# 2. What are the theoretical and practical consequences of autocorrelation?

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1 Lecture 10 Serial Correlation In this lecture, you will learn the following: 1. What is the nature of autocorrelation? 2. What are the theoretical and practical consequences of autocorrelation? 3. Since the assumption of nonautocorrelation relates to the unobservable disturbance ε i, how does one know that there is autocorrelation in any given situation? 4. What are the remedies for the problem of autocorrelation? 10.1 Nature of Autocorrelation Below are some concepts about the independence and serial correlation (or autocorrelation) for a time series. Serial independence: error terms ε t and ε s, for different observations t and s, are independently distributed. When one deals with time series data, this assumption is frequently violated. Error terms for time periods not too far apart may be correlated. Serial correlation (or autocorrelation): error terms ε t and ε s, for t s, are correlated. This property is frequently observed from time series data. 1

2 2 LECTURE 10 SERIAL CORRELATION Besides, three factors can also lead to serially correlated errors. They are: (1) omitted variables, (2) ignoring nonlinearities, and (3) measurement errors. For example, suppose a dependent variable Y t is related to the independent variables X t1 and X t2, but the investigator does not include X t2 in the model. The effect of this variable will be captured by the error term ε t. Because many time series exhibit trends over time, X t2 is likely to depend on X t 1,2, X t 2,2,.... This will translate into apparent correlation between ε t and ε t 1, ε t 2,..., thereby violating the serial independence assumption. Thus, growth in omitted variables could cause autocorrelation in errors. Serial correlation can also be caused by misspecification of the functional form. Suppose, for example, the relationship between Y and X is quadratic but we assume a straight line. Then the error term ε t will depend on X 2. If X has been growing over time, ε t will also exhibit such growth, indicating autocorrelation. Systematic errors in measurement can also cause autocorrelation. For example, suppose a firm is updating its inventory in a given period. If there is a systematic error in the way it was measured, cumulative inventory stock will reflect accumulated measurement errors. This will show up as serial correlation. Example: Consider the consumption of electricity during different hours of the day. Because the temperature patterns are similar between successive time periods, we can expect consumption patterns to be correlated between neighboring periods. If the model is not properly specified, this effect may show up as high correlation among errors from nearby periods. Example: Consider stock market data. The price of a particular security or a stock market index at the close of successive days or during successive hours is likely to be serially correlated Serial Correlation of the First Order If serial correlation is present, then Cov(ε t, ε t s ) 0 for t s; that is, the error for the period t is correlated with the error for the period s. There are

3 3 LECTURE 10 SERIAL CORRELATION many forms of process to capture the serial correlation. Two basic processes are the atuoregressive (AR) process and the moving average (MA) process. Given u t is a white noise, u t (0, σ 2 u), the pth order of AR process and the q-th order of MA process are defined as follows: AR(p): ε t = ρ 1 ε t 1 + ρ 2 ε t ρ p ε t p + u t MA(q): ε t = u t + θ 1 u t θ q u t q In this chapter, we only discuss the serial correlation in the form of autoregression. Specify the regression model with serial correlated error as: y t = β 0 + β 1 x t + ε t ε t = ρε t 1 + u t ; 1 < ρ < 1 ρ is called the first-order autocorrelation coefficient. The error term ε described above follows a first-order autoregressive process [AR(1)]. The white noise u t is assumed to satisfy the following conditions. DEFINITION OF WHITE NOISE with ZERO MEAN: {u t, t = 1, 2,, T } are independently and identically distributed with zero mean and constant variance so that E(u t ) = 0, E(u 2 t ) = σu 2 <, and E(u t u t s ) = 0 for s 0. REMARKS: By assuming u t as a white noise series with zero mean, ε t is correlated with all past errors. (reason: ε t depends on ε t 1, so they are correlated. Though ε t does not depend directly on ε t 2, it does do so indirectly through ε t 1 because ε t 1 depends on ε t 2. ) positive autocorrelation: when the covariance is positive. negative autocorrelation: when the covariance is negative. Cov(ε t, ε s ) = σ 2 uρ s, for s Consequences of Ignoring Serial Correlation If we ignore the serial correlation in error, the impacts on the OLS estimates are as follows:

4 4 LECTURE 10 SERIAL CORRELATION OLS estimates (and forecasts based on them) are unbiased and consistent even if the error terms are serially correlated. The problem is with the efficiency of the estimates. In the proof of the Gauss-Markov Theorem that established efficiency, one of the steps involved minimization of the variance of the linear combination a t ε t : Var ( at ε t ) = a 2 t σ 2 ε + t s at a s Cov(ε t, ε s ) where the summation is over all t and s that are different. If Cov(ε t, ε s ) 0, the second term on the right-hand side will not vanish. Therefore, the best linear unbiased estimator (BLUE) that minimizes Var( a t ε t ) will not be the same as the OLS estimator. That is, OLS estimates are not BLUE and are hence inefficient. Thus the consequences of ignoring autocorrelation are the same as those of ignoring heteroskedasticity, namely, the OLS estimates and forecasts are unbiased and consistent, but are inefficient. If the serial correlation in ε t is positive and the independent variable X t grows over time, then the estimated residual variance (ˆσ 2 ) will be an underestimate and the value of R 2 will be an overestimate. In other words, the goodness of fit will be exaggerated and the estimated standard errors will be smaller than the true standard errors. In the general case, the variances of the OLS estimates for regression coefficients will be biased Effects on Tests of Hypotheses In the case in which the serial correlation in ε t is positive and the independent variable x t grows over time, estimated standard errors will be smaller than the true standard errors, and hence the estimated standard errors will be underestimated. Therefore, the t-statistics will be overestimates a regression coefficient that appears to be significant may not really be so Effects: The estimated variances of the parameters will be biased and inconsistent. Thus the t- and F -tests are no longer valid.

5 5 LECTURE 10 SERIAL CORRELATION Effect on Forecasting Forecasts based on OLS estimated will be unbiased. But forecasts are inefficient with larger variances. By explicitly taking into account the serial correlation among residuals, it is possible to generate better forecasts than those generated by the OLS procedure. Suppose we ignore the AR(1) serial correlation and obtain OLS estimates ˆα and ˆβ. The OLS prediction would be ŷ t = ˆβ 0 + ˆβ 1 x t. However, in the case of first-order serial correlation, ε t is predictable from ρε t 1 + u t, provided ρ can be estimated (call it ˆρ). Once we have e t = ˆρû t 1, the residual for the previous period (û t 1 ) is known at time t. Therefore, the AR(1) prediction will be ỹ t = ˆβ 0 + ˆβ 1 x t + ˆρũ t 1 = ˆβ 0 + ˆβ 1 x t + ˆρ ( y t ˆβ 0 ˆβ 1 x t 1 ) by making use of the fact that û t 1 = y t 1 ˆβ 0 ˆβ 1 x t 1. Thus ỹ t will be more efficient than that obtained by the OLS procedure. The procedure for estimating ρ is described below. PROPERTY If serial correlation among the stochastic disturbance terms in a regression model is ignored and the OLS procedure is used to estimate the parameters, the following properties hold: 1. The estimates and forecasts based on them will still be unbiased and consistent. The consistency property does not hole, however, if lagged dependent variables are included as explanatory variables. 2. The OLS estimates are no longer BLUE and will be inefficient. Forecasts will also be inefficient. 3. The estimated variances of the regression coefficients will be biased, and hence tests of hypotheses are invalid. If the serial correlation is positive and the independent variables X t is growing over time, then the standard errors will underestimate of the true values. This means that the computed R 2 will be an overestimate, indicating a better fit

6 6 LECTURE 10 SERIAL CORRELATION than actually exists. Also, the t-statistics in such a case will tend to appear more significant than they actually are Testing for First-Order Serial Correlation The Residual Plot Residual plot: a graph of the estimated residuals e t against time (t). If successive residuals tend to cluster on one side of the zero line of the other, it is a graphical indication of the presence of serial correlation. As the first step toward identifying the presence of serial correlation, it is a good practice to plot e t against t and look for the clustering effect The Durbin-Watson Test Durbin and Watson (1950, 1951): For the multiple regression model with AR(1) error: y t = β 0 + β 1 x t1 + β 2 x t2 + + β k x tk + ε t ε t = ρε t 1 + u t 1 < ρ < 1 Durbin-Watson statistic is calculated by below steps: STEP 1: Estimate the model by OLS and compute the residuals e t as y t ˆβ 0 ˆβ 1 x t1 ˆβ k x tk. STEP 2: Compute the Durbin-Watson statistic: d = Tt=2 (e t e t 1 ) 2 Tt=1 e 2 t It is shown later that 0 d 4. The exact distribution of d depends on ρ, which is unknown, as well as on the observations on the x s. Durbin and Watson (1950) showed that the distribution of d is bounded by two limiting distributions. See Savin and White (1977) for the critical values for the limiting distributions of d, namely d U and d L, for different sample size T and the number of coefficients k, not counting

7 7 LECTURE 10 SERIAL CORRELATION the constant term. These are used to construct critical regions for the Durbin-Watson test. STEP 3a: To test H 0 : ρ = 0 against H 1 : ρ > 0 (one-tailed test), we at first have to find the critical values for the Durbin-Watson statistic: d L and d U. Reject H 0 if d d L. If d d U, we cannot reject H 0. If d L < d < d U, the test is inconclusive. STEP 3b: To test for negative serial correlation (that is, for H 1 : ρ < 0), use 4 d. This is done when d is greater than 2. If 4 d d L, we conclude that there is significant negative autocorrelation. If 4 d d U, we conclude that there is no negative autocorrelation. The test is inconclusive if d L < d < d U. REMARKS: The inconclusiveness of the DW test arisen from the fact that there is no exact small-sample distribution for the DW statistic d. When the test is inconclusive, one might try the Lagrange multiplier test described next. EXPLANATION: from the estimated residuals we can obtain an estimate of the first-order serial correlation coefficient as ˆρ = Tt=2 e t e t 1 Tt=1 e 2 t This estimate is approximately equal to the one obtained by regressing e t against û t 1 without a constant term. It can be shown that DW statistic d is approximately equal to 2(1 ˆρ) d 2(1 ˆρ) Because ρ can range from 1 to +1, the range for d is 0 to 4. When ρ is 0, d is 4. Thus, a DW statistic of nearly 2 means there is no first-order serial correlation. A strong positive autocorrelation means ρ is close to +1. This indicates low values of d. Similarly, values of d close to 4 indicate a strong negative correlation; that is, ρ is close to 1. The DW test is invalid if the right-hand side of regression equation includes lagged dependent variables: y t 1, y t 2,....

8 8 LECTURE 10 SERIAL CORRELATION The Lagrange Multiplier Test The LM statistic is useful in identifying serial correlation not only of the first order but of higher orders as well. Here we confine ourselves to the first-order case. The general case of AR(p) is discussed later. y t = β 0 + β 1 x t1 + β 2 x t2 + + β k x tk + ρε t 1 + u t The test for ρ = 0 can be treated as the LM test for the addition of the variable ε t 1 (which is unknown, and hence one would use e t 1 instead). Steps for Carrying Out the LM Test: STEP 1: Estimate the regression model by OLS and compute its estimated residuals, e t. STEP 2: Regress e t against a constant, x t1,, x tk, and e t 1, using the T 1 observations 2 through T. Then the LM statistic can be calculated by (T 1)R 2 e, where R 2 e is the R-squared from the auxiliary regression. T 1 is used because the efficient number of observations is T 1. STEP 3: Reject the null hypothesis of zero autocorrelation in favor of the alternative that ρ 0 if (T 1)R 2 e > χ 2 1,(1 α), the value of χ2 1 in the chi-square distribution with 1 d.f. such that the area to the right of it is 1 α, and α is the significance level. REMARKS: If there were serial correlation in the residuals, we would expect e t to be related to e t 1. This is the motivation behind the auxiliary regression in which e t 1 is included along with all the independent variables in the model. The LM test does not have the inconclusiveness of the DW test. However, the LM test is a large-sample test and would need at least 30 d.f. to be meaningful.

9 9 LECTURE 10 SERIAL CORRELATION 10.5 Treatment of Serial Correlation Model Formulation in First Differences Granger and Newbold (1974 and 1976) have cautioned against spurious regressions that might arise when a regression is based on levels of trending variables, especially when a significant DW statistic. A common way to get around this problem is to formulate models in terms of first difference which is the difference between the value at time t and at time t 1. That is, we estimate y t = β x t + u t where y t = y t y t 1 and x t = x t x t 1. However, the solution of using first differences might not always be appropriate. The first difference model can be rewritten as y t = y t 1 + β 1 x t β 1 x t 1 + u t Estimation Procedures When modified function forms do not eliminate autocorrelation, several estimation procedures are available that will produce more efficient estimates than those obtained by OLS procedure. These methods need to be applied only for time series data. With cross-section data one can rearrange the observations in any manner and get a DW statistic that is acceptable. REMARKS: The DW test is meaningless for cross-section data because one can rearrange the observations in any manner and get a DW statistic that is acceptable. Because time series data cannot be rearranged, one needs to be concerned about possible serial correlation. Some usual procedure for estimating models with AR(1) serial correlation are listed below. Cochrane-Orcutt (CORC) Iterative Procedure Cochrane and Orcutt (1949): This procedure requires the transformation of the regression model to a form in which the OLS procedure is applicable.

10 10 LECTURE 10 SERIAL CORRELATION Quasi-differencing or generalized differencing transformation: generate variables y and x. Rewrite the model for the period t 1 we get y t 1 = β 0 + β 1 x t 1,1 + β 2 x t 1,2 + + β k x t 1,k + ε t 1 Multiplying by ρ and subtracting from the original equation, we obtain y t ρy t 1 = β 0 (1 ρ) + β 1 [x t1 ρx t 1,1 ] + β 2 [x t2 ρx t 1,2 ] β k [x tk ρx t 1,k ] + u t where we have used the fact that ε t = ρε t 1 + u t. Rewrite this equation again, y t = β 0 + β 1 x t1 + β 2 x t2 + + β k x tk + u t (10.1) where y t = y t ρy t 1, β 0 = β 0 (1 ρ), and x ti = x ti ρx t 1,i, for t = 2, 3,..., T and i = 1,..., k. Note that the error term satisfies all the properties needed for applying the OLS procedure. If ρ were known, we could apply OLS to the transformed y and x and obtain estimates that are BLUE. However, ρ is unknown and has to be estimated from the sample. Steps for Carrying Out the ORCR Procedure: STEP 1: Estimate the original equation by OLS and compute its residuals e t. STEP 2: Estimate the first-order serial correlation coefficient (ˆρ) by regressing e t against e t 1. STEP 3: Transform the variables as follows: y t = y t ˆρy t 1, x t1 = x t1 ˆρx t 1,1, and so on STEP 4: Regress yt against a constant, x t1, x t2,..., x tk and get OLS estimate of βj, j = 0, 1,, k. STEP 5: Derive estimates for the β 0 as β 0/(1 ˆρ). Plug β 0 and estimated β j, j = 1,, k into the original regression, and then obtain a new set of estimates for ε t. Then go back and repeat Step 2 with these new values until the following stopping rules applies.

11 11 LECTURE 10 SERIAL CORRELATION STEP 6: This iterative procedure can be stopped when the estimates of ρ from two successive iterations differ by no more than some preselected values, such as the final ˆρ is then used to get the CORC estimates for transformed regression. Hildreth-Lu (HILU) Search Procedure Steps of Hildreth and Lu (1960) Procedure: STEP 1: Choose a value of ρ (say ρ 1 ). Using this value, transform the variables and estimate the transformed regression by OLS. STEP 2: From these estimates, derive û t from Equation (10.1) and the error sum of squares associated with it. Call it SSRû(ρ 1 ), Nest choose a different ρ (ρ 2 ) and repeat Steps 1 and 2. STEP 3: By varying ρ from 1 to +1 in some systematic way (say, at steps of length 0.05 or 0.01), we can get a series of values of SSRû(ρ). Choose that ρ for which SSRûis a minimum. This is the final ρ that globally minimizes the error sum of squares of the transformed model. Equation (10.1) is then estimated with the final ρ as the optimum solution. A Comparison of the Two Procedures THE HILU procedure is basically searches for the values of ρ between 1 and +1 that minimizes the sum of squares of the residuals of the transformed equation. If the step intervals are small, the procedure involves running a large number if regressions; hence, compared to the CORC procedure, the HILU method is computer intensive. The CORC procedure iterates to a local minimum of SSR(ρ) and might miss the global minimum if there is more than one local minimum High-Order Serial Correlation AR(p): pth-order autoregressive process of the residuals y t = β 0 + β 1 x t1 + β 2 x t2 + + β k x tk + ε t ε t = ρ 1 ε t 1 + ρ 2 ε t 2 + ρ 3 ε t ρ p ε t p + u t

12 12 LECTURE 10 SERIAL CORRELATION Lagrange Multiplier Test of Higher-Order Autocorrelation Combine the above two equations, we obtain y t = β 0 + β 1 x t1 + + β k x tk + ρ 1 ε t 1 + ρ 2 ε t ρ p ε t p + u t The null hypothesis is that each of the ρs is zero (that is, ρ 1 = ρ 2 = = ρ p = 0) against the alternative that at least one of them is not zero. The null hypothesis is very similar to the one for testing the addition of new variables. In this case, the new variables are ε t 1, ε t 2,..., ε t p, which can be estimated by e t 1, e t 2,..., e t p. Steps of LM test for higher order AR(p) serial correlation: STEP 1 Estimate the original regression by OLS and obtain the residuals e t. STEP 2 Regress e t against all the independent variables x 1,, x k plus e t 1, e t 2,, e t p. The effective number of observations used in the auxiliary regression is T p because t p is defines only for the period p + 1 to T. STEP 3 Compute (T p)r 2 e from the auxiliary regression run in Step 2. If it exceeds χ 2 p,1 α, the value of the chi-square distribution with p d.f. such that the area to the right is 1 α, then reject H 0 : ρ 1 = ρ 2 = = ρ p = 0 in favor of H 1 : at least one of the ρs is significantly different from zero Estimating a Model with General Order Autoregressive Errors If the LM test rejects the null hypothesis of no serial correlation, we must estimate efficiently the parameters of the below equation with pth-order autoregressive error: y t ρ 1 y t 1 ρ 2 y t 2 ρ 3 y t 3 ρ p y t p = β 0 (1 ρ 1 ρ 2 ρ p ) + β 1 [x t1 ρ 1 x t 1,1 ρ p x t p,1 ] + + β k [x tk ρ 1 x t 1,k ρ p x t p,k ] + u t

13 13 LECTURE 10 SERIAL CORRELATION STEP 1 Estimate the original regression model by OLS and obtain the residuals, e t. STEP 2 Regress e t against e t 1, e t 2,..., e t p (with no constant term) to obtain the estimates ˆρ 1, ˆρ 2, and so on of the parameters of e t j, j = 1, 2,, p. Here only T p observations are used. STEP 3 Using these estimates of ˆρ j, j = 1,, k, transform y and x s to get the dependent and independent variables to get the new transformed variables y and x s as y t = y t ˆρ 1 y t 1 ˆρ p y t p and x ti = x ti ˆρ 1 x t 1,i ˆρ p x t p,i, i = 1, 2,, k. STEP 4 Estimate the transformed model by regressing y t against x ti, i = 1, 2,, k. Using the estimate β 0, we calculate β 0 = β 0/(1 ˆρ 1 ˆρ 2 ˆρ p ). STEP 5 Then plug this β 0 along with estimates of β j, j = 1, 2,, k into the original regression to compute a revised estimate of the residuals ε t. Then go back to Step 2 and iterate until some criterion is satisfied. REMARKS: The final ρs obtained in Step 5 can then be used to make one last transformation of the data to estimate β s Forecasts and Goodness of Fit in AR Models The predicted y t is: ŷ t = ˆβ 0 + ˆβ 1 x t1 + + ˆβ k x tk + ˆρ 1 ε t 1 + ˆρ 2 ε t ˆρ p ε t p The forecast of y t obtained this way will be more efficient than the OLS prediction that ignores the e terms Engle s ARCH Test The variance of prediction errors is not a constant but differs from period to period. For instance, free-floating exchange rates fluctuate a great deal, making their variances larger. Increased volatility of security prices are often indicators that the variances are not constant over time.

14 14 LECTURE 10 SERIAL CORRELATION Engle (1982) introduced a new approach to modelling heteroskedasticity in time series context. For the regression model, where Var(ε t F t 1 = σ 2 t. y t = β 0 + β 1 x t1 + β 2 x t2 + + β k x tk + ε t ARCH (autoregressive conditional heteroskedasticity) model: Specify the conditional variance process as follows: σ 2 t = α 0 + α 1 σ 2 t α p σ 2 t p + v t This conditional variance process is denoted as the pth order ARCH, ARCH(p), process. The conditional variance at time t depends on those in previous periods and hence the term conditional heteroskedasticity. ARCH test: this is test for the null hypothesis H 0 : α 1 = α 2 = = α p = 0. STEP 1 Estimate β s in the original equation by OLS. STEP 2 Compute the residual e t = y t ˆβ 0 ˆβ 1 x t1 ˆβ 2 x t2 ˆβ k x tk, square it to obtain e 2 t, and generate e 2 t 1, e 2 t 2,, e 2 t p. STEP 3 Regress e 2 t against a constant, e 2 t 1, e 2 t 2,..., and e 2 t p. This is the auxiliary regression, which uses T p observations. STEP 4 Using R 2 e 2, the R-squared of the auxiliary regression, we compute LM=(T p)r 2 e 2. Under the null hypothesis H 0 : α 1 = α 2 = = α p = 0, (T p)r 2 e 2 has the chi-square distribution with p d.f. Reject H 0 if (T p)r 2 e 2 > χ 2 p,1 α, the point on χ 2 p with an area 1 α to the right of it. References Greene, W. H., 2003, Econometric Analysis, 5th ed., Prentice Hall. Chapter 12. Gujarati, D. N., 2003, Basic Econometrics, 4th ed., McGraw-Hill. Chapter 11. Ramanathan, R., 2002, Introductory Econometrics with Applications, 5th ed., Harcourt College Publishers. Chapter 9. Ruud, P. A., 2000, An Introduction to Classical Econometric Theory, 1st ed., Oxford University Press. Chapter 19.

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