Explores heteroskedasticity in econometrics, discussing its impact on standard errors, alternative estimators, testing methods, and implications for hypothesis testing.
Explores heteroskedasticity and autocorrelation in econometrics, covering implications, applications, testing methods, and hypothesis testing consequences.
Covers the detection and correction of parameter errors in power grids, focusing on statistical properties, error identification, computational efficiency, sensitivity analysis, and robust state estimation.
Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.
Introduces the Generalized Method of Moments (GMM), a versatile approach for estimation based on moment restrictions, with applications in asset pricing models.
Covers confidence intervals, hypothesis tests, standard errors, statistical models, likelihood, Bayesian inference, ROC curve, Pearson statistic, goodness of fit tests, and power of tests.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Explores enhancing machine learning predictions by refining error metrics and applying constraints for improved accuracy in electron density predictions.
