Standard Synthetic Control Methods: The Case of Using All Preintervention Outcomes Together With Covariates

Kaul, Ashok; Klößner, Stefan; Pfeifer, Gregor; Schieler, Manuel

doi:10.1080/07350015.2021.1930012

Cited by 103 publications

(91 citation statements)

References 41 publications

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“…On the one hand, using all lagged outcome variables avoids the problem of omitting potentially irrelevant covariates. Indeed, this practice eliminates all other predictors' effects so that the synthetic counterfactual is created regardless of the other predictors' values (Kaul et al, 2021). This specification is the one that minimizes the root mean squared prediction error (RMSPE) in the pre-treatment period, and that is not subject to arbitrary decisions.…”

Section: Specification Searchingmentioning

confidence: 99%

“…While this finding gives indirect support to the effectiveness of non-pharmaceutical interventions in reducing the mortality rate, there is still a need for a criterion to select a given specification from a set of possible alternatives. It is well known that when covariates are expected to be useless in explaining the outcome, the recommended specification should use all pre-treatment outcome lags, i.e., specification (a0) (Kaul et al, 2021). However, if the control unit should also match several socio-economically relevant covariates, attention should be paid to the specifications allowing external predictors.…”

Section: Specification Searchingmentioning

confidence: 99%

See 1 more Smart Citation

The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy

Cerqueti

Coppier

Girardi³

et al. 2021

The Econometrics Journal

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This paper estimates the effects of non-pharmaceutical interventions – mainly, the lockdown – on the COVID-19 mortality rate for the case of Italy, the first Western country to impose a national shelter-in-place order. We use a new estimator, the Augmented Synthetic Control Method (ASCM), that overcomes some limits of the standard Synthetic Control Method (SCM). The results are twofold. From a methodological point of view, the ASCM outperforms the SCM in that the latter cannot select a valid donor set, assigning all the weights to only one country (Spain) while placing zero weights to all the remaining. From an empirical point of view, we find strong evidence of the effectiveness of non-pharmaceutical interventions in avoiding losses of human lives in Italy: conservative estimates indicate that the policy saved in total more than 21,000 human lives.

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Section: Specification Searchingmentioning

confidence: 99%

Section: Specification Searchingmentioning

confidence: 99%

The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy

Cerqueti

Coppier

Girardi³

et al. 2021

The Econometrics Journal

View full text Add to dashboard Cite

show abstract

“…Moreover, we focus only on episodes of multilateral trade sanctions, that is sanctions imposed by the UN and/or jointly imposed by the EU and the US. We select the countries meeting the following conditions: (a) the treated country and the control group must 11 There is a debate about the optimal choice of predictor variables, and Kaul et al (2021) show that estimation results can vary considerably when the usage of outcome lags as predictors is restricted. As a robustness check, we also add a fairly standard set of trade predictors such as real per capita GDP, population, total trade (as a percentage of GDP), a war dummy, and the Polity IV dichotomous indicator for democracy.…”

Section: Case Studiesmentioning

confidence: 99%

Economic sanctions and trade flows in the neighbourhood

Bove¹,

Salvatore²,

Nisticò³

2021

WIDER Working Paper

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“…In applications the covariate matrix X a may contain some elements of Z a . In cases where X a contains all pre-treatment outcomes, Kaul et al (2015) has shown that the predictor weights V will give no weight to auxillary covariates (covariates that are not the pre-treatment outcomes), rendering these irrelevant to the model.…”

Section: Variable Selectionmentioning

confidence: 99%

Balancing weights for region-level analysis: the effect of Medicaid Expansion on the uninsurance rate among states that did not expand Medicaid

Rubinstein¹,

Haviland²,

Choi³

2021

Preprint

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We estimate the effect of Medicaid expansion on the adult uninsurance rate in states that did not expand Medicaid in 2014. Using data from the American Community Survey (ACS), we estimate this effect -the treatment effect on the controls (ETC) -by re-weighting expansion regions to approximately balance the covariates from non-expansion regions using an extension of the stable balancing weights objective function (Zubizarreta (2015)). We contribute to the balancing weights literature by accounting for hierarchical data structure and covariate measurement error when calculating our weights, and to the synthetic controls literature (see, e.g. Abadie, Diamond and Hainmueller (2010)) by outlining a set of assumptions that identifies the ETC using time-series cross-sectional data. We estimate that Medicaid expansion would have changed the uninsurance rate by -2.33 (-3.49, -1.16) percentage points. These results are smaller in absolute magnitude than existing estimates of the treatment effect on the treated (ETT), though may not be directly comparable due to the study design, target population, and level of analysis. Regardless, we caution against making inferences about the ETC using estimates of the ETT, and emphasize the need to directly estimate the appropriate counterfactual when they are the quantity of interest.

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Standard Synthetic Control Methods: The Case of Using All Preintervention Outcomes Together With Covariates

Cited by 103 publications

References 41 publications

The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy

The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy

Economic sanctions and trade flows in the neighbourhood

Balancing weights for region-level analysis: the effect of Medicaid Expansion on the uninsurance rate among states that did not expand Medicaid

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