Time Series Analysis

Calendar

SES #	TOPICS
I. Introduction: Stationary Time Series
1–3	Introduction to stationary time series  _ARMA, limit theory for stationary time series, causal relationships, HAC_
3–4	Frequency domain analysis  _Spectra; filters; transforms; nonparametric estimation_
5	Model selection and information  _Consistent estimation of number of lags, discussion of non-uniformity and post-selection inferences_
II. Mutivariate Stationary Analysis
6–7	VAR  _Definition, estimation: OLS, ML, Granger causality, impulse response functions and variance decompositions_
8	Structural VARs  _Identification, short term restrictions, long-term restrictions_
9	VAR and DSGE models  _World decomposition, fundamentality of shocks, do long-run restrictions identify anything_
10–11	Factor model and FAVAR  _Motivation, principal components, choosing number of static and dynamic factors, structural FAVAR, IV regression with factors_
III. Univariate Non-Stationary Processes
12	Asymptotic theory of empirical processes
13–14	Univariate unit roots and near unit root problem  _Unit root problem, unit root testing, confidence sets for persistence, tests for stationarity_
15	Structural breaks and non-linearity  _Testing for breaks with known and unknown dates, multiple breaks, estimating number of breaks_
IV. Multivariate Non-Stationary
16–17	Multivariate unit roots and co-integration  _Estimating cointegration relations, canonical form_
18	Persistent regressors (prediction regression)  _Limit theory, Stambaugh correction, nuisance parameter problem, conservative procedures, conditional procedures_
V. GMM and related issues
19	GMM and Simulated GMM  _GMM estimation and asymptotic theory, testing in GMM setting, simulated method of moments and time series specifics: estimation of covariance structure, initial condition problem, indirect inference_
20	Weak IV  _What is weak IV?, alternative asymptotic theory, how to detect weak IV, procedures robust to weak IV, unsolved problems._
VI. Likelihood Methods
21	Kalman filter and its applications  _State-Space models, time varying coefficients_
22	ML estimation of DSGE  _Stochastic singularities problem, misspecification and quasi-ML, identification_
23	Identification and weak identification of DSGE
VII. Bayesian Methods
24	Bayesian concepts
25	Markov Chain Monte Carlo (MCMC)  _Metropolis-Hastings, Gibbs sampler, data augmentation_
26	Estimation of DSGE models using Bayesian methods