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STAT 497 LECTURE NOTE 11

STAT 497 LECTURE NOTE 11. VAR MODELS AND GRANGER CAUSALITY. VECTOR TIME SERIES. A vector series consists of multiple single series. Why we need multiple series? To be able to understand the relationship between several components To be able to get better forecasts. VECTOR TIME SERIES.

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STAT 497 LECTURE NOTE 11

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  1. STAT 497LECTURE NOTE 11 VAR MODELS AND GRANGER CAUSALITY

  2. VECTOR TIME SERIES • A vector series consists of multiple single series. • Why we need multiple series? • To be able to understand the relationship between several components • To be able to get better forecasts

  3. VECTOR TIME SERIES • Price movements in one market can spread easily and instantly to another market. For this reason, financial markets are more dependent on each other than ever before. So, we have to consider them jointly to better understand the dynamic structure of global market. Knowing how markets are interrelated is of great importance in finance. • For an investor or a financial institution holding multiple assets play an important role in decision making.

  4. VECTOR TIME SERIES

  5. VECTOR TIME SERIES

  6. VECTOR TIME SERIES

  7. VECTOR TIME SERIES • Consider an m-dimensional time series Yt=(Y1,Y2,…,Ym)’. The series Yt is weakly stationary if its first two moments are time invariant and the cross covariance between Yit and Yjs for all i and j are functions of the time difference (s-t) only.

  8. VECTOR TIME SERIES • The mean vector: • The covariance matrix function

  9. VECTOR TIME SERIES • The correlation matrix function: where D is a diagonal matrix in which the i-th diagonal element is the variance of the i-th process, i.e. • The covariance and correlation matrix functions are positive semi-definite.

  10. VECTOR WHITE NOISE PROCESS • {at}~WN(0,) iff {at} is stationary with mean 0 vector and

  11. VECTOR TIME SERIES • {Yt} is a linear process if it can be expressed as where {j} is a sequence of mxn matrix whose entries are absolutely summable, i.e.

  12. VECTOR TIME SERIES • For a linear process, E(Yt)=0 and

  13. MA (WOLD) REPRESENTATION • For the process to be stationary, s should be square summable in the sense that each of the mxm sequence ij.sis square summable.

  14. AR REPRESENTATION • For the process to be invertible, s should be absolute summable.

  15. THE VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA) PROCESSES • VARMA(p,q) process:

  16. VARMA PROCESS • VARMA process is stationary if the zeros of |p(B)| are outside the unit circle. • VARMA process is invertible if the zeros of |q(B)| are outside the unit circle.

  17. IDENTIFIBILITY PROBLEM • Multiplying matrices by some arbitrary matrix polynomial may give us an identical covariance matrix. So, the VARMA(p,q) model is not identifiable. We cannot uniquely determine p and q.

  18. IDENTIFIBILITY PROBLEM • Example: VARMA(1,1) process MA()=VMA(1)

  19. IDENTIFIBILITY • To eliminate this problem, there are three methods suggested by Hannan (1969, 1970, 1976, 1979). • From each of the equivalent models, choose the minimum MA order q and AR order p. The resulting representation will be unique if Rank(p(B))=m. • Represent p(B) in lower triangular form. If the order of ij(B) for i,j=1,2,…,m, then the model is identifiable. • Represent p(B) in a form p(B) =p(B)Iwhere p(B) is a univariate AR(p). The model is identifiable if p0.

  20. VAR(1) PROCESS • Yi,t depends not only the lagged values of Yitbut also the lagged values of the other variables. • Always invertible. • Stationary if outside the unit circle. Let =B-1. The zeros of |IB| is related to the eigenvalues of .

  21. VAR(1) PROCESS • Hence, VAR(1) process is stationary if the eigenvalues of ; i, i=1,2,…,m are all inside the unit circle. • The autocovariance matrix:

  22. VAR(1) PROCESS • k=1,

  23. VAR(1) PROCESS • Then,

  24. VAR(1) PROCESS • Example: The process is stationary.

  25. VMA(1) PROCESS • Always stationary. • The autocovariance function: • The autocovariance matrix function cuts of after lag 1.

  26. VMA(1) PROCESS • Hence, VMA(1) process is invertible if the eigenvalues of ; i, i=1,2,…,m are all inside the unit circle.

  27. IDENTIFICATION OF VARMA PROCESSES • Same as univariate case. • SAMPLE CORRELATION MATRIC FUNCTION: Given a vector series of n observations, the sample correlation matrix function is where ‘s are the crosscorrelation for the i-th and j-th component series. • It is very useful to identify VMA(q).

  28. SAMPLE CORRELATION MATRIC FUNCTION • Tiao and Box (1981): They have proposed to use +, and . signs to show the significance of the cross correlations. + sign: the value is greater than 2 times the estimated standard error • sign: the value is less than 2 times the estimated standard error . sign: the value is within the 2 times estimated standard error

  29. PARTIAL AUTOREGRESSION OR PARTIAL LAG CORRELATION MATRIX FUNCTION • They are useful to identify VAR order. The partial autoregression matrix function is proposed by Tiao and Box (1981) but it is not a proper correlation coefficient. Then, Heyse and Wei (1985) have proposed the partial lag correlation matrix function which is a proper correlation coefficient. Both of them can be used to identify the VARMA(p,q).

  30. GRANGER CAUSALITY • In time series analysis, sometimes, we would like to know whether changes in a variable will have an impact on changes other variables. • To find out this phenomena more accurately, we need to learn more about Granger Causality Test.

  31. GRANGER CAUSALITY • In principle, the concept is as follows: • If X causes Y, then, changes of X happened first then followed by changes of Y.

  32. GRANGER CAUSALITY • If X causes Y, there are two conditions to be satisfied: 1. X can help in predicting Y. Regression of X on Y has a big R2 2. Y can not help in predicting X.

  33. GRANGER CAUSALITY • In most regressions, it is very hard to discuss causality. For instance, the significance of the coefficient  in the regression only tells the ‘co-occurrence’ of x and y, not that x causes y. • In other words, usually the regression only tells us there is some ‘relationship’ between x and y, and does not tell the nature of the relationship, such as whether x causes y or y causes x.

  34. GRANGER CAUSALITY • One good thing of time series vector autoregression is that we could test ‘causality’ in some sense. This test is first proposed by Granger (1969), and therefore we refer it Granger causality. • We will restrict our discussion to a system of two variables, x and y. y is said to Granger-cause x if current or lagged values of y helps to predict future values of x. On the other hand, y fails to Granger-cause x if for all s > 0, the mean squared error of a forecast of xt+s based on (xt, xt−1, . . .) is the same as that is based on (yt, yt−1, . . .) and (xt, xt−1, . . .).

  35. GRANGER CAUSALITY • If we restrict ourselves to linear functions, x fails to Granger-cause x if • Equivalently, we can say that x is exogenous in the time series sense with respect to y, or y is not linearly informative about future x.

  36. GRANGER CAUSALITY • A variable X is said to Granger cause another variable Y, if Y can be better predicted from the past of X and Y together than the past of Y alone, other relevant information being used in the prediction (Pierce, 1977).

  37. GRANGER CAUSALITY • In the VAR equation, the example we proposed above implies a lower triangular coefficient matrix: Or if we use MA representations,

  38. GRANGER CAUSALITY • Consider a linear projection of yt on past, present and future x’s, where E(etx ) = 0 for all t and . Then y fails to Granger-cause x iff dj = 0 for j = 1, 2, . . ..

  39. TESTING GRANGER CAUSALITY Procedure 1) Check that both series are stationary in mean, variance and covariance (if necessary transform the data via logs, differences to ensure this) 2) Estimate AR(p) models for each series, where p is large enough to ensure white noise residuals. F tests and other criteria (e.g. Schwartz or Akaike) can be used to establish the maximum lag p that is needed. 3) Re-estimate both model, now including all the lags of the other variable 4) Use F tests to determine whether, after controlling for past Y, past values of X can improve forecasts Y (and vice versa)

  40. TEST OUTCOMES 1. X Granger causes Y but Y does not Granger cause X 2. Y Granger causes X but X does not Granger cause Y 3. X Granger causes Y and Y Granger causes X (i.e., there is a feedback system) 4. X does not Granger cause Y and Y does not Granger cause X

  41. TESTING GRANGER CAUSALITY • The simplest test is to estimate the regression which is based on using OLS and then conduct a F-test of the null hypothesis H0 : 1 = 2 = . . . = p = 0.

  42. TESTING GRANGER CAUSALITY 2.Run the following regression, and calculate RSS (full model) 3.Run the following limited regression, and calculate RSS (Restricted model).

  43. TESTING GRANGER CAUSALITY 4.Do the following F-test using RSS obtained from stages 2 and 3: F = [{(n-k) /q }.{(RSSrestricted-RSSfull) / RSSfull}] n: number of observations k: number of parameters from full model q: number of parameters from restricted model

  44. TESTING GRANGER CAUSALITY 5. If H0 rejected, then X causes Y. • This technique can be used in investigating whether or not Y causes X.

  45. Example of the Usage of Granger Test World Oil Price and Growth of US Economy • Does the increase of world oil price influence the growth of US economy or does the growth of US economy effects the world oil price? • James Hamilton did this study using the following model: Zt= a0+ a1 Zt-1+...+amZt-m+b1Xt-1 +…bmXt-m+εt Zt= ΔPt; changes of world price of oil Xt= log (GNPt/ GNPt-1)

  46. World Oil Price and Growth of US Economy • There are two causalities that need to be observed: (i) H0: Growth of US Economy does not influence world oil price Full: Zt= a0+ a1 Zt-1+...+amZt-m+b1Xt-1 +…+bmXt-m+εt Restricted: Zt= a0+ a1 Zt-1+...+amZt-m+ εt

  47. World Oil Price and Growth of US Economy (ii) H0 : World oil price does not influence growth of US Economy • Full : Xt= a0+ a1 Xt-1+ …+amXt-m+ b1Zt-1+…+bmZt-m+ εt • Restricted: Xt= a0+ a1 Xt-1+ …+amXt-m+ εt

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