Model Uncertainty

This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.

Robust Parametric Inference

This chapter looks at alternatives to the use of asymptotic theory and finite-sample theory for the purpose of inference. It considers numerical approaches that include the bootstrap and the Jackknife and considers procedures for dependent processes as well as heteroskedastic and independent identically distributed instances.

Univariate Linear Time Series Models

This chapter looks at a range of popular univariate time series models and their use for forecasting.

Random Walks, Unit Roots, and Spurious Relationships

This chapter outlines pitfalls of using standard inference procedures common in cross- sectional settings in time series settings and presents alternative procedures. It also addresses the issue of spurious regression and cautions the reader against the unquestioning use of cross section tools in time series settings.

Introduction to Linear Time Series Models

This chapter introduces time series data and outlines how it differs from cross sectional data. It also highlights how the object of interest when modelling time series data is the forecast, which differs from the object of interest in cross-sectional modelling, which is typically some parameter of interest that has an economic interpretation.

Advanced Topics

This chapter covers two advanced topics: a machine learning method (support vector machines useful for classification) and nonparametric kernel regression.

Robust Parametric Estimation

This chapter looks at issues surrounding outliers in data and methods for addressing their presence.