Narrow search
Showing 1 - 10 of 4,427
Cover Image
Historical Econometrics : Instrumental Variables and Regression Discontinuity Designs
Valencia Caicedo, Felipe - 2020
in the econometrics field — in the IV and RDD fronts. I then present a practical guide on regression diagnostics …This chapter surveys the usage of Instrumental Variables (IVs) and Regression Discontinuity Designs (RDDs) in economic …
Persistent link: https://www.econbiz.de/10012824629
Cover Image
A Sharp Test for the Judge Leniency Design
Coulibaly, Mohamed; Hsu, Yu-Chin; Mourifié, Ismael; … - National Bureau of Economic Research - 2024
We propose a new specification test to assess the validity of the judge leniency design. We characterize a set of sharp testable implications, which exploit all the relevant information in the observed data distribution to detect violations of the judge leniency design assumptions. The proposed...
Persistent link: https://www.econbiz.de/10014544734
Cover Image
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations
Farrell, Max - 2015
technical results for high-dimensional, sparse multinomial logistic regression. A simulation study shows our estimator performs …
Persistent link: https://www.econbiz.de/10013035078
Cover Image
Causality, confounding, and Simpson's paradox
Zaman, Asad; Salahuddin, Taseer - In: International econometric review 12 (2020) 1, pp. 50-74
This is an introductory article which explains the importance of explicit consideration and modeling of causality, contrary to current econometric practice, in order to use data set for extraction of meaningful information. One of the easiest to understand approaches to causality is via...
Persistent link: https://www.econbiz.de/10012265782
Cover Image
A graphical approximation to generalization : definitions and diagrams
Martel García, Fernando; Wantchekon, Leonard - 2013
The fundamental problem of external validity is not to generalize from one experiment, so much as to experimentally test generalizable theories. That is, theories that explain the systematic variation of causal effects across contexts. Here we show how the graphical language of causal diagrams...
Persistent link: https://www.econbiz.de/10009789052
Cover Image
A causal approach to test empirical capital structure regularities
Cenci, Simone; Kealhofer, Stephen - In: The Journal of finance and data science : JFDS 8 (2022), pp. 214-232
Capital structure theories are often formulated as causal narratives to explain which factors drive financing choices. These narratives are usually examined by estimating cross-sectional relations between leverage and its determinants. However, the limitations of causal inference from...
Persistent link: https://www.econbiz.de/10014433694
Cover Image
Calculating effective degrees of freedom for forecast combinations and ensemble models
Younker, James - 2022
Forecast combinations, also known as ensemble models, routinely require practitioners to select a model from a massive number of potential candidates. Ten explanatory variables can be grouped into 21078 forecast combinations, and the number of possibilities increases further to 21078+21078 if we...
Persistent link: https://www.econbiz.de/10013402082
Cover Image
Visualizing Uncertainty : On Soyer's and Hogarth's 'The Illusion of Predictability: How Regression Statistics Mislead Experts'
Ziliak, Stephen - 2012
predictions based on commonly provided regression output. Visual displays of quantitative information, including simple plots of …
Persistent link: https://www.econbiz.de/10013103861
Cover Image
Evaluating Performance of Regression Machine Learning Models Using Multiple Error Metrics in Azure Machine Learning Studio
Botchkarev, Alexei - 2018
Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud … limitations, e.g. small number of performance metrics and lack of functionality to evaluate custom built regression models with R … evaluation of the regression models. EEM offers 4 times more metrics than built-in Azure Evaluate Model module. EEM metrics …
Persistent link: https://www.econbiz.de/10012919484
Cover Image
Lasso Regressions and Forecasting Models in Applied Stress Testing
Chan-Lau, Jorge A. - 2016
Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are...
Persistent link: https://www.econbiz.de/10012991517