forcings. In particular, we consider quantile regression models with potentially time-varying parameters (TVP), implemented by …

and Prediction -- 1.8 Chapter Summary -- Chapter 2 Simple Linear Regression -- 2.1 PROBABILITY MODEL FOR X and Y -- 2 ….2 Least Squares Criterion -- 2.3 Model Evaluation -- 2.3.1 Regression standard error -- 2.3.2 Coefficient of determination-R2 … Linear Regression -- 3.1 Probability Model for (X1, X2, …) and Y -- 3.2 Least Squares Criterion -- 3.3 Model Evaluation -- 3 …

on Regression Discontinuity designs, covering a wide range of methodological and practical topics such as identification …Front Cover -- Regression Discontinuity Designs: Theory and Applications -- Copyright Page -- Contents -- List of … -- Acknowledgments -- References -- On Interpreting the Regression Discontinuity Design as a Local Experiment -- 1. The Continuity …

semiparametric modeling and estimation, A/B experiments, big-data analysis, and quantile regression. …

Baseline: Regressions with Constant Coefficients -- 3.2. TVP Regression Models, Model Averaging and Model Switching with Google …

.1.2 Poisson Process -- 1.1.3 Waiting Time Distributions -- 1.1.4 Binomial Stopped by the Poisson -- 1.2 Poisson Regression -- 1 ….2.1 Counts Derived from a Point Process -- 1.2.2 Counts Derived from Continuous Data -- 1.2.3 Regression Specification -- 1 …-Based Models -- 2.7.5 Conditional Moment Tests -- 2.8 Bibliographic Notes -- 2.9 Exercises -- CHAPTER 3 Basic Count Regression. …