What Impulse Response Do Instrumental Variables Identify?
工具变量究竟识别哪一种脉冲响应?AI 解读 ↓
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工具变量究竟识别哪一种脉冲响应?AI 解读 ↓
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The local projection-instrumental variable (LP-IV) literature has been largely silent on cases in which impulse responses are set-identified, arising when the shock of interest is composite and instruments are correlated with multiple components. We demonstrate that LP-IV estimands constructed using one instrument at a time identify affine combinations of impulse responses to structural shock components with instrument-specific and potentially negative weights, challenging standard causal interpretation. The two-stage least squares compounds the identification problem. However, we show that individual LP-IV estimands characterize the identified set when sign restrictions on the correlations between instruments and structural shock components are imposed. Under weak stationarity, these identified sets are sharp and cannot be further narrowed in key cases. Two empirical examples--decomposing the U.S. government spending multiplier and disentangling pure monetary shocks from central bank information shocks--illustrate the usefulness of our approach.
Two frequent approaches for identifying structural VARs are external instruments, which carry economic content but are often weak, and non-Gaussianity of the shocks, which provides statistical identification but carries no economic meaning. We combine the two strategies in a single generalized method of moments framework. This hybrid approach point-identifies the target shocks while also identifying the non-target shocks up to sign and ordering. Under suitable rank…
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In observational time series, statistical inference for dynamic causal effects of a one-time intervention across horizons is complicated by high-dimensional observed pre-treatment information, unmeasured confounding, and serial dependence. To address these challenges, we develop a semiparametric framework for inference from a single serially dependent time series, integrating debiased machine learning with instrumental variables through buffered block cross-fitting. Under…
时间序列中的动态局部平均处理效应
This paper discusses identification, estimation, and inference on dynamic local average treatment effects (LATEs) in instrumental variables (IVs) settings. First, we show that compliers--observations whose treatment status is affected by the instrument--can be identified individually in time series data using smoothness assumptions and local comparisons of treatment assignments. Second, we show that this result enables not only better interpretability of IV estimates but…
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This paper studies state-dependent local projections (LPs). First, I establish a general characterization of their estimand: under minimal assumptions, state-dependent LPs recover weighted averages of causal effects. This holds for essentially all specifications used in practice. Second, I show that state-dependent LPs and VARs target different estimands and propose a simple VAR-based estimator whose probability limit equals the LP estimand. Third, in instrumental variable…
使用外部工具变量的面板向量自回归:识别、估计与推断
This paper proposes an identification inspired from the SVAR-IV literature that uses external instruments to identify PVARs, and discusses associated issues of identification, estimation, and inference. I introduce a form of local average treatment effect - the $μ$-LATE - which arises when a continuous instrument targets a binary treatment. Under standard assumptions of independence, exclusion, and monotonicity, I show that externally instrumented PVARs estimate the $μ$-LATE.…
独立冲击的核最大似然统计识别及全球原油市场应用
Independent component analysis has emerged as a promising approach for revealing structural relationships in multivariate dynamic systems, particularly in scenarios with limited knowledge of causal patterns. This article introduces a robust kernel-based maximum likelihood (KML) estimation method that accommodates the distributional characteristics of the structural sources of data variation. Our Monte Carlo study demonstrates the superior performance of the KML estimator…