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Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are inconsistent. To illustrate the severity of this issue, we randomly generate placebo laws in state-level data on female wages from the Current Population Survey. For each law, we use OLS to compute the DD estimate of its “effect” as well as the standard error of this estimate. These conventional DD standard errors severely understate the standard deviation of the estimators: we find an “effect” significant at the 5 percent level for up to 45 percent of the placebo interventions. We use Monte Carlo simulations to investigate how well existing methods help solve this problem. Econometric corrections that place a specific parametric form on the time-series process do not perform well. Bootstrap (taking into account the autocorrelation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre”- and “post”-period and explicitly takes into account the effective sample size works well even for small numbers of states.

/pdf/how-much-should-we-trust-differences-in-differences-407p70a9b4.pdf

How Much Should We Trust Differences-In-Differences Estimates?

计量经济分析 = Econometric analysis

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Modern Applied Statistics With S

Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

In many settings, a decision-maker wishes to learn a rule, or policy, that maps from observable characteristics of an individual to an action. Examples include selecting offers, prices, advertisements, or emails to send to consumers, as well as the problem of determining which medication to prescribe to a patient. While there is a growing body of literature devoted to this problem, most existing results are focused on the case where data comes from a randomized experiment, and further, there are only two possible actions, such as giving a drug to a patient or not. In this paper, we study the offline multi-action policy learning problem with observational data and where the policy may need to respect budget constraints or belong to a restricted policy class such as decision trees. We build on the theory of efficient semi-parametric inference in order to propose and implement a policy learning algorithm that achieves asymptotically minimax-optimal regret. To the best of our knowledge, this is the first result of this type in the multi-action setup, and it provides a substantial performance improvement over the existing learning algorithms. We then consider additional computational challenges that arise in implementing our method for the case where the policy is restricted to take the form of a decision tree. We propose two different approaches, one using a mixed integer program formulation and the other using a tree-search based algorithm.

Offline Multi-Action Policy Learning: Generalization and Optimization

We present a new perspective on the Synthetic Control (SC) method as a weighted least squares 
regression estimator with time fixed effects and unit weights. This perspective suggests a 
generalization with two way (both unit and time) fixed effects, and both unit and time weights, 
which can be interpreted as a unit and time weighted version of the standard Difference In 
Differences (DID) estimator. We find that this new Synthetic Difference In Differences (SDID) 
estimator has attractive properties compared to the SC and DID estimators. Formally we show that 
our approach has double robustness properties: the SDID estimator is consistent under a wide 
variety of weighting schemes given a well-specified fixed effects model, and SDID is consistent 
with appropriately penalized SC weights when the basic fixed effects model is misspecified and 
instead the true data generating process involves a more general low-rank structure (e.g., a latent 
factor model). We also present results that justify standard inference based on weighted DID 
regression. Further generalizations include unit and time weighted factor models.

https://www.nber.org/system/files/working_papers/w25532/w25532.pdf

Synthetic Difference In Differences

Set-asides and subsidies are used extensively in government procurement and resource sales. We analyze these policies in an empirical model of US Forest Service timber auctions. The model ...

/pdf/set-asides-and-subsidies-in-auctions-qi5gb1gpi2.pdf

Set-asides and subsidies in auctions

Market design to accelerate COVID-19 vaccine supply.

We provide a model of the market for advertising on news media outlets when consumers have opportunities to switch between outlets. We hypothesize that the move to online news content has facilitated greater consumer switching, as well as heterogeneity in consumer switching patterns. The news outlets are modeled as competing two-sided platforms bringing together heterogeneous, partially multi-homing consumers with advertisers with heterogeneous valuations for reaching consumers, and the multi-homing behavior of the advertisers is determined endogenously. The presence of switching consumers means that, in the absence of certain consumer tracking technologies, scarce advertising capacity is taken up by advertisers purchasing wasted impressions on outlets, as a given advertiser may reach the same consumer too many times. This has subtle effects on the equilibrium price for ad impressions and the profits of outlets, and it may lead to heterogeneity in the multi-homing behavior of advertisers. We characterize the impact of greater consumer switching on outlet profits and the impact of technologies that track consumers both within and across outlets on those profits. Somewhat surprisingly, superior tracking technologies may not always increase outlet profits. In addition, we analyze the impact of blogs, aggregators and paywalls on outlet profits from advertising, which ultimately determine market structure and outlet quality investment.

/pdf/the-impact-of-the-internet-on-advertising-markets-for-news-4q646mjhoc.pdf

Susan Athey

Papers

Offline Multi-Action Policy Learning: Generalization and Optimization

Synthetic Difference In Differences

Set-asides and subsidies in auctions

Market design to accelerate COVID-19 vaccine supply.

The Impact of the Internet on Advertising Markets for News Media