CAO HỌC TÀI LIỆU PHÂN TÍCH STATA 2

CAO HỌC TÀI LIỆU PHÂN TÍCH STATA . NHỮNG ĐIỀU CẦN BIẾT VỀ CAO HỌC TÀI LIỆU PHÂN TÍCH STATA, LÝ THUYẾT CAO HỌC TÀI LIỆU PHÂN TÍCH STATA, BÀI GIẢNG CAO HỌC TÀI LIỆU PHÂN TÍCH STATA. TỔNG QUAN CAO HỌC TÀI LIỆU PHÂN TÍCH STATA

Pham Thi Bich Ngoc, Ph.D (University of Kiel, Germany)

FEC/Hoa Sen University

ngoc.phamthibich@hoasen.edu.vn

UNIVERSITY OF ECONOMICS HOCHIMINHCITY, June 2014

 Multicollinearity occurs when two or more

independent variables in a regression model

are highly correlated to each other

will be higher if the corresponding

independent variable is more highly correlated

to the other independent variables in the

model

 Perfect multicollinearity occurs when there is a perfect linear correlation between two or more independent variables

value in all observations

June 14 - Dr Pham Thi Bich Ngoc 3

 The symptoms of a multicollinearity problem

1 independent variable(s) considered

critical in explaining the model’s dependent variable are not

statistically significant according to the tests

2 High R2, highly significant F-test,

but few or no statistically significant

t tests

3 Parameter estimates drastically

change values and become

statistically significant when

excluding some independent

variables from the regression

June 14 - Dr Pham Thi Bich Ngoc 5

 A simple test for multicollinearity is to

conduct “artificial” regressions between each independent variable (as the “dependent”

variable) and the remaining independent

1 VIF





 VIFj = 2, for example, means that variance is

by multicollinearity

June 14 - Dr Pham Thi Bich Ngoc 7

 Although it is useful to be aware of the

presence of multicollinearity, it is not easy to

remedy severe (non-perfect) multicollinearity

new sample might help lessen multicollinearity

 Exclude the independent variables that appear

to be causing the problem

help, for example:

 using real instead of nominal economic data

 using a reciprocal instead of a polynomial

specification on a given independent variable

June 14 - Dr Pham Thi Bich Ngoc 9

 Var( u | x ) = σ2 [MLR.5]

 Homoscedasticity assumption: variance is

constant

implied that conditional on the explanatory

variables, the variance of the unobserved

error, u , was constant

 If this is not true, that is if the variance of

u is different for different values of the x ’s,

then the errors are heteroskedastic

 This provides an estimator of the variance of which is consistent

standard error for inference

heteroscedasticity-consistent standard errors

errors…]

ˆ

j

b

13

standard errors only have asymptotic

statistics formed with robust standard errors

inferences will not be correct

regress

June 14 - Dr Pham Thi Bich Ngoc

(3) Autocorrelation

 Autocorrelation occurs in time-series studies

when the errors associated with a given time

period carry over into future time periods

 For example, if we are predicting the growth of stock dividends, an overestimate in one year is likely to lead to overestimates in succeeding

years

 Test: Durbin-Watson statistic :

d  (e i  e i1)2

e i2

 , for n and K-1 d.f.

Positive Zone of No Autocorrelation Zone of Negative

autocorrelation indecision indecision autocorrelation

| _| | _| _| | _|

0 d-lower d-upper 2 4-d-upper 4-d-lower 4

Autocorrelation is clearly evident Ambiguous – cannot rule out autocorrelation Autocorrelation in not evident

June 14 - Dr Pham Thi Bich Ngoc 15

 regress lnY to lnK, lnL, lnM, horizontal, Bam, Bch

 estat vif

 calculates the centered or uncentered variance inflation

factors (VIFs) for the independent variables specified in a

linear regression model

 regress lnY to lnK, lnL, lnM, horizontal, Bam, Bch

 regress lnY to lnK, lnL, lnM, horizontal, Bam, Bch

 estat bgodfrey  Breusch-Godfrey test for

higher-order serial correlation

H0: no serial correlation

 estat dwatson  Durbin-Watson d statistic to test

for first-order serial correlation

 The Durbin-Watson statistic has a range from 0 to

4 with a midpoint of 2

For panel data:

 regress lnY to lnK, lnL, lnM, horizontal, Bam, Bch

 estat ovtest

 Ramsey regression specification-error test for omitted

variables

 Ho: model has no omitted variables

June 14 - Dr Pham Thi Bich Ngoc 19

 xtreg lnY to lnK, lnL, lnM, horizontal, Bam, Bch

 Multicollinearity: not problematic

 Pooled OLS

Hausman test

David Roodman, 2009 " How to do xtabond2: An introduction to

difference and system GMM in Stata ," Stata Journal , StataCorp LP, vol

9(1), pages 86-136, March

David Roodman, 2006 " How to Do xtabond2: An Introduction to

"Difference" and "System" GMM in Stata ," Working Papers 103, Center

for Global Development

June 14 - Dr Pham Thi Bich Ngoc 21

• Suppose y is firm output and x is a number of employees

• We have i = 1…n firms and t = 1…T time periods (year)

• A simple econometric model:

uit is a random error term: E (uit ) ~ N (0, σ2)

Assumptions: intercept and slope coefficients are constant across time and firms and that the error term captures

it it

it a a x u

Pooled regression by OLS (STATA_xtreg…)

Pooled regression by OLS may result in heterogeneity bias :

Pooled regression:

yit= a0+ a1xit+ uit

True model: Firm 1

True model: Firm 2

True model: Firm 3

True model: Firm 4

(One Way) Fixed Effects Model:

If each group (firm) to have its own intercept:

HOW?  create a set of dummy (binary) variables, one for

each firm, and include them as regressors

 This form of estimation is also known as Least Squares

Dummy Variables (LSDV)

it it

i

it a a x u

it it

N

it i

it a D a x u

Fixed Effects Estimation:

(Two Way) Fixed Effects Model:

 allow the intercept to vary across the different time periods (Two Way Fixed Effects):

it it

T

t

it i N

i

it i

0

STATA: xtreg … i.id i.year

June 14 - Dr Pham Thi Bich Ngoc 25

Fixed Effects/Within:

 discards all variation between individuals and uses only

variation over time within an individual

) (

)

(

1 0

it i

it y a x x u

STATA: xtreg … , fe

it a a x v

Random Effects Estimation:

June 14 - Dr Pham Thi Bich Ngoc 27

We assume that:

regressor) of

t independen (both

0 ) (

) (

n) correlatio group

across (no

if 0 ) (

ation) autocorrel

(no or

if 0 ) (

) components two

of nce (independe ,

, 0

) (

tic) homoscedas components

(both )

(

) (

0 ) ( )

(

2 2

2 2

i

j i

js it

j it

it

v i

it i

x E

x v

E

j i

v

v

E

j i

s t

E

j t i v

Choosing between Fixed Effects (FE) and Random Effects (RE)

1 With large T and small N there is likely to be little

difference, so FE is preferable as it is easier to compute

2 With large N and small T, estimates can differ significantly

If the cross-sectional groups are a random sample of the

population RE is preferable If not the FE is preferable

3 If the error component, vi , is correlated with x then RE is biased, but FE is not

4 For large N and small T and if the assumptions behind RE hold then RE is more efficient than FE

June 14 - Dr Pham Thi Bich Ngoc 29

Tests for the statistical significance of the difference

between the coefficient estimates obtained by FE and by

RE, under then null hypothesis that the RE estimates are

efficient and consistent, and FE estimates are inefficient

Hausman test:

STATA: hausman FE RE (LM test: xttest0 after xtreg , re)

June 14 - Dr Pham Thi Bich Ngoc 31

estimates store RE hausman FE RE

June 14 - Dr Pham Thi Bich Ngoc 32