DATE

TOPIC

HANDOUTS (data & dofiles)

ASSIGNMENT(S)

Week 1

Useful Information: Replication, dofiles and log files, CLARIFY and simulation

Week 2

Maximum Likelihood Estimation: Theory and mechanics

Weeks 3 & 4

Binary Dependent Variables: Logit, probit, scobit, heteroskedastic probit, rare events logit

Weeks 5 & 6

Discrete Choice Models: A general framework for discrete choice models. Multinomial logit, conditional logit, nested logit, multinomial probit, and mixed logit. Simulated maximum likelihood.

Week 7

Ordered Response Models: Ordered probit and logit, generalized ordered logit, heteroskedastic ordered probit

Week 8

Event Count Models: Poisson model, negative binomial model, generalized event count model, hurdle model, zeroinflated models

Week 9

Continuous Time Duration Models: Basic components of duration analysis. Nonparametric models, parametric models (exponential, weibull, loglogistic, generalized gamma, etc.), semiparametric models (Cox)
Note: For notes and exercises engaging discrete time duration models (logit, probit, clog log) and advanced duration models (frailty, competing risks, repeated events, split population, selection) see weeks 10 and 11 on Matt Golder's Methods IV page. 
Week 10

Tobit Models: Truncated and censored data

Week 11

Selection Models: Heckman model (twostep and MLE), bivariate probit, bivariate probit with partial observability, bivariate probit with sample selection

Weeks 12 & 13

Time Series: Stationary and nonstationary data, DurbinWatson and LM tests for serial correlation, CochraneOrcutt and PraisWinsten procedures, lags (finite distributed, ADL, etc.), error correction models, impulse and unit response functions, AR and MA error processes, tests for stationarity and unit roots, drifts and trends, cointegration, structural stability, spurious regression
