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 |
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