Count Data Models & Univariate Time Series Analysis

This lecture covers count data models, focusing on situations where the outcome variable is a weakly positive integer number, such as the number of patents issued by a firm or insurance claims made by a customer. It introduces the Poisson distribution and Poisson regression model for modeling such outcomes. Additionally, it discusses the interpretation of coefficients in count data models and provides an example of analyzing patents and R&D expenditures. The lecture then transitions to univariate time series analysis, emphasizing the modeling of a single economic variable's dynamics for forecasting purposes, particularly through autoregressive integrated moving average (ARIMA) models.