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Nonstationary Time Series Data and Cointegration ECON 6002 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes.

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Presentation on theme: "Nonstationary Time Series Data and Cointegration ECON 6002 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes."— Presentation transcript:

1 Nonstationary Time Series Data and Cointegration ECON 6002 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes

2  12.1 Stationary and Nonstationary Variables  12.2 Spurious Regressions  12.3 Unit Root Tests for Stationarity  12.4 Cointegration  12.5 Regression When There is No Cointegration

3 Figure 12.1(a) US economic time series Yt-Y t-1 On the right hand side “Differenced series” Fluctuates about a rising trend Fluctuates about a zero mean

4 Figure 12.1(b) US economic time series Yt-Y t-1 On the right hand side “Differenced series”

5 Stationary if:

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7 Each realization of the process has a proportion rho of the previous one plus an error drawn from a distribution with mean zero and variance sigma squared It can be generalised to a higher autocorrelation order We just show AR(1)

8 We can show that the constant mean of this series is zero

9 We can also allow for a non-zero mean, by replacing yt with yt-mu Which boils down to (using alpha = mu(1-rho))

10 Or we can allow for a AR(1) with a fluctuation around a linear trend (mu+delta times t) The “de-trended” model, which is now stationary, behaves like an autoregressive model: With alpha =(mu(1-rho)+rho times delta) And lambda = delta(1-rho)

11 Figure 12.2 (a) Time Series Models

12 Figure 12.2 (b) Time Series Models

13 Figure 12.2 (c) Time Series Models

14 The first component is usually just zero, since it is so far in the past that it has a negligible effect now The second one is the stochastic trend

15  A random walk is non-stationary, although the mean is constant:

16 A random walk with a drift both wanders and trends:

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18 Both independent and artificially generated, but…

19 Figure 12.3 (a) Time Series of Two Random Walk Variables

20 Figure 12.3 (b) Scatter Plot of Two Random Walk Variables

21  Dickey-Fuller Test 1 (no constant and no trend)

22 Easier way to test the hypothesis about rho Remember that the null is a unit root: nonstationarity!

23  Dickey-Fuller Test 2 (with constant but no trend)

24  Dickey-Fuller Test 3 (with constant and with trend)

25 First step: plot the time series of the original observations on the variable.  If the series appears to be wandering or fluctuating around a sample average of zero, use Version 1  If the series appears to be wandering or fluctuating around a sample average which is non-zero, use Version 2  If the series appears to be wandering or fluctuating around a linear trend, use Version 3

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27  An important extension of the Dickey-Fuller test allows for the possibility that the error term is autocorrelated.  The unit root tests based on (12.6) and its variants (intercept excluded or trend included) are referred to as augmented Dickey-Fuller tests.

28 F = US Federal funds interest rate B = 3-year bonds interest rate

29 In STATA: use usa, clear gen date = q(1985q1) + _n - 1 format %tq date tsset date TESTING UNIT ROOTS “BY HAND”: * Augmented Dickey Fuller Regressions regress D.F L1.F L1.D.F regress D.B L1.B L1.D.B

30 In STATA: TESTING UNIT ROOTS “BY HAND”: * Augmented Dickey Fuller Regressions regress D.F L1.F L1.D.F regress D.B L1.B L1.D.B

31 In STATA: Augmented Dickey Fuller Regressions with built in functions dfuller F, regress lags(1) dfuller B, regress lags(1) Choice of lags if we want to allow For more than a AR(1) order

32 In STATA: Augmented Dickey Fuller Regressions with built in functions dfuller F, regress lags(1)

33 In STATA: Augmented Dickey Fuller Regressions with built in functions dfuller F, regress lags(1) Alternative: pperron uses Newey-West standard errors to account for serial correlation, whereas the augmented Dickey-Fuller test implemented in dfuller uses additional lags of the first-difference variable. Also consider now using DFGLS (Elliot Rothenberg and Stock, 1996) to counteract problems of lack of power in small samples. It also has in STATA a lag selection procedure based on a sequential t test suggeste by Ng and Perron (1995)

34 In STATA: Augmented Dickey Fuller Regressions with built in functions dfuller F, regress lags(1) Alternatives: use tests with stationarity as the null KPSS (Kwiatowski, Phillips, Schmidt and Shin. 1992) which also has an automatic bandwidth selection tool or the Leybourne & McCabe test.

35 The first difference of the random walk is stationary It is an example of a I(1) series (“integrated of order 1” First-differencing it would turn it into I(0) (stationary) In general, the order of integration is the minimum number of times a series must be differenced to make it stationarity

36 So now we reject the Unit root after differencing once: We have a I(1) series

37 In STATA: ADF on differences dfuller D.F, noconstant lags(0) dfuller D.B, noconstant lags(0)

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39 Not the same as for dfuller, since the residuals are estimated errors no actual ones Note: unfortunately STATA dfuller will not notice and give you erroneous critical values!

40 Check: These are wrong!

41 The null and alternative hypotheses in the test for cointegration are:

42  First Difference Stationary The variable y t is said to be a first difference stationary series. Then we revert to the techniques we saw in Ch. 9

43 Manipulating this one you can construct and Error Correction Model to investigate the SR dynamics of the relationship between y and x

44 where and

45 To summarize:  If variables are stationary, or I(1) and cointegrated, we can estimate a regression relationship between the levels of those variables without fear of encountering a spurious regression.  Then we can use the lagged residuals from the cointegrating regression in an ECM model  This is the best case scenario, since if we had to first-differentiate the variables, we would be throwing away the long-run variation  Additionally, the cointegrated regression yields a “superconsistent” estimator in large samples

46 To summarize:  If the variables are I(1) and not cointegrated, we need to estimate a relationship in first differences, with or without the constant term.  If they are trend stationary, we can either de-trend the series first and then perform regression analysis with the stationary (de-trended) variables or, alternatively, estimate a regression relationship that includes a trend variable. The latter alternative is typically applied.

47 Slide Principles of Econometrics, 3rd Edition  Augmented Dickey-Fuller test  Autoregressive process  Cointegration  Dickey-Fuller tests  Mean reversion  Order of integration  Random walk process  Random walk with drift  Spurious regressions  Stationary and nonstationary  Stochastic process  Stochastic trend  Tau statistic  Trend and difference stationary  Unit root tests

48 Slide Principles of Econometrics, 3rd Edition Kit Baum has really good notes on these topics that can be used to learn also about extra STATA commands to handle the analysis: For example, some of you should look at seasonal unit root analysis (command HEGY in STATA) Panel unit roots would be here Further issues

49 Slide Principles of Econometrics, 3rd Edition You may want to some time consider unit root tests that allow for structural Breaks You can also take a look at the literature review in this working paper: Further issues

50 Slide Principles of Econometrics, 3rd Edition When you deal with more than 2 regressors you should consider the Johansen’s method to examine the cointegration relationships Further issues


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