This course provides a Ph.D.-level introduction to econometric time-series analysis.
Book, slides, code, etc. here. The slides are the center of everything.
Format: Lectures that stress applied econometric theory.
Time Domain: The Wold Representation and its Approximation
Frequency Domain: The Spectrum and its Approximation
Markovian Structure, State Space, and the Kalman Filter
Likelihood Evaluation and Optimization
Bayesian Posterior Analysis and Markov Chain Monte Carlo
Nonlinear/Non-Gaussian State Space
More Simulation Methods and Applications (Monte Carlo Methods, Bootstrap, etc.)
Integration, Cointegration, and Long Memory
Conditional Variance Dynamics
Office hours (held in McNeil 519) here.
Teaching assistants will be heavily involved, including small-scale help by email and large-scale help in weekly review/supplementary sessions. (Office hours and review session times and locations, contact info, etc., to be announced.)
Grading: N problem sets (each 60/N %) and a final exam ("practice prelim") (40%). Good performance is crucially dependent on regular class preparation, attendance and participation.
Problem Set 1 (Due February 22.)
Use monthly U.S. housing starts and completions data (detrended and deseasonalized if necessary), reserving the last six observations for out-of-sample forecast comparisons. Discuss all results as appropriate. First graph the data. Then do the following. Model the two series jointly as a VAR(4); Calculate the autocorrelation and spectral density functions implied by your estimated VAR; Perform a Granger-causality analysis; Using Cholesky factor identification, calculate and graph the full set of impulse-response functions; Forecast the six hold-out observations and assess accuracy; using the full dataset forecast the sample path for the next twelve months.
Final exam date: Standard
Note well that modifications and adjustments to this syllabus / web site are inevitable and may be implemented at any time. Check frequently for updates.