Śılvia Gonçalves
Biographic Data
| ID | 9107508 |
|---|---|
| NAME | Śılvia Gonçalves |
| GIVEN NAMES | Śılvia |
| FAMILY NAME | Gonçalves |
| SIGNATURE | GONÇALVES S |
| AFFILIATIONS | McGill University |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2017 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Discussion of: “Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly”
The ease of estimating linear local projections has made them a popular tool for impulse response function analysis. Kolesár and Plagborg-Møller’s main goal is to inquire whether local projections (LP) estimands of impulse response functions have a causal interpretation when the data generating process (DGP) is nonlinear. This discussion focuses on two questions. First, how should we interpret the magnitude of the linear LP estimands in nonlinear…
Imputation of Counterfactual Outcomes when the Errors are Predictable: Rejoinder
We thank Bruno Ferman (BF), Marcelo Medeiros (MM), Yuya Sasaki (YS), and Kaspar Wüthrich (KW) for their constructive comments on our paper. Below are some further thoughts.1 The discussants offered
Imputation of Counterfactual Outcomes when the Errors are Predictable
A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about …
Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators of Time Series Models
This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing th…
Bootstrap Prediction Intervals for Factor Models
We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, asymptotic intervals have been shown to be valid under Gaussianity of the …
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Bootstrap Prediction Intervals for Factor Models
We propose bootstrap prediction intervals for an observation h periods into the future and its conditional mean. We assume that these forecasts are made using a set of factors extracted from a large panel of variables. Because we treat these factors as latent, our forecasts depend both on estimated factors and estimated regression coefficients. Under regularity conditions, asymptotic intervals have been shown to be valid under Gaussianity of the …
Bootstrapping Two-Stage Quasi-Maximum Likelihood Estimators of Time Series Models
This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing th…
Imputation of Counterfactual Outcomes when the Errors are Predictable: Rejoinder
We thank Bruno Ferman (BF), Marcelo Medeiros (MM), Yuya Sasaki (YS), and Kaspar Wüthrich (KW) for their constructive comments on our paper. Below are some further thoughts.1 The discussants offered
Imputation of Counterfactual Outcomes when the Errors are Predictable
A crucial input into causal inference is the imputed counterfactual outcome. Imputation error can arise because of sampling uncertainty from estimating the prediction model using the untreated observations, or from out-of-sample information not captured by the model. While the literature has focused on sampling uncertainty, it vanishes with the sample size. Often overlooked is the possibility that the out-of-sample error can be informative about …
Discussion of: “Dynamic Causal Effects in a Nonlinear World: the Good, the Bad, and the Ugly”
The ease of estimating linear local projections has made them a popular tool for impulse response function analysis. Kolesár and Plagborg-Møller’s main goal is to inquire whether local projections (LP) estimands of impulse response functions have a causal interpretation when the data generating process (DGP) is nonlinear. This discussion focuses on two questions. First, how should we interpret the magnitude of the linear LP estimands in nonlinear…
Computer Science (4 works) · Econometrics (4 works) · Mathematics (4 works) · Statistics (4 works) · Counterfactual thinking (2 works) · Estimator (2 works) · Forecasting Techniques and Applications (2 works) · Missing data (2 works) · Monetary Policy and Economic Impact (2 works) · Psychology (2 works)