# Estimation and Inference in Cointegration Models Economics 582

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1 Estimation and Inference in Cointegration Models Economics 582 Eric Zivot May 17, 2012 Tests for Cointegration Let the ( 1) vector Y be (1). Recall, Y is cointegrated with 0 cointegrating vectors if there exists an ( ) matrix B 0 such that β 0 B 0 1 Y 1 Y =. β 0 =. (0) Y Testing for cointegration may be thought of as testing for the existence of long-run equilibria among the elements of Y.

2 Cointegration tests cover two situations: There is at most one cointegrating vector Originally considered by Engle and Granger (1986), Econometrica. They developed a simple two-step residual-based testing procedure basedonregressiontechniques. There are possibly 0 cointegrating vectors. Originally considered by Johansen (1988), Journal of Economics Dynamics and Control. He developed a sophisticated sequential procedure for determining the existence of cointegration and for determining the number of cointegrating relationships based on maximum likelihood techniques (covered in econ 584) Residual-Based Tests for Cointegration Engle and Granger s two-step procedure for determining if the ( 1) vector β is a cointegrating vector is as follows: Form the cointegrating residual β 0 Y = Performaunitrootteston to determine if it is (0). The null hypothesis in the Engle-Granger procedure is no-cointegration and the alternative is cointegration.

3 There are two cases to consider. 1. The proposed cointegrating vector β is pre-specified (not estimated). For example, economic theory may imply specific values for the elements in β such as β =(1 1) 0. The cointegrating residual is then readily constructed using the prespecified cointegrating vector. 2. The proposed cointegrating vector is estimated from the data and an estimate of the cointegrating residual ˆβ 0 Y =ˆ is formed. Note: Tests for cointegration using a pre-specified cointegrating vector are generally much more powerful than tests employing an estimated vector. Testing for Cointegration When the Cointegrating Vector Is Pre-specified Let Y denote an ( 1) vector of (1) time series, let β denote an ( 1) prespecified cointegrating vector and let The hypotheses to be tested are = β 0 Y = cointegrating residual 0 : = β 0 Y (1) (no cointegration) 1 : = β 0 Y (0) (cointegration)

4 Remarks: Any unit root test statistic may be used to evaluate the above hypotheses. Cointegration is found if the unit root test rejects the no-cointegration null. It should be kept in mind, however, that the cointegrating residual may include deterministic terms (constant or trend) and the unit root tests should account for these terms accordingly. Testing for Cointegration When the Cointegrating Vector Is Estimated Let Y denote an ( 1) vector of (1) time series and let β denote an ( 1) unknown cointegrating vector. The hypotheses to be tested are 0 : = β 0 Y (1) (no cointegration) 1 : = β 0 Y (0) (cointegration)

5 Remarks: Since β is unknown, to use the Engle-Granger procedure it must be first estimated from the data. Before β can be estimated some normalization assumption must be made to uniquely identify it. A common normalization is to specify Y =( 1 Y 0 2 )0 where Y 2 = ( 2 ) 0 is an (( 1) 1) vector and the cointegrating vector is normalized as β =(1 β 0 2 )0. Engle and Granger propose estimating the normalized cointegrating vector β 2 by least squares from the regression 1 = γ 0 D + β 0 2 Y 2 + D = deterministic terms and testing the no-cointegration hypothesis with a unit root test using the estimated cointegrating residual ˆ = 1 ˆγ 0 D ˆβ 2 Y 2 The unit root test regression in this case is without deterministic terms (constantorconstantandtrend). For example, if one uses the ADF test, the test regression is X ˆ = ˆ 1 + ˆ + =1

6 Distribution Theory Phillips and Ouliaris (PO) (1990) show that ADF unit root tests (t-tests andnormalizedbias)appliedtotheestimatedcointegratingresidualdo not have the usual Dickey-Fuller distributions under the null hypothesis of no-cointegration. Due to the spurious regression phenomenon under the null hypothesis, the distribution of the ADF unit root tests have asymptotic distributions that are functions of Wiener processes that Depend on the deterministic terms in the regression used to estimate β 2 Depend on the number of variables, 1 in Y 2. PO Critical Values for Engle-Granger Cointegration Test. (Constant included in test regression) n-1 1% 5%

8 Regression-Based Estimates of Cointegrating Vectors and Error Correction Models Least Square Estimator Least squares may be used to consistently estimate a normalized cointegrating vector. However, the asymptotic behavior of the least squares estimator is nonstandard. The following results about the behavior of ˆβ 2 if Y is cointegrated are due to Stock (1987) and Phillips (1991): (ˆβ 2 β 2 ) converges in distribution to a not necessarily centered at zero. non-normal random variable The least squares estimate ˆβ 2 is consistent for β 2 and converges to β 2 at rate instead of the usual rate 1 2.Thatis,ˆβ 2 is super consistent. ˆβ 2 is consistent even if Y 2 is correlated with so that there is no asymptotic simultaneity bias. In general, the asymptotic distribution of (ˆβ 2 β 2 ) is asymptotically biased and non-normal. The usual OLS formula for computing (ˆβ d 2 ) is incorrect and so the usual OLS standard errors are not correct. Even though the asymptotic bias goes to zero as gets large ˆβ 2 may be substantially biased in small samples. The least squres estimator is also not efficient. The above results indicate that the least squares estimator of the cointegrating vector β 2 could be improved upon. A simple improvement is suggested by Stock and Watson (1993).

9 Stock and Watson s Efficient Lead/Lag Estimator Stock and Watson (1993) provide a very simple method for obtaining an asymptotically efficient (equivalent to maximum likelihood) estimator for the normalized cointegrating vector β 2 as well as a valid formula for computing its asymptotic variance. Let Y = ( 1 Y2 0 )0 Y 2 = ( 2 ) 0 β = (1 β 0 2 )0 Stock and Watson s efficient estimation procedure is: Augment the cointegrating regression of 1 on Y 2 with appropriate deterministic terms D with leads and lags of Y 2 1 = γ 0 D + β 0 2 Y 2 + X = ψ 0 Y 2 + = γ 0 D + β 0 2 Y 2 + ψ 0 0 Y 2 +ψ 0 + Y ψ 0 +1 Y ψ 0 1 Y ψ 0 Y 2 + Estimate the augmented regression by least squares. The resulting estimator of β 2 is called the dynamic OLS estimator and is denoted ˆβ 2.It will be consistent, asymptotically normally distributed and efficient (equivalent to MLE) under certain assumptions (see Stock and Watson (1993))

10 Asymptotically valid standard errors for the individual elements of ˆβ 2 are computed using the Newey-West HAC standard errors. Estimating Error Correction Models by Least Squares Consider a bivariate (1) vector Y =( 1 2 ) 0 and assume that Y is cointegrated with cointegrating vector β =(1 2 ) 0 so that β 0 Y = is (0). Suppose one has a consistent estimate ˆ 2 (by OLS or DOLS) of the cointegrating coefficient and is interested in estimating the corresponding error correction model for 1 and 2 using 1 = ( 1 1 ˆ ) + X X = ( 1 1 ˆ ) + X X

11 Because ˆ 2 is super consistent it may be treated as known in the ECM, so that the estimated disequilibrium error 1 ˆ 2 2 may be treated like the known disequilibrium error Since all variables in the ECM are (0), the two regression equations may be consistently estimated using ordinary least squares (OLS). Alternatively, the ECM system may be estimated by seemingly unrelated regressions (SUR) to increase efficiency if the number of lags in the two equations are different. VAR Models and Cointegration The Granger representation theorem links cointegration to error correction models. Inaseriesofimportantpapersandinamarveloustextbook,SorenJohansen firmly roots cointegration and error correction models in a vector autoregression framework. This section outlines Johansen s approach to cointegration modeling.

12 The Cointegrated VAR Consider the levels VAR( ) forthe( 1) vector Y Y = ΦD + Π 1 Y Π Y + ε D = deterministic terms The VAR( ) model is stable if det(i Π 1 Π )=0 has all roots outside the complex unit circle. If there are roots on the unit circle then some or all of the variables in Y are (1) and they may also be cointegrated. VECM Representation If is cointegrated then the VAR representation is not the most suitable representation for analysis because the cointegrating relations are not explicitly apparent. The cointegrating relations become apparent if the levels VAR is transformed to the vector error correction model (VECM) Y = ΦD + ΠY 1 + Γ 1 Y Γ 1 Y +1 + ε Π = Π Π I X Γ = Π =1 1 = +1

13 Cointegration Restrictions In the VECM, Y and its lags are (0). The term ΠY 1 is the only one which includes potential (1) variables and for Y to be (0) it must be the case that ΠY 1 is also (0). Therefore, ΠY 1 must contain the cointegrating relations if they exist. If the VAR( ) process has unit roots ( =1)then det(i Π 1 Π )=0 det(π) =0 Π is singular If Π is singular then it has reduced rank; thatis (Π) =. There are two cases to consider: 1. (Π) =0.Thisimpliesthat Π = 0 Y (1) and not cointegrated The VECM reduces to a VAR( 1) infirst differences Y = ΦD + Γ 1 Y Γ 1 Y +1 + ε

14 2. 0 (Π) =. This implies that Y is (1) with linearly independent cointegrating vectors and common stochastic trends (unit roots). Since Π has rank it can be written as the product Π ( ) = α β 0 ( )( ) where α and β are ( ) matrices with (α) = (β) =. The rows of β 0 form a basis for the cointegrating vectors and the elements of α distribute the impact of the cointegrating vectors to the evolution of Y. The VECM becomes Y = ΦD + αβ 0 Y 1 + Γ 1 Y Γ 1 Y +1 + ε where β 0 Y 1 (0) since β 0 is a matrix of cointegrating vectors. Non-uniqueness It is important to recognize that the factorization Π = αβ 0 is not unique since for any nonsingular matrix H we have αβ 0 = αhh 1 β 0 =(ah)(βh 10 ) 0 = a β 0 Hence the factorization Π = αβ 0 only identifies the space spanned by the cointegrating relations. To obtain unique values of α and β 0 requires further restrictions on the model.

15 Example: A bivariate cointegrated VAR(1) model Consider the bivariate VAR(1) model for Y =( 1 2 ) 0 The VECM is Y = Π 1 Y 1 + ² Y = ΠY 1 + ε Π = Π 1 I 2 Assuming Y is cointegrated there exists a 2 1 vector β =( 1 2 ) 0 such that β 0 Y = (0) Using the normalization 1 = 1 and 2 = the cointegrating relation becomes β 0 Y = 1 2 This normalization suggests the stochastic long-run equilibrium relation 1 = 2 + Since Y is cointegrated with one cointegrating vector, (Π) =1so that Ã! Ã! Π = αβ 0 1 ³ 1 = 1 = The elements in the vector α are interpreted as speed of adjustment coefficients. The cointegrated VECM for Y may be rewritten as Y = αβ 0 Y 1 + ε Writing the VECM equation by equation gives 1 = 1 ( )+ 1 2 = 2 ( )+ 2

16 Stability of the VECM The stability conditions for the bivariate VECM are related to the stability conditions for the disequilibrium error β 0 Y. It is straightforward to show that β 0 Y follows an AR(1) process or β 0 Y =(1 + β 0 α)β 0 Y 1 + β 0 ε = 1 + = β 0 Y = 1+β 0 α =1+( 1 2 ) = β 0 ε = 1 2 The AR(1) model for is stable as long as = 1+( 1 2 ) 1 For example, suppose =1. Then the stability condition is = 1+( 1 2 ) 1 which is satisfied if and 1 2 2

17 Johansen s Methodology for Modeling Cointegration The basic steps in Johansen s methodology are: Specify and estimate a VAR( ) modelfory Construct likelihood ratio tests for the rank of Π to determine the number of cointegrating vectors. If necessary, impose normalization and identifying restrictions on the cointegrating vectors. Given the normalized cointegrating vectors estimate the resulting cointegrated VECM by maximum likelihood. Likelihood Ratio Tests for the Number of Cointegrating Vectors The unrestricted cointegrated VECM is denoted ( ). The (1) model ( ) can be formulated as the condition that the rank of Π is less than or equal to. This creates a nested set of models (0) ( ) ( ) (0) = non-cointegrated VAR ( ) = stationary VAR(p) This nested formulation is convenient for developing a sequential procedure to test for the number of cointegrating relationships.

18 Remarks: Since the rank of the long-run impact matrix Π gives the number of cointegrating relationships in Y, Johansen formulates likelihood ratio (LR) statistics for the number of cointegrating relationships as LR statistics for determining the rank of Π These LR tests are based on the estimated eigenvalues ˆ 1 ˆ 2 ˆ of the matrix Π. These eigenvalues also happen to equal the squared canonical correlations between Y and Y 1 corrected for lagged Y and D and so lie between 0 and 1. Recall, the rank of Π is equal to the number of non-zero eigenvalues of Π. Johansen s Trace Statistic Johansen s LR statistic tests the nested hypotheses 0 ( ) : = 0 vs. 1 ( 0 ): 0 The LR statistic, called the trace statistic, isgivenby X ( 0 )= ln(1 ˆ ) = 0 +1 If (Π) = 0 then ˆ 0 +1 ˆ should all be close to zero and ( 0 ) should be small since ln(1 ˆ ) 0 for 0. In contrast, if (Π) 0 then some of ˆ 0 +1 ˆ will be nonzero (but less than 1) and ( 0 ) should be large since ln(1 ˆ ) 0 for some 0.

19 The asymptotic null distribution of ( 0 ) is not chi-square but instead is a multivariate version of the Dickey-Fuller unit root distribution which depends on the dimension 0 and the specification of the deterministic terms. Critical values for this distribution are tabulated in Osterwald-Lenum (1992) for 0 =1 10. Sequential Procedure for Determining the Number of Cointegrating Vectors 1. First test 0 ( 0 =0)against 1 ( 0 0). Ifthisnullisnotrejected then it is concluded that there are no cointegrating vectors among the variables in Y. 2. If 0 ( 0 =0)is rejected then it is concluded that there is at least one cointegrating vector and proceed to test 0 ( 0 =1)against 1 ( 0 1). If this null is not rejected then it is concluded that there is only one cointegrating vector. 3. If the 0 ( 0 =1)is rejected then it is concluded that there is at least two cointegrating vectors.

20 4. The sequential procedure is continued until the null is not rejected. Johansen s Maximum Eigenvalue Statistic Johansen also derives a LR statistic for the hypotheses 0 ( 0 ): = 0 vs. 1 ( 0 ): 0 = 0 +1 The LR statistic, called the maximum eigenvalue statistic, is given by max ( 0 )= ln(1 ˆ 0 +1) As with the trace statistic, the asymptotic null distribution of max ( 0 ) is not chi-square but instead is a complicated function of Brownian motion, which depends on the dimension 0 and the specification of the deterministic terms. Critical values for this distribution are tabulated in Osterwald-Lenum (1992) for 0 =1 10.

21 Specification of Deterministic Terms Following Johansen (1995), the deterministic terms in are restricted to the form ΦD = μ = μ 0 + μ 1 If the deterministic terms are unrestricted then the time series in Y may exhibit quadratic trends and there may be a linear trend term in the cointegrating relationships. Restricted versions of the trend parameters μ 0 and μ 1 limit the trending nature of the series in Y. The trend behavior of Y can be classified into five cases: 1. Model 2 ( ): μ =0(no constant): Y = αβ 0 Y 1 +Γ 1 Y Γ 1 Y +1 + ε and all the series in Y are (1) without drift and the cointegrating relations β 0 Y have mean zero. 2. Model 1 ( ): μ = μ 0 = αρ 0 (restricted constant): Y = α(β 0 Y 1 + ρ 0 ) +Γ 1 Y Γ 1 Y +1 + ε the series in Y are (1) without drift and the cointegrating relations β 0 Y have non-zero means ρ 0.

22 3. Model 1 ( ): μ = μ 0 (unrestricted constant): Y = μ 0 + αβ 0 Y 1 +Γ 1 Y Γ 1 Y +1 + ε the series in Y are (1) with drift vector μ 0 and the cointegrating relations β 0 Y may have a non-zero mean. 4. Model ( ): μ = μ 0 + αρ 1 (restricted trend). The restricted VECM is Y = μ 0 + α(β 0 Y 1 + ρ 1 ) +Γ 1 Y Γ 1 Y +1 + ε the series in Y are (1) with drift vector μ 0 and the cointegrating relations β 0 Y have a linear trend term ρ Model ( ): μ = μ 0 + μ 1 (unrestricted constant and trend). The unrestricted VECM is Y = μ 0 + μ 1 + αβ 0 Y 1 +Γ 1 Y Γ 1 Y +1 + ε the series in Y are (1) with a linear trend (quadratic trend in levels) and the cointegrating relations β 0 Y have a linear trend.

23 Maximum Likelihood Estimation of the Cointegrated VECM If it is found that (Π) =, 0, then the cointegrated VECM Y = ΦD + αβ 0 Y 1 + Γ 1 Y Γ 1 Y +1 + ε becomes a reduced rank multivariate regression. Johansen derived the maximum likelihood estimation of the parametes under the reduced rank restriction (Π) = (see Hamilton for details). Johansen shows that ˆβ =(ˆv 1 ˆv ) where ˆv are the eigenvectors associated with the eigenvalues ˆ, The MLEs of the remaining parameters are obtained by least squares estimation of Y = ΦD + αˆβ 0 Y 1 + Γ 1 Y Γ 1 Y +1 + ε

24 Normalized Estimates of α and β The factorization is not unique ˆΠ = ˆα ˆβ 0 The columns of ˆβ may be interpreted as linear combinations of the underlying cointegrating relations. For interpretations, it is often convenient to normalize or identify the cointegrating vectors by choosing a specific coordinate system in which to express the variables. Johansen s normalized MLE An arbitrary normalization, suggested by Johansen, is to solve for the triangular representation of the cointegrated system (default method in Eviews). The resulting normalized cointegrating vector is denoted ˆβ. The normalization of the MLE for β to ˆβ will affect the MLE of α but not the MLEs of the other parameters in the VECM. Let ˆβ denote the MLE of the normalized cointegrating matrix β. Johansen (1995) showed that ( (ˆβ ) (β )) is asymptotically (mixed) normally distributed ˆβ is super consistent

25 Testing Linear Restrictions on β The Johansen MLE procedure only produces an estimate of the basis for the space of cointegrating vectors. It is often of interest to test if some hypothesized cointegrating vector lies in the space spanned by the estimated basis: Ã! 0 : β 0 β 0 = ( ) φ 0 β 0 0 = matrix of hypothesized cv s φ 0 = ( ) matrix of remaining unspecified cv s Result: Johansen (1995) showed that a likelihood ratio statistic can be computed, which is asymptotically distributed as a 2 with ( ) degrees of freedom.

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