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Unit Root Tests: Methods and Problems. Roger Perman Applied Econometrics Lecture 12. Unit Root Tests. How do you find out if a series is stationary or not?. Order of Integration of a Series. A series which is stationary after being differenced once

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unit root tests methods and problems

Unit Root Tests: Methods and Problems

Roger Perman

Applied Econometrics Lecture 12

unit root tests

Unit Root Tests

How do you find out if a series is stationary or not?

slide3

Order of Integration of a Series

A series which is stationary after being differenced once

is said to be integrated of order 1 and is denoted by I(1).

In general a series which is stationary after being

differenced d times is said to be integrated of order d,

denoted I(d). A series, which is stationary without

differencing, is said to be I(0)

=

+

+

e

®

Y

b0

Y

I

(1)

-

t

t

1

t

D

=

-

=

+

e

®

Y

Y

Y

b0

I

(0)

-

t

t

t

1

t

slide4

Informal Procedures to identify non-stationary processes

  • (1) Eye ball the data (a) Constant mean?
        • (b) Constant variance?
slide5

Informal Procedures to identify non-stationary processes

  • (2) Diagnostic test - Correlogram
  • Correlation between 1980 and 1980 + k.
        • For stationary process correlogram dies out rapidly.
        • Series has no memory. 1980 is not related to 1985.
slide6

Informal Procedures to identify non-stationary processes

  • (2) Diagnostic test - Correlogram
        • For a random walk the correlogram does not die out.
      • High autocorrelation for large values of k
slide7

Statistical Tests for stationarity: Simple t-test

Set up AR(1) process with drift (b0)

Yt = b0 + b1Yt-1 + t t ~ iid(0,σ2) (1)

Simple approach is to estimate eqn (1) using OLS and examine estimated b1

Use a t-test with null Ho: b1 = 1 (non-stationary)

against alternative Ha: b1 < 1 (stationary).

Test Statistic: TS = (b1 – 1) / (Std. Err.(b1))

reject null hypothesis when test statistic is large negative

- 5% critical value is -1.65

slide8

Statistical Tests for stationarity: Simple t-test

  • Simple t-test based on AR(1) process with drift (b0)
  • Yt = b0 + b1Yt-1 + t t ~ iid(0,σ2) (1)
  • Problem with simple t-test approach
      • (1) lagged dependent variables => b1 biased downwards in small samples (i.e. dynamic bias)
      • (2) When b1 =1, we have non-stationary process and standard regression analysis is invalid
      • (i.e. non-standard distribution)
slide9

Dickey Fuller (DF) approach to non- stationarity testing

  • Dickey and Fuller (1979) suggest we subtract Yt-1 from both sides of eqn. (1)
  • Yt - Yt-1 = b0 + b1Yt-1 - Yt-1 + t t ~ iid(0,σ2)
  • ΔYt = b0 + Yt-1 + t  = b1 –1(2)
  • Use a t-test with: null Ho:  = 0 (non-stationary or Unit Root)
      • against alternative Ha:  < 0 (stationary).
  • - Large negative test statistics reject non- stationarity
  • - This is known as unit root test since in eqn. (1) Ho: b1=1.
variants of df test
Variants of DF test

Three different regression can be used to test the presence of a unit root

The difference between the three regressions concerns the

presence of deterministic elements b0 and b2t.

1 – For testing if Y is a pure Random Walk

2 – For testing if Y is a Random Walk with Drift

3 – For testing if Y is a Random walk with Drift and Deterministic Trend

slide11

The simplest model (appropriate only if you think there are no

other terms present in the ‘true’ regression model)

Use the t statistic and compare it with the the table of critical values computed by Dickey and Fuller. If your t value is outside the confidence interval, the null hypothesis of unit root is rejected

Statistic

slide12

A more general model (allowing for ‘drift’)

Statistic - Use the F statistic to check if  = b0 = 0 using the

non standard tables

Statistic - use the t statistic to check if =0 , again using

non-standard tables

slide13

Example

Sample size of n = 25 at 5% level of significance for eqn. (2)

τμ-critical value = -3.00 t-test critical value = -1.65

Δpt-1 = -0.007 - 0.190pt-1

(-1.05) (-1.49)

 = -0.190 τμ = -1.49 > -3.00

hence cannot reject H0 and so unit root.

slide14

Incorporating time trends in DF test for unit root

  • Some time series clearly display an upward or downward trend (non-stationary mean).
  • Should therefore incorporate trend in the regression used for the DF test.
  • ΔYt = b0 + Yt-1 + b2 trend + t (4)
  • It may be the case that Yt will be stationary around a trend. Although if a trend is not included series is non-stationary.
slide15

Different DF tests – Summaryt-type test

ττΔYt = b0 + βYt-1 + b2trend+ t

(a) Ho:β = 0Ha:β < 0

τμΔYt = b0 + βYt-1+ t

(b) Ho: β = 0 Ha: β < 0

τΔYt = βYt-1 + t

(c)Ho: β = 0 Ha: β < 0

Critical values from Fuller (1976)

slide16

Different DF tests – Summary F-type test

Φ3ΔYt = b0 + Yt-1 + b2 trend+ t

(a) Ho:β = b2=0Ha: 0 and/or b20

Φ1 ΔYt = b0 + Yt-1+ t

(b) Ho:= b0=0Ha: 0 and/or b0 0

Critical values from Dickey and Fuller (1981)

slide17

Summary of Dickey-Fuller Tests

(Critical values for n = 100)

slide18

Augmented Dickey Fuller (ADF) test for unit root

Dickey Fuller tests assume that the residuals t in the DF regression are non- autocorrelated.

Solution: incorporate lagged dependent variables.

For quarterly data add up to four lags.

ΔYt = b0 + Yt-1 + θ1ΔYt-1 + θ2ΔYt-2 + θ3ΔYt-3 + θ4ΔYt-4 + t (3)

Problem arises of differentiating between models.

Use a general to specific approach to eliminate insignificant variables

Check final parsimonious model for autocorrelation.

Check F-test for significant variables

Use Information Criteria. Trade-off parsimony vs. residual variance.

slide19

Consider The Following Series and Its Correlogram

This variable Y is clearly trended and you have to determine if this trend is stochastic

or deterministic. After having created the difference variable Y estimate the

following model, with as many lags of Y as you think appropriate.

(in the example I choose 4 lags of the variable Y)

slide23

Choose Between Alternative Models - The Model-Progress Results

Both the F-Test and the Schwarz Information Criteria indicates

that MODEL 4 is the one to be preferred

slide24

Unit Root Testing

After having estimated, according to the previous analysis, the following equation

the relevant hypotheses to examine are (in this particular case)

b

=

H

:

b

,

b

,

b

,

0

,

0

0

0

2

0

v

b

¹

H

:

b

,

b

,

b

,

0

,

0

1

0

2

0

To do this perform an F-Test and use the statistic

slide25

PcGive output of test result:

Wald test for linear restrictions: Subset

LinRes F( 2,493) = 5.0781 [0.0066] **

Be careful here. The value 5.0781 is not significant at the 5% critical value, although PcGive marks it as significant (it is using the conventional F distribution).

Therefore we cannot reject the null hypothesis, and so infer that we do not have a deterministic time trend in the equation. Hence, we can continue the analysis using

and then use

statistic - use the F statistic to check if  = b0 = 0 using the

non standard tables

statistic - use the t statistic to check if =0 , again using

non-standard tables

slide26

The t-stat cannot reject the null hypothesis of Unit Root while the F-stat

rejects the null hypothesis that the drift is equal to zero. Therefore we can

conclude that the model most likely to describe the true DGP is

slide27

Look at the Series – Is there a Trend?

Yes

No

Estimate

Estimate

Use

to test

Use

to test

b

=

H

:

b

,

0

,

0

0

0

b

=

H

:

b

,

b

,

b

,

0

,

0

v

0

0

2

0

v

b

¹

H

:

b

,

0

,

0

1

0

b

¹

H

:

b

,

b

,

b

,

0

,

0

1

0

2

0

Accept

Reject

Reject

Accept

Pure Random Walk

test =0 using the

t-stat. from step 1

using

test =0 using the

t-stat. from step 1

using

Reject

Accept

Reject

Accept

Unit Root +Trend

No Unit Root

Stable Series,

use normal test

to check the drift

Random

Walk + Drift

Use

Normal Test procedure

to determine the

presence of

Time trend or Drift

To determine if there

is a drift as well

slide28

Alternative statistical test for stationarity

One further approach is the Sargan and Bhargava (1983) test which uses the Durbin-Watson statistic.

If Yt is regressed on a constant alone, we then examine the residuals for serial correlation.

Serial correlation in the residuals (long memory) will fail the DW test and result in a low value for this test.

This test has not proven so popular.

slide29

Testing Strategy for Unit Roots

  • Three main aspects of Unit root testing
  • - Deterministic components (constant, time trend).
  • - ADF Augmented Dickey Fuller test - lag length
  • - use F-test or Schwarz Information Criteria
  • - In what sequence should we test?
  • - Phi and tau tests
slide30

Testing Strategy for Unit Roots

  • Formal Strategy
  • (A) Set up Model
      • (1) Use informal tests – eye ball data and correlogram
      • (2) Incorporate Time trend if data is upwards trending
      • (3) Specification of ADF test
      • – how many lags should we incorporate to avoid serial correlation?
slide31

Example- Real GDP (2000 Prices) Seasonally Adjusted

    • (1) Plot Time Series - Non-Stationary
  • (i.e. time varying mean and correlogram non-zero)

GDP

Time

r

k

slide32

Unit Root Testing

    • (1) Plot First Difference of Time Series - Stationary
  • (i.e. constant mean and correlogram zero)

Time

r

k

slide33

Unit Root Testing

(2) Incorporate Linear Trend since data is trending upwards

slide34

Unit Root Testing

    • (3) Determine Lag length of ADF test
  • Estimate general model and test for serial correlation
  • EQ ( 1) ΔYt = b0+b2 trend+ Yt-1 + θ1ΔYt-1 + θ2ΔYt-2 + θ3ΔYt-3 + θ4ΔYt-4 + t
  • EQ( 1) Modelling DY by OLS (using Lab2.in7)
  • The estimation sample is: 1956 (2) to 2003 (3) n = 190
  • Coefficient Std.Error t-value t-prob Part.R^2
  • Constant 0.538887 0.3597 1.50 0.136 0.0121
  • Trend 0.00701814 0.004836 1.45 0.148 0.0114
  • Y_1 -0.0156708 0.01330 -1.18 0.240 0.0075
  • DY_1 -0.0191048 0.07395 -0.258 0.796 0.0004
  • DY_2 0.137352 0.07297 1.88 0.061 0.0190
  • DY_3 0.188071 0.07354 2.56 0.011 0.0345
  • DY_4 0.0474897 0.07473 0.635 0.526 0.0022
  • AR 1-5 test: F(5,178) = 1.7263 [0.1308]
  • Test accepts null of no serial correlation.
  • Nevertheless we use F-test and Schwarz Criteria to check model.
slide35

Unit Root Testing

(3) Determine Lag length of ADF test

Model

EQ ( 1) ΔYt = b0+b2 trend+ Yt-1 + θ1ΔYt-1 + θ2ΔYt-2 + θ3ΔYt-3 + θ4ΔYt-4 + t

EQ ( 2) ΔYt = b0+b2 trend+ Yt-1 + θ1ΔYt-1 + θ2ΔYt-2 + θ3ΔYt-3 + t

EQ ( 3) ΔYt = b0+b2 trend+ Yt-1 + θ1ΔYt-1 + θ2ΔYt-2 + t

EQ ( 4) ΔYt = b0+b2 trend+ Yt-1 + θ1ΔYt-1 + t

EQ ( 5) ΔYt = b0+b2 trend+ Yt-1 + t

Use both the F-test and the Schwarz information Criteria (SC).

Reduce number of lags where F-test accepts.

Choose equation where SC is the lowest

i.e. minimise residual variance and number of estimated parameters.

slide36

Unit Root Testing

(3) Determine Lag length of ADF test

Progress to date

Model T p log-likelihood Schwarz Criteria

EQ( 1) 190 7 OLS -156.91128 1.8450

EQ( 2) 190 6 OLS -157.12068 1.8196

EQ( 3) 190 5 OLS -160.37203 1.8262

EQ( 4) 190 4 OLS -162.16872 1.8175

EQ( 5) 190 3 OLS -162.17130 1.7899

Tests of model reduction

EQ( 1) --> EQ( 2): F(1,183) = 0.40382 [0.5259]Accept model reduction

EQ( 1) --> EQ( 3): F(2,183) = 3.3947 [0.0357]*Reject model reduction

EQ( 1) --> EQ( 4): F(3,183) = 3.4710 [0.0173]*

EQ( 1) --> EQ( 5): F(4,183) = 2.6046 [0.0374]*

Some conflict in results. F-tests suggest equation (2) is preferred to equation (1) and equation (3) is not preferred to equation (2).

Additionally, the relative performance of these three equations is confirmed by information criteria.

Therefore adopt equation (2).

slide37

Unit Root Testing

(B) Conduct Formal Tests

EQ( 2) Modelling DY by OLS (using Lab2.in7)

The estimation sample is: 1956 (2) to 2003 (3)

Coefficient Std.Error t-value t-prob Part.R^2

Constant 0.505231 0.3552 1.42 0.157 0.0109

Trend 0.00655304 0.004772 1.37 0.171 0.0101

Y_1 -0.0141798 0.01307 -1.08 0.279 0.0064

DY_1 -0.0119522 0.07297 -0.164 0.870 0.0001

DY_2 0.142437 0.07241 1.97 0.051 0.0206

DY_3 0.185573 0.07332 2.53 0.012 0.0336

AR 1-5 test: F(5,179) = 0.68451 [0.6357]

Main issue is serial correlation assumption for this test.

Can we accept the null hypothesis of no serial correlation? Yes!

slide38

Unit Root Testing

Apply F-type test- Include time trend in specification

Φ3: ΔYt = b0 + b2 trend + Yt-1 + θ1ΔYt-1 + θ2ΔYt-2 + θ3ΔYt-3 + t(a) Ho:= b2=0Ha:β 0 and/or b20

PcGive Output : Test/Exclusion Restrictions.

Test for excluding: [0] = Trend [1] = Y_1

F(2,184) = 2.29 < 6.39 = 5% C.V. (by interpolation).

Hence accept joint null hypothesis of unit root and no time trend

(next test whether drift term is required).

NB Critical Values (C.V.) from Dickey and Fuller (1981) for Φ3

Sample Size (n) 25 50 100 250 500

C.V. at 5% 7.24 6.73 6.49 6.34 6.30

slide39

Unit Root Testing

Apply F-type test- Exclude time trend from specification

Φ1: ΔYt = b0 + Yt-1+ θ1ΔYt-1 + θ2ΔYt-2 + θ3ΔYt-3 + t

(b) Ho: = b0=0Ha: 0 and/or b0 0

PcGive Output : Test/Exclusion Restrictions.

Test for excluding: [0] = Constant[1] = Y_1

F(2,185) = 10.27 > 4.65 = 5% C.V.

Hence reject joint null hypothesis of unit root and no drift.

NB Critical Values (C.V.) from Dickey and Fuller (1981) for Φ1

Sample Size (n) 25 50 100 250 500

C.V. at 5% 5.18 4.86 4.71 4.63 4.61

slide40

Unit Root Testing

Apply t-type test(τμ)

τμΔYt = b0 + Yt-1+ θ1ΔYt-1 + θ2ΔYt-2 + θ3ΔYt-3 + t

(b) Ho:  = 0 Ha:  < 0

τμ = 1.64 > -2.88 = 5% C.V.

Hence accept null of unit root.

N.B. Critical Values (C.V.) from Fuller (1976) for τμ

Sample Size (n) 25 50 100 250 500

C.V. at 5% -3.00 -2.93 -2.89 -2.88 -2.87

slide41

Unit Root Testing

EQ(2a) Modelling DY by OLS (using Lab2.in7)

The estimation sample is: 1956 (2) to 2003 (3)

Coefficient Std.Error t-value t-prob Part.R^2

Constant 0.0535255 0.1343 0.399 0.691 0.0009

Y_1 0.00352407 0.002150 1.64 0.103 0.0143

DY_1 -0.0218516 0.07279 -0.300 0.764 0.0005

DY_2 0.131601 0.07215 1.82 0.070 0.0177

DY_3 0.172115 0.07283 2.36 0.019 0.0293

AR 1-5 test: F(5,180) = 0.50464 [0.7725]

τμ= 1.64 > -2.88 (5% C.V.) hence we can not reject the null of unit root.

slide43

Problem Number 1: Structural Breaks

Perron (1989) - He argues that most macroeconomic variables

are not unit root processes. They are Trend Stationary with

Structural Breaks

  • For example
  • 1929 Depression
  • Oil Shocks
  • Technological Change

All these events have changed the mean of a process like GDP

If you do not recognize the structural break, you’ll find unit root

where there is not

With Structural Change All Unit Root Tests Are Biased

Towards the Non Rejection of a Unit Root

slide44

Unit Root Test For Y

Dickey-Fuller test for Y; DY on

Variable Coefficient Std.Error t-value

Y_1 -0.065307 0.037352 -1.748

\sigma = 1.16902 DW = 2.35 DW(Y) = 0.2242 DF(Y) = -1.748

Critical values used in DF test: 5%=-1.943 1%=-2.587

RSS = 132.5618324 for 1 variables and 98 observations

Information Criteria:

SC = 0.348867 HQ = 0.333159 FPE=1.38056 AIC = 0.32249

The Unit Root Hypothesis is not rejected.

Perron proposed a method to overcome this problem - But you

need to know when the structural break happened

slide45

Consider The Following Variable

=

+

+

e

Y

0

.

5

Y

D

-

1

t

t

t

=

0

for

t

0

.

.

49

=

D

t

=

2

for

t

50

.

.

100

slide46

Problem Number Two : Low Power

The Power of a test is the probability of rejecting a false

Null Hypothesis -

Unit Root Tests

  • Low Power to Distinguish Between Unit and near Unit Root
  • Low Power to Distinguish Between Trend and Drift

Y is a unit root series;

Z is a near-unit root series

slide47

Is  = 0 in ΔYt = b0 + Yt-1 + t

  • Test result is based on the standard error of 
  • - Measure of how accurate is our estimated coefficient
  • - with increasing observations we become more certain.
  • In this case, power of the test is the ability to reject the null of non-stationarity when it is false (equivalently, the ability to accept alternative hypothesis of stationarity).
  • Low power implies a series may be stationary but Dickey-Fuller test suggests unit root.
  • Low power is especially a problem when series is stationary but close to being unit root.
  • One solution to low power is to increase the number of observations by increasing the span of data. However, there may be differences in economic structure or policy which should be modelled differently. Alternative solution to low power is a number of joint ADF tests.
  • - Take information from a number of countries.
  • - And pool coefficients. (i.e. combine information).