SES # | TOPICS |
---|---|
I. Introduction: Stationary Time Series | |
1–3 | Introduction to stationary time series |
3–5 | 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 |
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 |
17 | Persistent regressors (prediction regression) Limit theory, Stambaugh correction, nuisance parameter problem, conservative procedures, conditional procedures |
V. GMM and related issues | |
18 | 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 |
19 | Weak IV What is weak IV?, alternative asymptotic theory, how to detect weak IV, procedures robust to weak IV, unsolved problems. |
VI. Likelihood Methods | |
20–21 | 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 |