions. The estimator shrinks towards imposed restrictions and stops shrinkage when the data provide evidence against a restriction. We demonstrate that incorporating valid restrictions through the shrinkage approach enhances the efficiency of the statistically identified estimator, and the impact of invalid restrictions vanishes as the sample size increases. Applying the estimator to an oil market model indicates that incorporating stock market data into the analysis is crucial, as it enables the identification of information shocks, which are shown to be important drivers of the oil price
Estimator · Market data · Shrinkage · Shrinkage estimator · Stock market · Energy Load and Power Forecasting · Market Dynamics and Volatility · Monetary Policy and Economic Impact
The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States
What are the effects of monetary policy on output? Results from an agnostic identification procedure
Structural Interpretation of Vector Autoregressions with Incomplete Identification
Oil prices, US stock return, and the dependence between their quantiles
The Role of Inventories and Speculative Trading in the Global Market for Crude Oil
Macroeconomics and Reality
Higher Moments and Efficiency Gains in Recursive Structural Vector Autoregressions
Identifying Structural Vector Autoregression via Leptokurtic Economic Shocks
GMM Estimation of Non-Gaussian Structural Vector Autoregression
A Generalized Method of Moments Estimator for Structural Vector Autoregressions Based on Higher Moments
Identification of Structural Vector Autoregressions by Stochastic Volatility
Statistical Identification of Independent Shocks with Kernel-based Maximum Likelihood Estimation and an Application to the Global Crude Oil Market
Identification Through Heteroskedasticity
Energy Markets and Global Economic Conditions
| Highly cited | No |
|---|