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Applied macroeconomists frequently use impulse response estimators motivated by linear models. We study whether the estimands of such procedures have a causal interpretation when the true data generating process is in fact nonlinear. We show that vector autoregressions and linear local projections onto observed shocks or proxies identify weighted averages of causal effects regardless of the extent of nonlinearities. By contrast, identification approaches that exploit heteroskedasticity or non-Gaussianity of latent shocks are highly sensitive to departures from linearity. Our analysis is based on new results on the identification of marginal treatment effects through weighted regressions, which may also be of interest to researchers outside macroeconomics., Comment: 60 pages, including all appendices

This study examines the effect of fiscal austerity measures on infant mortality in Greece. Austerity measures were initiated by the tripartite committee and implemented between 2010 and 2017 to counteract deep fiscal deficit and large public debt. By comparing Greece with a plausible donor pool of OECD and Mediterranean member states in the period 1991-2020, we estimate a series of missing counterfactual scenarios to evaluate the infant mortality effects of large-scale reduction in spending on health care. A series of hybrid synthetic control and difference-in-differences estimates indicate a unique and pervasive increase in infant mortality after the implementation of austerity measures. Compared to a plausible OECD and Mediterranean counterfactual scenario, pro-cyclical austerity measures are associated with derailed and permanently increased infant mortality up to the present day. Our estimates suggest that compared to a plausible counterfactual scenario, the cumulative infant mortality cost of austerity policies exceeds 10,000 infant deaths or slightly less than 850 deaths for each year of the austerity policies. Notably, mortality increases are concentrated among boys. The estimated impacts survive a battery of rigorous robustness and placebo tests.

Kaon physics is at a turning point -- while the rare-kaon experiments NA62 and KOTO are in full swing, the end of their lifetime is approaching and the future experimental landscape needs to be defined. With HIKE, KOTO-II and LHCb-Phase-II on the table and under scrutiny, it is a very good moment in time to take stock and contemplate about the opportunities these experiments and theoretical developments provide for particle physics in the coming decade and beyond. This paper provides a compact summary of talks and discussions from the Kaons@CERN 2023 workshop., Comment: 55 pages, Summary of Kaons@CERN 23 workshop, references updated, typos fixed, version as published in EPJC

We show, using three empirical applications, that linear regression estimates which rely on the assumption of sparsity are fragile in two ways. First, we document that different choices of the regressor matrix that do not impact ordinary least squares (OLS) estimates, such as the choice of baseline category with categorical controls, can move sparsity-based estimates two standard errors or more. Second, we develop two tests of the sparsity assumption based on comparing sparsity-based estimators with (OLS). The tests tend to reject the sparsity assumption in all three applications. Unless the number of regressors is comparable to or exceeds the sample size, (OLS) yields more robust results at little efficiency cost., Comment: 45 pages, including appendices

The sector of decay constants of the octet of light pseudoscalar mesons in the framework of 'resummed' $SU(3)$ chiral perturbation theory is investigated. A theoretical prediction for the decay constant of $\eta$-meson is compared to a range of available determinations. Compatibility of these determinations with the latest fits of the $SU(3)$ low energy coupling constants is discussed. Using a Bayesian statistical approach, constraints on the low energy coupling constants $L_4^r$ and $L_5^r$, as well as higher order remainders to the decay constants $F_K$ and $F_\eta$, are extracted from the most recent experimental and lattice QCD inputs for the values of the decay constants., Comment: 13 pages, 11 figures, published version, significantly updated

Competitive programming has become a popular way for programmers to test their skills. Large-scale online programming contests attract millions of experienced programmers to compete against each other. Competition-level programming problems are challenging in nature, and participants often fail to solve the problem on their first attempt. Some online platforms for competitive programming allow programmers to practice on competition-level problems as well, and the standard feedback for an incorrect practice submission is the first test case that the submission fails. Often, the failed test case does not provide programmers with enough information to resolve the errors in their code, and they abandon the problem after several more unsuccessful attempts. We present Clef, the first data-driven tool that can generate feedback on competition-level code automatically by repairing programmers' incorrect submissions. The key development is that Clef can learn how to generate repairs for incorrect submissions by examining the repairs that other programmers made to their own submissions over time. Since the differences between an incorrect program and a correct program for the same task may be significant, we introduce a new data structure, merge trees, to capture the changes between submissions. Merge trees are versatile: they can encode both large algorithm-level redesigns and small statement-level alterations. Clef applies the patterns it learns from a database of submissions to generate repairs for new submissions outside the database. We evaluated Clef on six real-world problems from Codeforces, the world's largest platform for competitive programming. Clef achieves 42.1% accuracy in repairing programmers' incorrect submissions. Even when given incorrect submissions from programmers who never found the solution to a problem on their own, Clef repairs the users' programs 34.1% of the time.

The 2021 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel was awarded to David Card "for his empirical contributions to labour economics" and to Joshua Angrist and Guido Imbens "for their methodological contributions to the analysis of causal relationships." We survey these contributions of the three laureates, and discuss how their empirical and methodological insights transformed the modern practice of applied microeconomics. By emphasizing research design and formalizing the causal content of different econometric procedures, the laureates shed new light on key questions in labour economics and advanced a robust toolkit for empirical analyses across many fields.

In many applications of regression discontinuity designs, the running variable used by the administrator to assign treatment is only observed with error. We show that, provided the observed running variable (i) correctly classifies the treatment assignment, and (ii) affects the conditional means of the potential outcomes smoothly, ignoring the measurement error nonetheless yields an estimate with a causal interpretation: the average treatment effect for units whose observed running variable equals to the cutoff. We show that, possibly after doughnut trimming, these assumptions accommodate a variety of settings where support of the measurement error is not too wide. We propose to conduct inference using bias-aware methods, which remain valid even when discreteness or irregular support in the observed running variable may lead to partial identification. We illustrate the results for both sharp and fuzzy designs in an empirical application., Comment: 29 pages

With the rapid development of new anti-cancer agents which are cytostatic, new endpoints are needed to better measure treatment efficacy in phase II trials. For this purpose, Von Hoff (1998) proposed the growth modulation index (GMI), i.e. the ratio between times to progression or progression-free survival times in two successive treatment lines. An essential task in studies using GMI as an endpoint is to estimate the distribution of GMI. Traditional methods for survival data have been used for estimating the GMI distribution because censoring is common for GMI data. However, we point out that the independent censoring assumption required by traditional survival methods is always violated for GMI, which may lead to severely biased results. In this paper, we construct nonparametric estimators for the distribution of GMI, accounting for the dependent censoring of GMI. We prove that the proposed estimators are consistent and converge weakly to zero-mean Gaussian processes upon proper normalization. Extensive simulation studies show that our estimators perform well in practical situations and outperform traditional methods. A phase II clinical trial using GMI as the primary endpoint is provided for illustration.

We revisit the finite-sample behavior of single-variable just-identified instrumental variables (just-ID IV) estimators, arguing that in most microeconometric applications, the usual inference strategies are likely reliable. Three widely-cited applications are used to explain why this is so. We then consider pretesting strategies of the form $t_{1}>c$, where $t_{1}$ is the first-stage $t$-statistic, and the first-stage sign is given. Although pervasive in empirical practice, pretesting on the first-stage $F$-statistic exacerbates bias and distorts inference. We show, however, that median bias is both minimized and roughly halved by setting $c=0$, that is by screening on the sign of the \textit{estimated} first stage. This bias reduction is a free lunch: conventional confidence interval coverage is unchanged by screening on the estimated first-stage sign. To the extent that IV analysts sign-screen already, these results strengthen the case for a sanguine view of the finite-sample behavior of just-ID IV., Comment: 29 pages

We study regressions with multiple treatments and a set of controls that is flexible enough to purge omitted variable bias. We show that these regressions generally fail to estimate convex averages of heterogeneous treatment effects -- instead, estimates of each treatment's effect are contaminated by non-convex averages of the effects of other treatments. We discuss three estimation approaches that avoid such contamination bias, including the targeting of easiest-to-estimate weighted average effects. A re-analysis of nine empirical applications finds economically and statistically meaningful contamination bias in observational studies; contamination bias in experimental studies is more limited due to smaller variability in propensity scores., Comment: 69 pages, including all appendices

We consider inference on a scalar regression coefficient under a constraint on the magnitude of the control coefficients. A class of estimators based on a regularized propensity score regression is shown to exactly solve a tradeoff between worst-case bias and variance. We derive confidence intervals (CIs) based on these estimators that are bias-aware: they account for the possible bias of the estimator. Under homoskedastic Gaussian errors, these estimators and CIs are near-optimal in finite samples for MSE and CI length. We also provide conditions for asymptotic validity of the CI with unknown and possibly heteroskedastic error distribution, and derive novel optimal rates of convergence under high-dimensional asymptotics that allow the number of regressors to increase more quickly than the number of observations. Extensive simulations and an empirical application illustrate the performance of our methods., Comment: 49 pages, including all appendices

The current state of knowledge of the order parameters of the spontaneously broken chiral symmetry, the quark condensate and the pseudoscalar decay constant in the chiral limit, is briefly reviewed, based on available phenomenological fits and lattice QCD calculations. We argue that while the theory is pretty well understood in the two-flavour case, there is still a gap in the knowledge of the characteristics of the QCD vacuum in the three-flavour one. Our constraints for the three-flavour parameters obtained by a Bayesian statistical analysis of the decays of eta to three pions are presented. Possible implications of a new analysis of subthreshold parameters of pion-pion scattering is outlined., Comment: Presented at ICHEP2020, Prague, July 31, 2020. 6 pages, 2 tables, 1 figure

We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem. The intervals are centered at the usual linear empirical Bayes estimator, but use a critical value accounting for shrinkage. Parametric EBCIs that assume a normal distribution for the means (Morris, 1983b) may substantially undercover when this assumption is violated. In contrast, our EBCIs control coverage regardless of the means distribution, while remaining close in length to the parametric EBCIs when the means are indeed Gaussian. If the means are treated as fixed, our EBCIs have an average coverage guarantee: the coverage probability is at least $1 - \alpha$ on average across the $n$ EBCIs for each of the means. Our empirical application considers the effects of U.S. neighborhoods on intergenerational mobility., Comment: 45 pages plus a 25-page supplemental appendix

The decay constant of the $\eta$-meson in the framework of 'resummed' chiral perturbation theory is discussed. A theoretical prediction is compared to the available determinations. Compatibility of these determinations with the latest fits of the SU(3) low energy coupling constants is investigated. Preliminary results for the obtained constraints on the low energy coupling constants $L^r_5$ and $L^r_4$, using Bayesian statistical approach, are presented., Comment: Presented at "QCD 19", Montpellier, France, July 4, 2019. 4 pages, 5 figures

We consider inference in models defined by approximate moment conditions. We show that near-optimal confidence intervals (CIs) can be formed by taking a generalized method of moments (GMM) estimator, and adding and subtracting the standard error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into account this potential bias, and therefore differs from the one that is optimal under correct specification. To formally show the near-optimality of these CIs, we develop asymptotic efficiency bounds for inference in the locally misspecified GMM setting. These bounds may be of independent interest, due to their implications for the possibility of using moment selection procedures when conducting inference in moment condition models. We apply our methods in an empirical application to automobile demand, and show that adjusting the weighting matrix can shrink the CIs by a factor of 3 or more., Comment: 69 pages, plus a 12-page supplemental appendix

We study inference in shift-share regression designs, such as when a regional outcome is regressed on a weighted average of sectoral shocks, using regional sector shares as weights. We conduct a placebo exercise in which we estimate the effect of a shift-share regressor constructed with randomly generated sectoral shocks on actual labor market outcomes across U.S. Commuting Zones. Tests based on commonly used standard errors with 5\% nominal significance level reject the null of no effect in up to 55\% of the placebo samples. We use a stylized economic model to show that this overrejection problem arises because regression residuals are correlated across regions with similar sectoral shares, independently of their geographic location. We derive novel inference methods that are valid under arbitrary cross-regional correlation in the regression residuals. We show using popular applications of shift-share designs that our methods may lead to substantially wider confidence intervals in practice., Comment: 44 pages, plus a 98 page online supplement