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WORKSHOP on
Introductory Econometrics with EViews
Asst. Prof. Dr. Kemal BağzıbağlıDepartment of Economic
Res. Asst. Pejman BahramianPhD Candidate, Department of Economic
Res. Asst. Gizem UzunerMSc Student, Department of Economic
EViews Workshop Series Agenda1. Introductory Econometrics with EViews
2. Advanced Time Series Econometrics with EViewsa. Unit root test and cointegrationb. Vector Autoregressive (VAR) modelsc. Structural Vector Autoregressive (SVAR) modelsd. Vector Error Correction Models(VECM)e. Autoregressive Distributed Lag processes
3. Forecasting, and Volatility Models with EViewsa. Forecastingb. Volatility modelsc. Regime Switching Models
2
Part 1 - Outline1. Violation of Classical Linear Multiple Regression
(CLMR) Assumptions
2. “Stationarity is Job 1!”
3. Univariate Time Series Modellinga. Autoregressive Integrated Moving Average (ARIMA) model
a. Heteroskedasticityb. Multicollinearity
c. Model Misspecificationd. Autocorrelation
3
1. Violation of Classical Linear Multiple
Regression (CLMR) Assumptions
Multiple Regression Model
● n observations on y and x:● α & βi: unknown parameters
Deterministic components Stochastic component
5
1) The error term (ut) is a random variable with E(ut )=0.2) Common (constant) Variance. Var(ut ) = σ2 for all i.3) Independence of ut and uj for all t.4) Independence of xj
● ut and xj are independent for all t and j.
5) Normality● ut are normally distributed for all t.● In conjunction with assumptions 1, 2 and 3;
ut 〜 IN (0, σ2)
Assumptions
6
HETEROSKEDASTICITY (nonconstant variance) var(ut ) = E(ut
2) = σ2 for all t (similar distribution) Homoskedasticity:
● σ12 = σ2
2 = … = σ2n
● Constant dispersion of the error terms around their mean zero
Violation of Basic Model Assumptions
7
Heteroskedasticity (cont.)● Rapidly increasing or
decreasing dispersion heteroskedasticity?
● Variances are different because of changing dispersion
● σ12 ≠ σ2
2 ≠ ...≠ σ2n Var(ut )=
σt2
● One of the assumptions is violated! 8
Heteroskedasticity (cont.) Residuals increasing by x
heteroskedasticity?
9
Consequences of Heteroskedasticity★ The ordinary least squares (OLS) estimators
are still unbiased but inefficient.➢ Inefficiency: It is possible to find an alternative
unbiased linear estimator that has a lower variance than the OLS estimator.
10
Consequences of Heteroskedasticity (cont.)
Effect on the Tests of Hypotheses★ The estimated variances and covariances of the
OLS estimators are biased and inconsistent➢ invalidating the tests of hypotheses (significance)
Effect on Forecasting★ Forecasts based on the estimators will be unbiased★ Estimators are inefficient
➢ forecasts will also be inefficient 11
1. Park Test is a two-stage procedureStage 1: ● Run an OLS regression disregarding the
heteroskedasticity question. ● Obtain ut from this regression;
Stage 2: ● if β is statistically significant, there is heteroskedasticity.
Lagrange Multiplier (LM) Tests for Heteroskedasticity
12
Park Test in EViewsls compensation c productivity
13
Park Test in EViews (cont.)
14
Park Test in EViews (cont.)
u=0
15
Park Test in EViews (cont.)u2=u^2
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Park Test in EViews (cont.)lnu2=log(u2) lnproductivity=log(productivity)
17
● Probability value (p-value) of lnproductivity (0.5257) is greater than the critical value of 0.05
● Statistically insignificanthomoskedasticity
Park Test in EViews (cont.)
18
2. Glejser Test is similar in spirit to the Park test.● Glejser (1969) suggested estimating regressions
of the type;IûtI = α + βXt IûtI = α + β/Xt IûtI = α + β√Xt and so on
● Testing the hypothesis β=0
Detection of Heteroskedasticity (cont.)
19
Glejser Test in EViewsgenr au=@abs(u)
20
Glejser Test in EViews (cont.)
Heteroskedasticity?
ls au c productivity
21
Glejser Test in EViews (cont.)
22
ls au c 1/productivity ls au c @sqrtproductivity
Glejser Test in EViews (cont.)ls compensation c productivity
23
Glejser Test in EViews (cont.)
24
3. White’s Test● Recommended over all the previous tests
Step 1: Obtain by OLSStep 2: Compute the residual and square it
Detection of Heteroskedasticity (cont.)
25
3. White’s Test (cont.)Step 3: Regress the squared residual against a constant, X2t, X3t etc. (auxiliary equation)
Step 4: Compute the statistic nR2
● n: sample size, R2: unadjusted R2 from S.3
Detection of Heteroskedasticity (cont.)
26
3. White’s Test (cont.)Step 5: Reject the null hypothesis that
● if ○ Upper a percent point on the chi-square dist. with 5 d.f.
● If the null hypothesis is not rejected○ the residuals are homoskedastic
Detection of Heteroskedasticity (cont.)
27
White Test in EViews
28
Solutions to the Heteroskedasticity Problem
➔ Taking the logarithm of Yt and Xt ◆ variance becomes smaller.
➔ Use the weighted least squares (WLS)
◆ Better than the first solution◆ Guaranties homoskedasticity.
29
Solutions to the Heteroskedasticity Problem (cont.)
Graphical Method
● Check the residuals (i.e.
error variance)○ linearly increasing with xt
● WLS
30
Solutions to the Heteroskedasticity Problem (cont.)
● Not linearly but
quadratically increasing error variance
31
Solutions to the Heteroskedasticity Problem (cont.)
● Error variance decreasing
linearly
32
Applications with EViewsls foodexp c totalexp foodexp c totalexp 01.makeresid u
33
Command: scat totalexp u
heteroskedasticity?
Applications with EViews (cont.)
34
Applications with EViews (cont.)
35
Applications with EViews (cont.)lnfoodexp=log(foodexp) lntotalexp=log(totalexp)
36
Command: ls lnfoodexp c lntotalexp
Applications with EViews (cont.)
37
Applications with EViews (cont.)
38
OLS Ordinary Least Squares
BLUE classical normal linear Independent variables in the regression model are not correlated.
Multicollinearity
39
What is Multicollinearity?
● The problem of multicollinearity arises when the explanatory variables have approximate linear relationships.○ i.e. explanatory variables move closely together
● In this situation, it would be difficult to isolate the partial effect of a single variable. WHY?
40
Multicollinearity (cont.)
1. Exact (or Perfect) Multicollinearitya. Linear relationship among the independent variables
2. Near Multicollinearitya. Explanatory variables are approximately linearly
related
For example; If ➡ Exact
➡ Near
41
Theoretical Consequences of Multicollinearity Unbiasedness & Forecasts★ OLS estimators are still BLUE and MLE and hence are
unbiased, efficient and consistent.★ Forecasts are still unbiased and confidence intervals
are valid★ Although the standard errors and t-statistics of
regression coefficients are numerically affected,○ tests based on them are still valid
42
Standard Errors★ Standard errors tend to be higher
○ making t-statistics lower ○ thus making coefficients less significant (and
possibly even insignificant)
Theoretical Consequences of Multicollinearity (cont.)
43
● High R2 with low values for t-statistics● High values for correlation coefficients● Regression coefficients sensitive to specification● Formal test for multicollinearity
○ Eigenvalues and condition index (CI)
k= max eigenvalues/min eigenvalues CI=√k ➡ k is between 100 and 1000 ➡ multicollinearity?High variance inflation factor (VIF) ➡ VIF>10 ➡ THEN multicollinearity is suspected.
Identifying Multicollinearity
44
Solutions to the Multicollinearity Problem
● Benign Neglect○ Less interested in interpreting individual coefficients
but more interested in forecasting
● Eliminating Variables○ The surest way to eliminate or reduce the effects of
multicollinearity
45
Solutions to the Multicol. Problem (cont.)
● Reformulating the Model ○ In many situations, respecifying the model can
reduce multicollinearity
46
● Using Extraneous Information○ Often used in the estimation of demand functions○ High correlation between time series data on real
income and the price level■ Making the estimation of income and price elasticities
difficult
○ Estimate the income elasticity from cross-section studies■ and then use that information in the time series
model to estimate the price elasticity
Solutions to the Multicol. Problem (cont.)
47
● Increasing the Sample Size○ reduces the adverse effects of multicollinearity○ R2, including the new sample
■ goes down or remains approx. the same
● the variances of the coefficients will indeed decrease and counteract the effects of multicollinearity
■ goes up substantially● there may be no benefit to adding to the sample size
Solutions to the Multicol. Problem (cont.)
48
Overall statistically significant
but one by one
statistically insignificant
multicollinearityproblem
Applications with EViews
49
Command: eq01.varinf
Applications with EViews (cont.)
50
Command: scalar ci= @sqrt(66795998/3.44E-06)
Applications with EViews (cont.)
CI: Condition Index51
Applications with EViews (cont.)
52
The highest correlation is between the price of cars and the general price level.
Even if we drop these variables one-by-one from the model, still we have a multicollinearity problem.
Applications with EViews (cont.)
53
● When we drop both the general price level and the price of cars, the multicollinearity problem is solved ○ but R2 is low.
● So we check the second highest correlation between disposable income and price level.
DROP: General price level and disposable income After removing the variables, the problem is solved.
Loss of valuable information?
It is better to try solving the problem by increasing the sample size
Applications with EViews (cont.)
54
1. Omitting Influential or Including Non-Influential Explanatory Variables
2. Various Functional Forms 3. Measurement Errors4. Tests for Misspecification 5. Approaches in Choosing an Appropriate
Model
Model Misspecification
55
The Ramsey RESET Test
RESET: Regression specification error test
Step 1: Estimate the model that you think is correct and obtain the fitted values of Y, call them Step 2: Estimate the model in Step 1 again, this time include as additional explanatory variables.
56
The Ramsey RESET Test (cont.)
Step 3: The model in Step 1 is the restricted model and the model in Step 2 is the unrestricted model. Calculate the F-statistic for these two models.
● i.e. carry out a Wald F-test for the omission of the two new variables in Step 2
● If the null hypothesis (H0: the new variables have no effect)
is rejected indication of a specification error 57
In the presence of autocorrelation, cov( ut,us )≠0 for t≠s and the error for period t is correlated with the error for period s.
●● -1< ρ <1
○ ρ approaching 0 no correlation○ ρ approaching +1 positive correlation○ ρ approaching -1 negative correlation
Autocorrelation
58
Autocorrelation (cont.)
59
Causes of Autocorrelation
DIRECT INDIRECT
● Inertia or Persistence ● Omitted Variables
● Spatial Correlation ● Functional Form
● Cyclical Influences ● Seasonality
60
Consequences of Autocorrelation● OLS estimates are still unbiased and consistent● OLS estimates are inefficient not BLUE
○ Forecasts will also be inefficient● The same as the case of ignoring heteroskedasticity ● Usual formulas give incorrect standard errors for OLS
estimates● Confidence intervals and hypothesis tests based on the
usual standard errors are not valid61
Detecting Autocorrelation ❖ Runs Test: Investigate the signs of the residuals. Are
they moving randomly? (+) and (-) comes randomly don’t need to suspect autocorrelation problem.
❖ Durbin-Watson (DW) d Test: Ratio of the sum of squared differences in successive residuals to the residual sum of squares.
❖ Breusch-Godfrey LM Test: A more general test which does not assume the disturbance to be AR(1).
62
Durbin-Watson d Test
STEP 1 Estimate the model by OLS and compute the residuals ut
STEP 2 Compute the Durbin-Watson d statistic:
63
Durbin-Watson d Test (cont.)
STEP 3 Construct the table with the calculated DW statistic and the dU, dL, 4-dU and 4-dL critical values.
STEP 4 Conclude64
Resolving Autocorrelation The Cochrane-Orcutt Iterative Procedure
Step 1: Estimate the regression and obtain residuals.Step 2: Estimate the first-order serial correlation coefficient (⍴) from regressing the residuals to its lagged terms.
Step 3: Transform the original variables as follows:
65
Resolving Autocorrelation (cont.)
Step 4: Run the regression again with the transformed variables and obtain a new set of residuals.Step 5 and on: Continue repeating Steps 2 to 4 for several rounds until the following stopping rule applies:● the estimates of ⍴ from two successive iterations differ by no
more than some preselected small value, such as 0.001.
66
AUTOCORRELATION?
Applications with EViews
1.143 1.739
Variables in natural logarith:● LNCO: Copper price● LNIN: Inudtrial production● LNLON: London stock exchange● LNHS: Housing price● LNAL: Aluminium price
67
Applications with EViews (cont.)
H0: No autocorrelation
68
Applications with EViews (cont.)To Fix it!
69
Applications with EViews (cont.)To Fix it!
u=u(0)
70
Generate series:● y= lnco-0.52*lnco(-1)● x2= lnin-0.52*lnin(-1)● x3= lnlon-0.52*lnlon(-1)● x4= lnhs-0.52*lnhs(-1)● x5= lnal-0.52*lnal(-1)
Applications with EViews (cont.)To Fix it!
71
Command: ls y c x2 x3 x4 x5
Applications with EViews (cont.)To Fix it!
1.124 1.743
72
Applications with EViews (cont.)To Fix it!
73
Problem Source Detection Remedy
Heteroskedasticity Nonconstant variance Park Test, Glejser, White Test Taking logarithm, Weighted least squares
Autocorrelation E(ut,ut-1)≠0 Durbin-Watson d Test, Run Test, Breusch Godfrey LM Test
Cochrane-Orcutt Iterative Procedure and GLS
Multicollinearity Interdependence of xj ● High R2 but few significant t ratios
● High pairwise correlation between independent variables
● Eigenvalues and condition index, High VIF, Auxiliary Regressions
● Reformulating the model
● Dropping variables, ● Additional new data● Faitor analysis ● Principal comp.
analysis
Summary
74
2. “Stationarity is Job 1!”
What is Stationarity?● A stationary series can be defined as one with a
○ constant mean, constant variance and constant autocovariances
for each given lag.● The mean and/or variance of nonstationary series are
time dependent.● The correlation between a series and its lagged values
depend only on the length of the lag and not on when the series started.
● A series that is integrated of order zero, i.e. I(0).76
Example of a white noise
process
Time series plot of a
random walk vs. a random walk with drift
77
ExamplePDI: Personal Disposable Income
78
What is Stationarity? (cont.)
● If a regression model is not stationary,⇒ the usual “t-ratios” will not follow a t-distribution.
● The use of nonstationary data can lead to spurious regressions.
● Results of the regression do not reflect the real relationship except if these variables are cointegrated.
79
3. Univariate Time Series Modelling
Some Stochastic ProcessesRandom Walk
Moving Average Process
Autoregressive Process
Autoregressive Moving Average Process
81
Autoregressive Integrated MA Process
● Most time series are nonstationary● Successive differencing stationarity●● : A stationary series that can be
expressed by an ARMA(p, q)● can be represented by an ARIMA model
ARIMA(p, d, q)82
Estimation and Forecasting with an ARIMA ModelThe Box and Jenkins (1970) Approach ● Identification● Fitting (Estimation), usually OLS ● Diagnostics● Refitting if necessary ● Forecasting
83
Identification ● The process of specifying the orders of differencing,
AR modeling, and MA modeling● How do the data look like?● What pattern do the data show?
- Are the data stationary?- Specification of p, d, and q?
● Tools - Plots of data - Autocorrelation Function (ACF)- Partial ACF (PACF) 84
● To determine the value of p and q we use the graphical properties of the autocorrelation function and the partial autocorrelation function.
● Again recall the following:
Identification (cont.)
85
● Model parameters are estimated by OLS● Output includes
○ Parameter estimates○ Test statistics○ Goodness of fit measures○ Residuals○ Diagnostics
Model Fitting
86
Diagnostics
● Determines whether the model fits the data adequately.○ The aim is to extract all information and ensure that
residuals are white noise
● Key measures ○ ACF of residuals○ PACF of residuals ○ Ljung-Box Pierce Q statistic
87
Preliminary Analysis with EViewsSelect the series “dividends” in the workfile, then select [Quick/Graph/Line graph]:
88
[Quick/Generate Series]:
Preliminary Analysis with EViews (cont.)
ddividends=d(dividends)89
Preliminary Analysis: IdentificationCorrelogram● The graph of autocorrelation function
against s, for s = 0, 1, 2, …, t-1
● Useful diagram for identifying patterns in correlation among series.
● Useful guide for determining how correlated the error term (ut ) is to the past errors ut-1, ut-2, ...
90
Preliminary Analysis: IdentificationInterpretation of Correlogram● If ⍴ is high, correlogram for AR
(1) declines slowly over time○ First differencing is indicated
91
Preliminary Analysis: IdentificationInterpretation of Correlogram● The function quickly decreases
to zero (a low ⍴)
92
Correlogram and Stationarity
93
Preliminary Analysis: Estimation ARIMA(1,1,1)Command: ls ddividens c AR(1) MA(1)
94
Empirical ExampleForecasting Monthly Electricity Sales
Total System Energy Demand
95
Empirical Example (cont.)Forecasting Monthly Electricity Sales
Correlogram for Monthly Electricity Sales Data
96
Empirical Example (cont.)Forecasting Monthly Electricity Sales
Correlogram for 12-Month Differenced Data(Xt-Xt-12)
97
Empirical Example (cont.)Forecasting Monthly Electricity Sales
Box-Jenkins Forecast of System Energy
RMSE: Root mean squared error
Superior model: ARIMA (0, 1, 4)
ARMA Order AIC RMSE
(1, 1) 1,930 320
(4, 1) 1,927 312
(1, 4) 1,926 311
(0, 4) 1,924 311
98
Bibliography● Brooks, C. (2008) Introductory Econometrics for Finance,
● Gujarati D.N., Porter D.C. (2004), Basic Econometrics,The McGraw−Hill Companies
● Maddala, G.S. (2002). Introduction to Econometrics.
● Ramanathan, R. (2002). Introductory econometrics with applications, Thomson Learning. Mason, Ohio, USA.
● Wooldridge,J. (2000) Introductory Econometrics: A modern Approach. South-Western College Publishing
99
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