Introduction to Econometrics - Econometric Analysis of Panel Data - Lecture Slides, Slides of Econometrics and Mathematical Economics

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Econometric Analysis of Panel Data

Econometric Analysis of Panel Data

1. Introduction to Econometrics

Measurement as Observation

Population Measurement

Theory

Characteristics

Behavior Patterns

Choices

Inference

Population Measurement

Econometrics

Characteristics

Behavior Patterns

Choices

Nonparametric Regression

Nonparametric Regression of Investment on Capital Stock

KC

500

1000

1500

2000

0 0 500 1000 1500 2000 2500

Invstmnt

What are the assumptions?

What are the conclusions?

λ (^)  λ 

λ =

N

i=1 i i

N

i=1 i

i

.

Kernel Regression

w (z)y ˆ F(z)=

w (z)

1 x -z

( ) K

.9Q / N

(t) (t)[1 (t)]

exp(t) (t)

1 exp(t)

i

w z

K

Semiparametric Regression

 Investment

i,t

= a + b*Capital

i,t

+ u

i,t

 Median[u

i,t

| Capital

i,t

] = 0

Least Absolute Deviations Regression of I on C

C

320

640

960

1280

1600

0 0 500 1000 1500 2000 2500

I ILAD

= (^) Invstmnt

− ∑

N

a,b (^) i 1 i i

Least Absolute Deviations

ˆ ˆ F(x)=a+bxˆ

ˆ ˆa,b=ArgMin |y a-bx |

Estimation Platforms

 Model based

 Kernels and smoothing methods (nonparametric)

 Moments and quantiles (semiparametric)

 Likelihood and M- estimators (parametric)

 Methodology based (?)

 Classical – parametric and semiparametric

 Bayesian – strongly parametric

The Sample and Measurement

Population Measurement

Theory

Characteristics

Behavior Patterns

Choices

Bayesian Inference

Population Measurement

Econometrics

Characteristics

Behavior Patterns

Choices

Sharp, ‘exact’ inference about

only the sample – the ‘posterior’

density.

Data Structures

 Observation mechanisms

 Passive, nonexperimental

 Active, experimental

 The ‘natural experiment’

 Data types

 Cross section

 Pure time series

 Panel – longitudinal data

 Financial data

Econometric Models

 Linear; static and dynamic

 Discrete choice

 Censoring and truncation

 Structural models and demand systems

Estimation Methods and Applications

 Least squares etc. – OLS, GLS, LAD, quantile

 Maximum likelihood

 Formal ML

 Maximum simulated likelihood

 Robust and M- estimation

 Instrumental variables and GMM

 Bayesian estimation – Markov Chain Monte

Carlo methods

Econometric Analysis of Panel Data

This Course

Prerequisites

 Econometrics I or equivalent

 Mathematical statistics

 Matrix algebra

We will do some proofs and derivations

We will also examine empirical applications