Event studies are commonly applied in corporate finance, with a focus on testing market efficiency hypotheses and evaluating the effects of corporate decisions on firm values, stock prices, and other outcome variables. The chapter discusses the event-study model using examples from (i) return predictability literature; (ii) the effects of firm-level and macro news on stock returns, testing semi-strong efficiency; as well as (iii) insider trading, testing the strong form of efficiency. In short-term event studies the chapter reviews abnormal (AR) and cumulative abnormal return (CAR) calculations and discusses statistical tests of ARs and CARs. It also covers long-term event studies and discusses the buy-and-hold abnormal returns as well as the calendar-time portfolio approach. The chapter provides an application of a short-term event study by examining how stock prices respond to the news of a CEO’s departure. The chapter ends with lab work and a mini case study.

This chapter introduces some nonlinear time series models of widespread use in economics and finance. Specifically, we consider structural breaks, GARCH models, and copula models.

This chapter gives a more comprehensive treatment of nonparametric methods for estimating density functions and dynamic regression models. We also consider the emerging material on the case where there are many explanatory variables and how selection methods can be used to apply estimation and inference techniques to this case.

This chapter introduces the Bayesian approach. We define the key concepts that are needed to understand Bayesian inference and the comparison with frequentist inference. We show how these concepts can be applied in the linear time series models considered earlier and discuss the modern treatment of vector autoregression models from a Bayesian perspective.

This chapter considers the multivariate case, extending the univariate concepts to the vector time series case. We consider vector autoregressions from different points of view.

This chapter introduces the class of autoregressive moving average models and discusses their properties in special cases and in general. We provide alternative methods for the estimation of unknown parameters and describe the properties of the estimators. We discuss key issues like hypothesis testing and model selection.

This chapter is concerned with different approaches to accounting for trend and seasonal components. We consider both deterministic and stochastic approaches and show the overlap and contrast between these approaches. Estimation and inference are treated.

This chapter introduces the frequency-domain view and how this way of thinking can help with understanding periodic behavior and cycles. We define the spectral density function and how commonly used filters affect the spectral shape. We discuss estimation by the periodogram and smoothing methods.

In this chapter we consider the continuous-time setting. We consider some classical models and their estimation, and the more recent literature on high-frequency econometrics.

In this chapter we consider the question of forecasting. We consider model-based and ad hoc approaches to this question. We discuss the issue of forecast evaluation and comparison.