Sumários

Binary Dependent Variable Models

22 junho 2022, 12:00 • Hugo Castro Silva

Binary Dependent Variable Models

  • Limited dependent variable models
  • Examples of research questions with limited y
  • Why not use linear regression?
  • The linear probability model
  • Alternatives to LPM: Logit and Probit
  • The latent variable model
  • Estimating logit and probit: maximum likelihood estimation
  • Interpreting logit and probit: partial/marginal effects
    • Partial effect at the average
    • Average partial effect
  • Goodness of fit in binary dependent variable models
  • LPM, Logit, or Probit?

Not Taught.

15 junho 2022, 12:00 • Hugo Castro Silva

Class not taught because of illness

Instrumental Variables and Two Stage Least Squares

8 junho 2022, 12:00 • Hugo Castro Silva

Instrumental Variables and Two Stage Least Squares

1. Instrumental Variables
  • Motivation
  • IV Assumptions
  • The IV Estimator
  • IV Estimation of the Multiple Regression Model
2. Two Stage Least Squares
  • Using multiple instruments
  • IV/2SLS with poor instruments
  • Testing for endogeneity
  • Testing overidentification restrictions

Panel Data Analysis II —Fixed Effects, Random Effects, Correlated Random Effects

1 junho 2022, 12:00 • Hugo Castro Silva

Panel Data Analysis II — Fixed Effects, Random Effects, Correlated Random Effects

1. Fixed effects
  • The within transformation.
  • Time-demeaning variables.
  • Estimating the within transformation with pooled OLS (POLS)
  • Application (example 14.1)
  • Effects that change over time, using FE
  • Application (example 14.2)
  • The dummy variable regression
  • Fixed Effects vs First Differencing
  • Fixed effects with unbalanced panels
2. Random effects
  • Random effects: motivation
  • Random effects: quasi-demeaned transformation
  • Application (Example 14.4)
  • RE vs POLS
  • RE vs FE
3. The Correlated Random Effects approach
4. Applying data panel methods to other data structures

Panel Data Analysis I — First Differences (FD)

25 maio 2022, 12:00 • Hugo Castro Silva

Panel Data Analysis I — First Differences (FD)

What is Panel Data?
  • Panel data in the long format
  • Advantages and disadvantages of panel data
  • The unobserved effects model
First Differences
  • The first difference (FD) transformation
  • FD interpretation
  • FD limitations
  • Policy analysis with FD
  • Differencing and effects that change over time
  • Application examples
  • Serial correlation in panel data: what it is, what are its consequences
  • Testing for serial correlation after a first differences regression
  • Positive vs. negative serial correlation
  • Correcting for serial correlation: clustered standard errors.
  • FD limitations