cointegration and error correction model
DESCRIPTION
Error correction model and its application to agri economics research.TRANSCRIPT
Error Correction Model And Its Application To Agricultural
Economics Research.PresenterAditya K.S., PALB (1094)Sr. M.Sc. (Agricultural Economics)
Major Adviser: Dr. T.N. Prakash Kammardi
Flow of presentation
Department Of Agricultural Economics, Bangalore
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Concepts and definitions.Cointegration.Residual based test for cointegration.Johansen’s cointegration test.Introduction to ECM.Engle – Granger two step ECM.Market integration of Arecanut in Karnataka state: An ECM approach.Final outcome.Concluding remarks.References.
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Concept and definitions
Stationary v/s non stationary
• If a time series is stationary, its mean and variance remain the same no matter at what point we measure them;
That is, they are time invariant.
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5Department Of Agricultural Economics, Bangalore
Figure 1: Monthly prices of Arecanut in Mangalore from 2005 to 2011
Pure Random Walk
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Random Walk with Drift
Order of Integration Differencing is a way to convert non stationary data into stationary. If the data has to be differenced d times to make it stationary then series
said to be integrated of order (d) and represented as I(d) I(1) processes are fairly common in economic time series data
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Figure 2: 1st difference of monthly prices of Arecanut in Mangalore from 2005 to 2011
Price series is I(1)
UNIT ROOT• If ρ = 1 it becomes a pure random walk. • If ρ is in fact 1, we face what is known as the unit root
problem, that is, a situation of nonstationary; • The name unit root is due to the fact that ρ = 1.
Yt = ρYt −1 + ut
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Synonymous
Testing for unit rootsAugmented dickey fuller test(ADF) – Include the lagged terms.
Phillip Perron tests (PP) – Non parametric method.
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NH: Series contains unit root
AH : Series does not contain unit roots
Decision rule: Reject NH if P<0.05
Spurious Regression
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Suppose that Yt and Xt are two non stationary time series variables
Yt = βXt + error:
β significant β not significant
Yt and Xt are independentDue to trend(non stationarity)
Due to actual relationship
Spurious regressionCointegration R2 >D.W stat
Cointegration• Economic theory often suggests that certain
subset of variables should be linked by a long-run equilibrium relationship.
• Although the variables under consideration may drift away from equilibrium for a while, economic forces or government actions may be expected to restore equilibrium.
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13Department Of Agricultural Economics, Bangalore
Observe that two series follow each
other closely
Figure 3: Monthly prices of Arecanut in Mangalore and Kundapura from 2005 to 2011
Short run disequilibrium
Residual-based Test for Cointegration
• One of most popular tests for (a single) co integration has been suggested by Engle and Granger (1987, Econometrica).
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Consider the multiple regression: Yt = βXt + ut;
• for yt and xt to be cointegrated, ut must be I(0).• Otherwise it is spurious. Thus, a basic idea
behind is to test whether ut is I(0) or I(1).
Ut is stationary
Ut is not stationary
Cointegration
Spurious regression
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Residual plot of regression Bantwala V/S kundapura
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Johansen's procedure
• Johansen's procedure builds cointegrated variables directly on maximum likelihood estimation
• Tests for determining the number of cointegrating vectors.
• Multivariate generalization of the Dickey-Fuller test. • Two different likelihood ratio tests namely the Trace
test and the Maximum Eigen value test.
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Two time series are cointegrated if
Cointegrated data are never drift too far away from each other
Both are integrated of the same order.
There is a linear combination of the two time series that is I(0) - i.e. - stationary.
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An Introduction to ECMs• Error Correction Models (ECMs) multiple time
series models that estimate the speed at which a dependent variable - Y - returns to equilibrium after a change in an independent variable - X. i.e SPEED OF ADJUSTMENT
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ECMs can be appropriate whenever
time series data
Interested in both short and
long term relationships
Non stationary
Integrated of same order Cointegrated
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• Yt = βXt + Ut
• Here, Ut represents the portion of Y (in levels) that is not attributable to X.
• In short, Ut will capture the error correction relationship by capturing the degree to which Y and X are out of equilibrium.
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• Ut-1 = Yt-1 - Xt-1
• When Ut-1 = 0 the system is in its equilibrium state.
• So ECM can be built as ∆Yt = C + Φ ∆Xt + αUt-1
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Engle and Granger Two-Step ECM
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• Engle and Granger (1987) suggested an appropriate model for Y, based two or more time series that are cointegrated.
• First, we can obtain an estimate of Ut by regressing Y on X.
• Second, we can regress ∆ Yt on Ut-1 plus any relevant short term effects as ∆ Xt.
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• Market integration of Arecanut in Karnataka state: An ECM approach.
(Source: Author)
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Market Integration
• Spatial market integration refers to co-movements or a long run relationship of prices.
• It is defined as the smooth transmission of price signals and information across spatially separated markets
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Integrated markets:EfficiencyEqualityStabilityMaximize social welfare
• Study of market integration is very important though neglected.
• Knowledge of market integration would be vital to know the market efficiency, and to device measures to overcome imperfections.
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• Traditional method of study employs correlation matrix to study the market integrations.
• Since the data are non stationary results may not be accurate and hence criticized.
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Data and Methodology• For the purpose of analyzing the integration of
arecanut markets, monthly prices of arecanut from 2005 to 2011 in 7 major arecanut markets in Karnataka was used.
• Data was collected from Agmarknet.
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Table 1:Markets selected for study
Sl no WCT RBT
1 Mangalore Shimoga
2 Bantwala Sagara
3 Kundapura Davangeree
4 Sirsi
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Methodology
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Unit root testingNH: Series is non stationary
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Table 2: Results of Unit root test for arecanut price in major RBT markets from 2005 to 2011
At level
PP P value ADF P value
Sagara -1.90949 0.3259 -1.53207 0.5105
Shimoga -2.59777 0.0991 -2.69163 0.0815
Davangeree -2.39903 0.1464 -1.59475 0.4787
Sirsi -2.14473 0.2285 -1.13264 0.6969
After first difference
PP P value ADF P value
Sagara -10.8727 0 -10.1247 0
Shimoga -14.8105 0 -6.57014 0
Davangeree -11.1522 0 -8.09634 0
Sirsi -11.37 0 -8.307 0
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Table 3: Results of Unit root test for arecanut price in major WCT markets from 2005 to 2011
Both tests indicate that prices are integrated of order (1)
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At levelADF P PP p
Mangalore -1.75041 0.4024 -1.75041 0.4024
Kundapura -2.09198 0.2484 -2.13241 0.2328
Bantwala -0.56366 0.8719 -0.64773 0.8531
At 1st differenceADF P PP p
Mangalore -7.89198 0 -7.94013 0
Kundapura -7.02788 0 -8.49619 0
Bantwala -12.1208 0.0001 -12.3691 0.0001
Testing for cointegration
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Engle Granger test -Decision rule
• Engle Granger critical value at 1% LOS is -3.96
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Ut= ΏUt-1 + e
Table 4. Engle Granger cointegration testfor major arecanut markets in Karnataka
Kundapura MangaloreBantwala -6.2580 -2.57891Kundapura -6.47711
Sagara Shimoga SirsiDavangeree -5.264 -6.16165 -5.4227
Sagara -5.7895 -5.5994
Shimoga -5.4529
There is cointegration among all markets under consideration except Bantwala and Mangalore
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Johansen cointegration test
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Table 5. Johansen’s cointegration test for RBT arecanut markets
Shimoga Davangeree Sirsi
No of coint
equations trace stat p trace stat p trace stat p
Sagara R=0 20.68967 0.0075 26.24133 0.0008 22.90293 0.0032
R ≤ 1 2.148919 0.1427 2.197354 0.1382 2.391261 0.122
Shimoga R=0 29.09037 0.0003 18.48941 0.0171
R≤ 1 4.906882 0.0267 2.71361 0.0995
Davangeree R=0 29.16382 0.0003
R≤ 1 2.9382 0.0865
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Contd…………….
Shimoga Davangere SirsiNumber of
coint
equations
Max eigen
valuep
Max
eigen
value
p
Max
eigen
value
p
SagaraR=0 18.54075 0.0099 24.04398 0.0011 20.51167 0.0045
R≤ 1 2.148919 0.1427 2.197354 0.1382 2.391261 0.122
ShimogaR=0 24.18349 0.001 15.7758 0.0286
R≤ 1 4.906882 0.0267 2.71361 0.0995
DavangereeR=0 26.22562 0.0004
R≤ 1 2.9382 0.0865
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Table :6 Johansen’s cointegration test for WCT arecanut markets
trace stat Max eigen value
Dependent IndependentNo. of coint
equationvalue P value P
Bantwala Mangalore R=0 10.29579 0.3888 7.901239 0.255
R ≤ 1 2.394551 0.1218 2.394551 0.1218
Kundapura Bantwala R=0 23.32457 0.0027 23.26433 0.0015
R ≤ 1 0.060234 0.8061 0.060234 0.8061
Kundapura Mangalore R=0 16.93599 0.0301 13.84253 0.05
R ≤ 1 3.093461 0.0786 3.093461 0.0786
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Table 7 : Error correction models for RBT arecanut markets
Error Correction model results for RBT.
∆ Dav = -9.73171+0.8484∆ sag – 0.64371 et-1
(0.90) (0) (0)
∆ Dav = -12.3961 + 0.8104 ∆ sir – 0.64273 et-1
(0.8967) (0) (0)
∆ Dav = -6.92457 + 0.7822 ∆ shiv – 0.73867 et-1
(0.9249) (0) (0)
∆ Sag = - 2.2253 + 0.65523 ∆ shiv – 0.6073 et-1
(0.98) (0) (0)
∆ Sag = - 3.9302 + 0.8762 ∆ shir– 0.6453 et-1
(0.95) (0) (0)
∆ shiv = - 7.6146 + 1.007 ∆ shir– 0.6719 et-1
(0.93) (0) (0)44Department Of Agricultural Economics,
Bangalore
Figures in parenthesis indicate the probability values.
Model estimated: ∆ Yt= C + Φ ∆Xt+ α Ut-1
Error Correction model results for WCT.
∆ kund = 3.79 + 0.83 ∆mang -0.66 et-1
( 0.98) ( 0.001) (0)
∆ bant = 22.75 +0.75 ∆kund -0.72 et-1
(0.97) (0.002) (0)
Figures in parenthesis indicate the probability values.
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Table 8 : Error correction models for RBT arecanut markets
Table 9: Speed of error correction
Sagara Shimoga Sirsi
Davang
eree
64 73 64
Sagara 60 64
Shimog
a
67
Mangalore Kundapura
Bantwala 66 72
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Final outcome• Arecanut markets are highly cointegrated may be
because of better marketing infrastructure, existence of cooperatives, easy flow of market information and non perishability.
• Price volatility observed during last few years has nothing to do with the inefficiency of domestic markets.
• If the government wants to stabilize the prices of arecanut, then it can be done by stabilizing the prices in one important market.
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Concluding remarks• Most valuable contribution of concept of cointegration is to
force us to test for Stationarity of the residuals.• Cointegration can be thought as pre test to avoid spurious
regression situation. • Cointegrated variables will always have a built in error
correction mechanism, estimation of which will be helpful to know short run dynamics of the system.
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• Though theoretically appealing, practically simple, ECM cannot be used in complex situations involving more number of non stationary variables.
• In such situations one can go for vector error correction models (VECM) which are nothing but multivariate specification of ECM.
Department Of Agricultural Economics, Bangalore
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Department Of Agricultural Economics, Bangalore
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