arXiv 30 Jun 2026 · Econometrics
arXiv:2606.31685 · PDF · DOI · OpenAlex · Extracted main text
This paper studies inference for time-series GMM when uncertainty comes from shock assignment within a realized historical episode. Rather than treating the data as one random draw from a population of hypothetical economies, the framework conditions on the historical environment and considers alternative realizations of shocks and instruments. For locally correctly specified GMM estimators, the centered moment has design long-run variance $Ω_R$, which determines the sandwich covariance for the finite-history estimand. Conventional HAC estimators instead converge to $Ω_R^+=Ω_R+Ω_μ$, where $Ω_μ\succeq0$ is the long-run variance of the centered mean-moment path. HAC inference is therefore conservative for scalar functions of the finite-history estimand. Projection adjustment using predetermined covariates can reduce this HAC variance limit in Loewner order and, under an additional long-run orthogonality condition, yields a tighter conservative bound on the corresponding asymptotic covariance. Monte Carlo evidence shows when the distinction is quantitatively important. In a monetary-policy application, standard-error reductions from rich macro covariates provide a diagnostic for economically meaningful predictable variation in the mean-moment path.
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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 | Rambachan \ Shephard (2025) `When Do Common Time Series Estimands Have Nonparametric Causal Meaning?' | 0.928 | 4 | 3 | 100% |
| 2 | Kakehi, Matsushita \ Otsu (2026) GMM under finite-population asymptotics: instrumental variables and regression adjustment, Econometrics Paper EM649, Suntory and… | 0.843 | 3 | 3 | 100% |
| 3 | Neyman (1923) `Sur les applications de la théorie des probabilités aux expériences agricoles: Essai des principes', Roczniki Nauk Rolniczych 1… | 0.843 | 3 | 3 | 100% |
| 4 | Abadie, Athey, Imbens \ Wooldridge (2020) `Sampling-Based Versus Design-Based Uncertainty in Regression Analysis', Econometrica 88(1), 265–296 | 0.644 | 2 | 2 | 100% |
| 5 | Bojinov \ Shephard (2019) `Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading', Journal of the American Statistical Assoc… | 0.644 | 2 | 2 | 100% |
| 6 | Newey \ McFadden (1994) Large Sample Estimation and Hypothesis Testing, in R. F | 0.644 | 2 | 2 | 100% |
| 7 | Xu (2021) `Potential Outcomes and Finite–Population Inference for M-Estimators', The Econometrics Journal 24(1), 162–176 | 0.644 | 2 | 2 | 100% |
| 8 | Andrews (1991) `Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation', Econometrica 59(3), 817–858 | 0.405 | 1 | 1 | 100% |
| 9 | Auerbach \ Gorodnichenko (2012) `Measuring the Output Responses to Fiscal Policy', American Economic Journal: Economic Policy 4(2), 1–27 | 0.405 | 1 | 1 | 100% |
| 10 | Bernanke, Boivin \ Eliasz (2005) `Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach', Quarterly Journal of Econ… | 0.405 | 1 | 1 | 100% |
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