ApEc 8212 Applied Econometrics - Lecture #28
Time Series Analysis VII: Trends in Time Series Data
(Enders, Chap. 4, Sections 1-7)
I. Deterministic and Stochastic Trends
In general, stochastic difference equations have 3 parts:
yt = trend + stationary comp

ApEc 8212 Econometric Analysis - Lecture #18
Density Estimation and Nonparametric Regression
Cameron and Trivedi, Chapter 9, Sections 1 - 6.
I. Introduction
Narrow-minded linear models, that is linear models
without higher ordered (e.g. squared) or intera

Apec 8212 Econometric Analysis Lecture #20
Program Evaluation 1: Estimating Program Impacts
when Unconfounded Assignment Holds
(Wooldridge, Chapter 21, Sections 1-3)
I. Introduction
Researchers and policymakers often want to know whether
a certain governm

ApEc 8212 Econometric Analysis - Lecture #8
Simultaneous Equation Models (Wooldridge, Ch. 9)
I. Introduction
This is the last lecture on systems of equations (not
counting future lectures on panel data). Simultaneous
equation models are systems of equatio

ApEc 8212 Econometrics Analysis - Lecture #2
Review of Asymptotic Theory (Wooldridge, Chap. 3)
This lecture reviews basic results in asymptotic
theory, which most of you have seen before in some
form (Appendix D of Greene, 2012).
This material is not very

ApEc 8212 Econometric Analysis - Lecture #9
Panel Data I: Fixed Effects and Random Effects
(Wooldridge, Chapter 10)
I. Introduction to Panel Data
Panel data are repeated observations on the same
unit over time. They are useful for several reasons.
First,

ApEc 8212 Econometric Analysis - Lecture #19
Semiparametric Estimation of Partially Linear,
Discrete Choice, and Selection Models
(Cameron & Trivedi: 9.7, 14.7 and 16.9;
Pagan and Ullah, Chapters 5, 7 and 8)
I. Introduction
This lecture explains how to es

ApEc 8212 Econometric Analysis II Lecture #3
Review of Linear Models and OLS Estimation
(Wooldridge, Chapter 4)
This lecture will review much of the material you had
in Apec 8211, but in a way that will prepare you for
topics that will be covered this sem

ApEc 8212 Econometric Analysis II - Lecture #12
Maximum Likelihood Estimation (MLE)
Reading: Wooldridge, Chapter 13 (Sections 1-8)
I. Introduction
Maximum likelihood estimation (MLE) methods are
one type of M-Estimation method. In linear models,
it is rar

ApEc 8212 Econometric Analysis - Lecture #6
Estimating Systems of Equations by OLS and GLS
I. Introduction and Examples
Sometimes we want to estimate more than one
equation, and the equations we want to estimate are
closely related. OLS and GLS estimation

ApEc 8212: Econometric Analysis II - Lecture #4
Instrumental Variables (Part 1)
Instrumental variable (IV) methods are used to deal
with problems of omitted variable bias, measurement
error and simultaneity. They are used very often in
applied econometric

ApEc 8212 Econometric Analysis - Lecture #17
Bootstrap Methods (Cameron and Trivedi, Ch. 11)
I. Introduction
Bootstrapping is a very general approach to
estimate a wide variety of statistics that we might be
interested in. It is often referred to as a res

ApEc 8212 Econometric Analysis - Lecture #10
Panel Data II: Additional Topics (Wooldridge, Ch. 11)
I. Introduction
This lecture examines five additional topics regarding
panel data models:
1. Generalized Method of Moments (GMM)
Estimation of Panel Data Mo

ApEc 8212 Econometric Analysis II - Lecture #14
Discrete Choice Models
(Wooldridge, Chapter 15 (Sects. 1-6, 8) & Chapter 16)
I. Introduction
There are many situations in which the dependent
variable y takes only two values, such as 0 or 1:
1. Employment a

Apec 8212 Econometric Analysis Lecture 30
Time Series Analysis IX: Introduction to Cointegration
I. Introduction
In Lecture 28, we learned how to identify and estimate
univariate time series processes in which the variable
was not stationary. For models w

ApEc 8212 Econometric Analysis II - Lecture #16
Models of Sample Selection and Attrition
(Wooldridge, Chapter 19, Sections 1-6)
I. Introduction
So far in this class we have assumed that the data we
have are a random sample from some underlying
population.