Econometric Analysis of Panel Data .


33 views
Uploaded on:
Category: People / Lifestyle
Description
Arbitrary Regressors. Pooled (Constant Effects) ModelOther established presumptions remained.OLS is one-sided; Instrumental variables estimation ought to be used.IV estimator is reliable.. Consistent Effects Model. Instrumental Variables Estimation. Consistent Effects Model. Instrumental Variables EstimationInstrumental Variables: ZiIncluded Instruments: X1i
Transcripts
Slide 1

Econometric Analysis of Panel Data Random Regressors Pooled (Constant Effects) Model Instrumental Variables Fixed Effects Model Random Effects Model Hausman-Taylor Estimator

Slide 2

Random Regressors Pooled (Constant Effects) Model Other established suppositions remained. OLS is one-sided; Instrumental factors estimation ought to be utilized. IV estimator is reliable.

Slide 3

Constant Effects Model Instrumental Variables Estimation

Slide 4

Constant Effects Model Instrumental Variables Estimation Instrumental Variables: Z i Included Instruments: X1 i # Z i ≥ # W i

Slide 5

Constant Effects Model Instrumental Variables Estimation

Slide 6

Constant Effects Model Instrumental Variables Estimation HAC Variance-Covariance Matrix

Slide 7

Constant Effects Model Hypothesis Testing of Instrumental Variables Test for Endogeneity Test for Overidentification Test for Weak Instruments

Slide 8

Random Regressors Fixed Effects Model Other traditional suspicions remained. Can not evaluate the parameters of time-invariant regressors, regardless of the possibility that they are associated with model blunder. The arbitrary regressors x2 must be time-shifting.

Slide 9

Fixed Effects Model The Model Instrumental Variables #Z i ≥ #X i ( Z i should be time variation)

Slide 10

Fixed Effects Model Within Estimator Panel-Robust Variance-Covariance Matrix

Slide 11

Example: Returns to Schooling Cornwell and Rupert Model (1988) Data (575 people more than 7 ears) Dependent Variable y it : LWAGE = log of wage Explanatory Variables x it : Time-Variant Variables x1 it : EXP = work understanding (+EXP 2 )  exogenous WKS = weeks worked  endogenous OCC = occupation, 1 if hands on  IV IND = 1 if fabricating industry  IV SOUTH = 1 if dwells in south  IV SMSA = 1 if lives in a city (SMSA)  IV MS = 1 if wedded  IV UNION = 1 if wage set by union contract  IV Time-Invariant Variables x2 i : ED = years of training  endogenous FEM = 1 if female BLK = 1 if individual is dark

Slide 12

Random Regressors Random Effects Model Other traditional presumptions remained. Mundlak approach might be utilized when Instrumental factors must be utilized if

Slide 13

Random Effects Model The Model

Slide 14

Random Effects Model (Partial) Within Estimator Panel-Robust Variance-Covariance Matrix

Slide 15

Example: Returns to Schooling Cornwell and Rupert Model (1988) Data (575 people more than 7 years) Dependent Variable y it : LWAGE = log of wage Explanatory Variables x it : Time-Variant Variables x1 it : EXP = work understanding (+EXP 2 )  exogenous WKS = weeks worked  endogenous OCC = occupation, 1 if industrial  IV IND = 1 if producing industry  IV SOUTH = 1 if dwells in south  IV SMSA = 1 if lives in a city (SMSA)  IV MS = 1 if wedded  IV UNION = 1 if wage set by union contract  IV Time-Invariant Variables x2 i : ED = years of instruction  endogenous FEM = 1 if female  IV BLK = 1 if individual is dark  IV

Slide 16

Hausman-Taylor Estimator The Model Time-variation Variables: x1 it , x2 it Time-invariant Variables:x3 i , x4 i Fixed impacts model can not gauge b 3 and b 4; Random impacts display has irregular regressors: x2 and x4 related with u.

Slide 17

Hausman-Taylor Estimator Fixed Effects Model

Slide 18

Hausman-Taylor Estimator Fixed Effects Model Within Residuals

Slide 19

Hausman-Taylor Estimator Random Effects Model

Slide 20

Hausman-Taylor Estimator Instrumental Variables Hausman-Taylor (1981) Amemiya-Macurdy (1986)

Slide 21

Hausman-Taylor Estimator Instrumental Variable Estimation

Slide 22

Example: Returns to Schooling Cornwell and Rupert Model (1988) Data (575 people more than 7 ears) Dependent Variable y it : LWAGE = log of wage Explanatory Variables x it : Time-Variant Variables x1 it : EXP = work encounter  endogenous (+EXP 2 ) WKS = weeks worked  endogenous OCC = occupation, 1 if hands on, IND = 1 if fabricating industry SOUTH = 1 if dwells in south SMSA = 1 if lives in a city (SMSA) MS = 1 if wedded  endogenous UNION = 1 if wage set by union contract  endogenous Time-Invariant Variables x2 i : ED = years of instruction  endogenous FEM = 1 if female BLK = 1 if individual is dark

Recommended
View more...