Introduction
The courses examines time series methods for data analysis with an emphasis on macroeconomic applications. The aim of the class is to provide students with a basic formal introduction to modern time series techniques, hands-on experience in applying them to real-world macroeconomic data, and understanding of how to bring data to bear on policy-relevant macroeconomic theories.
- Syllabus
- Reference: “Applied Time Series for Macroeconomics,” by Hilde Bjornland and Leif Thorsrud, Gyldendal Publisher (2015).
Module 1: Fundamentals of Time Series
Module 1 begins with a quick review of random variables, and hypothesis testing. It then introduces the basic elements of univariate time-series analysis: covariance stationarity, white noise process, moving averages, and autoregressive processes.
Module 2: Estimating and Using Univariate Linear Time Series Models
Module 2 studies the estimation of linear time-series models and their use for inference and for prediction.
Module 3: Non-Stationarity, Trends, and Cycles
Module 3 studies the properties of non-stationary time series, and analyzes alternative measures of trends and cycles.
Module 4: Vector Autoregressions
Module 4 studies vector autoregressions (VARs), starting with reduced-form analysis, and introducing structural VARs via the equations ordering (Cholesky Decomposition) approach.
Module 5: Cointegration and Spurious Regressions
Module 5 studies nonstationarity in a multivariate setting, analyzing spurious regressions, and cointegration.