Chapter 3: Classical Linear Regression Model PDF

Chapter 3 A brief overview of the classical linear regression model ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 1  Regression Regression is probably the single most important tool at the ec...

Chapter 3 A brief overview of the classical linear regression model ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 1  Regression Regression is probably the single most important tool at the econometrician’s disposal. But what is regression analysis? It is concerned with describing and evaluating the relationship between a given variable (usually called the dependent variable) and one or more other variables (usually known as the independent variable(s)). ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 2  Some Notation Denote the dependent variable by y and the independent variable(s) by x1, x2,... , xk where there are k independent variables. Some alternative names for the y and x variables: y x dependent variable independent variables regressand regressors effect variable causal variables explained variable explanatory variable Note that there can be many x variables but we will limit ourselves to the case where there is only one x variable to start with. In our set-up, there is only one y variable. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 3  Regression is different from Correlation If we say y and x are correlated, it means that we are treating y and x in a completely symmetrical way. In regression, we treat the dependent variable (y) and the independent variable(s) (x’s) very differently. The y variable is assumed to be random or “stochastic” in some way, i.e. to have a probability distribution. The x variables are, however, assumed to have fixed (“non-stochastic”) values in repeated samples. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 4  Simple Regression For simplicity, say k=1. This is the situation where y depends on only one x variable. Examples of the kind of relationship that may be of interest include: – How asset returns vary with their level of market risk – Measuring the long-term relationship between stock prices and dividends. – Constructing an optimal hedge ratio ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 5  Simple Regression: An Example Suppose that we have the following data on the excess returns on a fund manager’s portfolio (“fund XXX”) together with the excess returns on a market index: Year, t Excess return Excess return on market index = rXXX,t – rft = rmt - rft 1 17.8 13.7 2 39.0 23.2 3 12.8 6.9 4 24.2 16.8 5 17.2 12.3 We have some intuition that the beta on this fund is positive, and we therefore want to find whether there appears to be a relationship between x and y given the data that we have. The first stage would be to form a scatter plot of the two variables. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 6  Graph (Scatter Diagram) 45 Excess return on fund XXX 40 35 30 25 20 15 10 5 0 0 5 10 15 20 25 Excess return on market portfolio ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 7  Finding a Line of Best Fit We can use the general equation for a straight line, y=a+bx to get the line that best “fits” the data. However, this equation (y=a+bx) is completely deterministic. Is this realistic? No. So what we do is to add a random disturbance term, u into the equation. yt =  + xt + ut where t = 1,2,3,4,5 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 8  Why do we include a Disturbance term? The disturbance term can capture a number of features: - We always leave out some determinants of yt - There may be errors in the measurement of yt that cannot be modelled. - Random outside influences on yt which we cannot model ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 9  Determining the Regression Coefficients So how do we determine what  and  are? Choose  and  so that the (vertical) distances from the data points to the fitted lines are minimised (so that the line fits the data as closely as possible): y x ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 10  Ordinary Least Squares The most common method used to fit a line to the data is known as OLS (ordinary least squares). What we actually do is take each distance and square it (i.e. take the area of each of the squares in the diagram) and minimise the total sum of the squares (hence least squares). Tightening up the notation, let yt denote the actual data point t ŷt denote the fitted value from the regression line û t denote the residual, yt - ŷt ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 11  Actual and Fitted Value y yi û i ŷi xi x ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 12  How OLS Works 5 So min. uˆ1 + uˆ 2 + uˆ3 + uˆ 4 + uˆ5 , or minimise 2 2 2 2 2  uˆ t =1 2 t. This is known as the residual sum of squares. But what was û t ? It was the difference between the actual point and the line, yt - ŷt. ( So minimising  ty − ˆ y t )2 is equivalent to minimising  t ˆ u 2 with respect to $ and $. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 13  Deriving the OLS Estimator ˆ t = ˆ + ˆxt , so let But y L =  ( yt − yˆ t ) 2 =  ( yt − ˆ − ˆxt ) 2 t i Want to minimise L with respect to (w.r.t.) $ and $ , so differentiate L w.r.t. $ and $ L ˆ t  = −2 ( yt − ˆ − ˆxt ) = 0 (1) L = −2 xt ( yt − ˆ − ˆxt ) = 0 (2) ˆ t From (1),  ( y t − ˆ − ˆxt ) = 0  y t − Tˆ − ˆ  xt = 0 t But  y t = Ty and  x t = Tx. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 14  Deriving the OLS Estimator (cont’d) So we can write Ty − Tˆ − Tˆx = 0 or y − ˆ − ˆx = 0 (3) From (2),  xt ( yt − ˆ − ˆxt ) = 0 (4) t From (3), ˆ = y − ˆx (5) Substitute into (4) for $ from (5),  xt ( yt − y + ˆx − ˆxt ) = 0 t  t t  t x y − y x + ˆ x  t x − ˆ   t =0 x 2 t  t t x y − T y x + ˆ Tx 2 − ˆ   t =0 x 2 t ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 15  Deriving the OLS Estimator (cont’d) Rearranging for $ , ˆ (Tx 2 −  xt2 ) = Tyx −  xt yt So overall we have ˆ =  xt yt − Tx y andˆ = y − ˆx   xt2 − Tx 2 This method of finding the optimum is known as ordinary least squares. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 16  What do We Use $ and $ For? In the CAPM example used above, plugging the 5 observations in to make up the formulae given above would lead to the estimates $ = -1.74 and $= 1.64. We would write the fitted line as: yˆ t = −1.74 + 1.64x t Question: If an analyst tells you that she expects the market to yield a return 20% higher than the risk-free rate next year, what would you expect the return on fund XXX to be? Solution: We can say that the expected value of y = “-1.74 + 1.64 * value of x”, so plug x = 20 into the equation to get the expected value for y: yˆ i = −1.74 + 1.64 20 = 31.06 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 17  Accuracy of Intercept Estimate Care needs to be exercised when considering the intercept estimate, particularly if there are no or few observations close to the y-axis: y 0 x ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 18  The Population and the Sample The population is the total collection of all objects or people to be studied, for example, Interested in Population of interest predicting outcome the entire electorate of an election A sample is a selection of just some items from the population. A random sample is a sample in which each individual item in the population is equally likely to be drawn. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 19  The DGP and the PRF The population regression function (PRF) is a description of the model that is thought to be generating the actual data and the true relationship between the variables (i.e. the true values of  and ). The PRF is yt =  + xt + ut The SRF is yˆ t = ˆ + ˆxt and we also know that uˆt = yt − yˆ t. We use the SRF to infer likely values of the PRF. We also want to know how “good” our estimates of  and  are. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 20  Linearity In order to use OLS, we need a model which is linear in the parameters ( and  ). It does not necessarily have to be linear in the variables (y and x). Linear in the parameters means that the parameters are not multiplied together, divided, squared or cubed etc. Some models can be transformed to linear ones by a suitable substitution or manipulation, e.g. the exponential regression model Yt = e X t eut ln Yt =  +  ln X t + ut Then let yt=ln Yt and xt=ln Xt yt =  + xt + ut ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 21  Linear and Non-linear Models This is known as the exponential regression model. Here, the coefficients can be interpreted as elasticities. Similarly, if theory suggests that y and x should be inversely related:  yt =  + + ut xt then the regression can be estimated using OLS by substituting 1 zt = xt But some models are intrinsically non-linear, e.g.  yt =  + xt + ut ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 22  Estimator or Estimate? Estimators are the formulae used to calculate the coefficients Estimates are the actual numerical values for the coefficients. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 23  The Assumptions Underlying the Classical Linear Regression Model (CLRM) The model which we have used is known as the classical linear regression model. We observe data for xt, but since yt also depends on ut, we must be specific about how the ut are generated. We usually make the following set of assumptions about the ut’s (the unobservable error terms): Technical Notation Interpretation 1. E(ut) = 0 The errors have zero mean 2. Var (ut) = 2 The variance of the errors is constant and finite over all values of xt 3. Cov (ui,uj)=0 The errors are statistically independent of one another 4. Cov (ut,xt)=0 No relationship between the error and corresponding x variate ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 24  The Assumptions Underlying the CLRM Again An alternative assumption to 4., which is slightly stronger, is that the xt’s are non-stochastic or fixed in repeated samples. A fifth assumption is required if we want to make inferences about the population parameters (the actual  and ) from the sample parameters ( $ and $ ) Additional Assumption 5. ut is normally distributed ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 25  Properties of the OLS Estimator If assumptions 1. through 4. hold, then the estimators $ and$ determined by OLS are known as Best Linear Unbiased Estimators (BLUE). What does the acronym stand for? “Estimator” - $ is an estimator of the true value of . “Linear” - $ is a linear estimator “Unbiased” - On average, the actual value of the $ and $’s will be equal to the true values. “Best” - means that the OLS estimator $ has minimum variance among the class of linear unbiased estimators. The Gauss-Markov theorem proves that the OLS estimator is best. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 26  Consistency/Unbiasedness/Efficiency Consistent The least squares estimators $ and $ are consistent. That is, the estimates will converge to their true values as the sample size increases to infinity. Need the assumptions E(xtut)=0 and Var(ut)=2 <  to prove this. Consistency implies that  lim Pr ˆ −    = 0   0 T →  Unbiased The least squares estimates of $ and $ are unbiased. That is E($)= and E($ )= Thus on average the estimated value will be equal to the true values. To prove this also requires the assumption that E(ut)=0. Unbiasedness is a stronger condition than consistency. Efficiency An estimator $ of parameter  is said to be efficient if it is unbiased and no other unbiased estimator has a smaller variance. If the estimator is efficient, we are minimising the probability that it is a long way off from the true value of . ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 27  Precision and Standard Errors Any set of regression estimates of $ and $ are specific to the sample used in their estimation. Recall that the estimators of  and  from the sample parameters ($ and $) are ˆ =  t 2 t given by x y − Tx y andˆ = y − ˆx  xt − Tx 2 What we need is some measure of the reliability or precision of the estimators ( $ and $ ). The precision of the estimate is given by its standard error. Given assumptions 1 - 4 above, then the standard errors can be shown to be given by  t =s  t , 2 2 x x SE (ˆ ) = s T  ( xt − x ) 2 T  xt2 − T 2 x 2 1 1 SE ( ˆ ) = s =s  ( xt − x ) 2 t x 2 − T x 2 where s is the estimated standard deviation of the residuals. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 28  Estimating the Variance of the Disturbance Term The variance of the random variable ut is given by Var(ut) = E[(ut)-E(ut)]2 which reduces to Var(ut) = E(ut2) We could estimate this using the average of ut2: 1 s2 = T  ut2 Unfortunately this is not workable since ut is not observable. We can use the sample counterpart to ut, which is û t : 1 2 2 s = T  uˆ t But this estimator is a biased estimator of 2. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 29  Estimating the Variance of the Disturbance Term (cont’d) An unbiased estimator of  is given by s=  t ˆ u 2 T −2 where  uˆ 2 t is the residual sum of squares and T is the sample size. Some Comments on the Standard Error Estimators 1. Both SE($ ) and SE($ ) depend on s2 (or s). The greater the variance s2, then the more dispersed the errors are about their mean value and therefore the more dispersed y will be about its mean value. 2. The sum of the squares of x about their mean appears in both formulae. The larger the sum of squares, the smaller the coefficient variances. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 30  Some Comments on the Standard Error Estimators Consider what happens if (  tx − x )2 is small or large: y y y y x x 0 x 0 x ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 31  Some Comments on the Standard Error Estimators (cont’d) 3. The larger the sample size, T, the smaller will be the coefficient variances. T appears explicitly in SE($ ) and implicitly in SE( $ ). ( T appears implicitly since the sum  tx − x )2 is from t = 1 to T. 4. The term  xt appears in the SE($ ). 2 The reason is that  xt measures how far the points are away from the 2 y-axis. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 32  Example: How to Calculate the Parameters and Standard Errors Assume we have the following data calculated from a regression of y on a single variable x and a constant over 22 observations. Data:  xt yt = 830102 , T = 22, x = 416.5, y = 86.65, x 2 t = 3919654 , RSS = 130.6 830102 − (22 * 416.5 * 86.65) Calculations: $ = 2 = 0.35 3919654 − 22 *(416.5) $ = 86.65 − 035. = −5912. * 4165. We write yˆt = ˆ + ˆxt yˆ t = 59.12 + 0.35 xt ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 33  Example (cont’d) SE(regression), s =  uˆ t2 = 130.6 = 2.55 T −2 20 3919654 SE ( ) = 2.55 * = 3.35 ( (22  3919654 ) − 22  416.5 2 ) 1 SE (  ) = 2.55 * = 0.0079 ( 3919654 − 22  416.5 2 ) We now write the results as yˆ t = − 59.12 + 0.35xt (3.35) (0.0079) ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 34  An Introduction to Statistical Inference We want to make inferences about the likely population values from the regression parameters. Example: Suppose we have the following regression results: yˆ t = 20.3 + 0.5091xt (14.38) (0.2561) $ = 0.5091 is a single (point) estimate of the unknown population parameter, . How “reliable” is this estimate? The reliability of the point estimate is measured by the coefficient’s standard error. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 35  Hypothesis Testing: Some Concepts We can use the information in the sample to make inferences about the population. We will always have two hypotheses that go together, the null hypothesis (denoted H0) and the alternative hypothesis (denoted H1). The null hypothesis is the statement or the statistical hypothesis that is actually being tested. The alternative hypothesis represents the remaining outcomes of interest. For example, suppose given the regression results above, we are interested in the hypothesis that the true value of  is in fact 0.5. We would use the notation H0 :  = 0.5 H1 :   0.5 This would be known as a two sided test. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 36  One-Sided Hypothesis Tests Sometimes we may have some prior information that, for example, we would expect  > 0.5 rather than  < 0.5. In this case, we would do a one-sided test: H0 :  = 0.5 H1 :  > 0.5 or we could have had H0 :  = 0.5 H1 :  < 0.5 There are two ways to conduct a hypothesis test: via the test of significance approach or via the confidence interval approach. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 37  The Probability Distribution of the Least Squares Estimators We assume that ut  N(0,2) Since the least squares estimators are linear combinations of the random variables i.e. $ =  wt yt The weighted sum of normal random variables is also normally distributed, so $  N(, Var()) $  N(, Var()) What if the errors are not normally distributed? Will the parameter estimates still be normally distributed? Yes, if the other assumptions of the CLRM hold, and the sample size is sufficiently large. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 38  The Probability Distribution of the Least Squares Estimators (cont’d) Standard normal variates can be constructed from $ and $ : ˆ −  ˆ −  ~ N (0,1) and ~ N (0,1) var ( ) var ( ) But var() and var() are unknown, so ˆ −  ˆ −  ~ tT −2 and ~ tT −2 SE (ˆ ) ˆ SE (  ) ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 39  Testing Hypotheses: The Test of Significance Approach Assume the regression equation is given by , yt =  + xt + ut for t=1,2,...,T The steps involved in doing a test of significance are: 1. Estimate $ , $ and SE($ ) , SE( $ ) in the usual way 2. Calculate the test statistic. This is given by the formula $ −  * test statistic = SE ( $ ) where  * is the value of  under the null hypothesis. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 40  The Test of Significance Approach (cont’d) 3. We need some tabulated distribution with which to compare the estimated test statistics. Test statistics derived in this way can be shown to follow a t- distribution with T-2 degrees of freedom. As the number of degrees of freedom increases, we need to be less cautious in our approach since we can be more sure that our results are robust. 4. We need to choose a “significance level”, often denoted . This is also sometimes called the size of the test and it determines the region where we will reject or not reject the null hypothesis that we are testing. It is conventional to use a significance level of 5%. Intuitive explanation is that we would only expect a result as extreme as this or more extreme 5% of the time as a consequence of chance alone. Conventional to use a 5% size of test, but 10% and 1% are also commonly used. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 41  Determining the Rejection Region for a Test of Significance 5. Given a significance level, we can determine a rejection region and non- rejection region. For a 2-sided test: f(x) 2.5% 95% non-rejection 2.5% rejection region region rejection region ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 42  The Rejection Region for a 1-Sided Test (Upper Tail) f(x) 95% non-rejection region 5% rejection region ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 43  The Rejection Region for a 1-Sided Test (Lower Tail) f(x) 95% non-rejection region 5% rejection region ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 44  The Test of Significance Approach: Drawing Conclusions 6. Use the t-tables to obtain a critical value or values with which to compare the test statistic. 7. Finally perform the test. If the test statistic lies in the rejection region then reject the null hypothesis (H0), else do not reject H0. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 45  A Note on the t and the Normal Distribution You should all be familiar with the normal distribution and its characteristic “bell” shape. We can scale a normal variate to have zero mean and unit variance by subtracting its mean and dividing by its standard deviation. There is, however, a specific relationship between the t- and the standard normal distribution. Both are symmetrical and centred on zero. The t-distribution has another parameter, its degrees of freedom. We will always know this (for the time being from the number of observations -2). ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 46  What Does the t-Distribution Look Like? normal distribution t-distribution ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 47  Comparing the t and the Normal Distribution In the limit, a t-distribution with an infinite number of degrees of freedom is a standard normal, i.e. t () = N (01,) Examples from statistical tables: Significance level N(0,1) t(40) t(4) 50% 0 0 0 5% 1.64 1.68 2.13 2.5% 1.96 2.02 2.78 0.5% 2.57 2.70 4.60 The reason for using the t-distribution rather than the standard normal is that we had to estimate  2, the variance of the disturbances. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 48  The Confidence Interval Approach to Hypothesis Testing An example of its usage: We estimate a parameter, say to be 0.93, and a “95% confidence interval” to be (0.77,1.09). This means that we are 95% confident that the interval containing the true (but unknown) value of . Confidence intervals are almost invariably two-sided, although in theory a one-sided interval can be constructed. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 49  How to Carry out a Hypothesis Test Using Confidence Intervals 1. Calculate $ , $ and SE($ ) , SE( $ ) as before. 2. Choose a significance level, , (again the convention is 5%). This is equivalent to choosing a (1-)100% confidence interval, i.e. 5% significance level = 95% confidence interval 3. Use the t-tables to find the appropriate critical value, which will again have T-2 degrees of freedom. 4. The confidence interval is given by ( ˆ − t crit  SE( ˆ ), ˆ + t crit  SE( ˆ )) 5. Perform the test: If the hypothesised value of  (*) lies outside the confidence interval, then reject the null hypothesis that  = *, otherwise do not reject the null. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 50  Confidence Intervals Versus Tests of Significance Note that the Test of Significance and Confidence Interval approaches always give the same answer. Under the test of significance approach, we would not reject H0 that  = * if the test statistic lies within the non-rejection region, i.e. if $ −  * −tcrit £ $ £ +tcrit SE (  ) Rearranging, we would not reject if − t crit  SE ( ˆ ) £ ˆ −  * £ +t crit  SE ( ˆ ) ˆ − t crit  SE ( ˆ ) £  * £ ˆ + t crit  SE ( ˆ ) But this is just the rule under the confidence interval approach. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 51  Constructing Tests of Significance and Confidence Intervals: An Example Using the regression results above, yˆ t = 20.3 + 0.5091xt , T=22 (14.38) (0.2561) Using both the test of significance and confidence interval approaches, test the hypothesis that  =1 against a two-sided alternative. The first step is to obtain the critical value. We want tcrit = t20;5% ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 52  Determining the Rejection Region f(x) 2.5% rejection region 2.5% rejection region -2.086 +2.086 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 53  Performing the Test The hypotheses are: H0 :  = 1 H1 :   1 Test of significance Confidence interval approach approach test stat = $ −  * ˆ  t crit  SE ( ˆ ) SE ( $ ) 05091. −1 = 0.5091  2.086  0.2561 = = −1917. 0.2561 = (−0.0251,1.0433 ) Do not reject H0 since Since 1 lies within the test stat lies within confidence interval, non-rejection region do not reject H0 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 54  Testing other Hypotheses What if we wanted to test H0 :  = 0 or H0 :  = 2? Note that we can test these with the confidence interval approach. For interest (!), test H0 :  = 0 vs. H1 :   0 H0 :  = 2 vs. H1 :   2 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 55  Changing the Size of the Test But note that we looked at only a 5% size of test. In marginal cases (e.g. H0 :  = 1), we may get a completely different answer if we use a different size of test. This is where the test of significance approach is better than a confidence interval. For example, say we wanted to use a 10% size of test. Using the test of significance approach, $ −  * test stat = SE ( $ ) 05091. −1 = = −1917. 0.2561 as above. The only thing that changes is the critical t-value. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 56  Changing the Size of the Test: The New Rejection Regions f(x) 5% rejection region 5% rejection region -1.725 +1.725 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 57  Changing the Size of the Test: The Conclusion t20;10% = 1.725. So now, as the test statistic lies in the rejection region, we would reject H0. Caution should therefore be used when placing emphasis on or making decisions in marginal cases (i.e. in cases where we only just reject or not reject). ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 58  Some More Terminology If we reject the null hypothesis at the 5% level, we say that the result of the test is statistically significant. Note that a statistically significant result may be of no practical significance. E.g. if a shipment of cans of beans is expected to weigh 450g per tin, but the actual mean weight of some tins is 449g, the result may be highly statistically significant but presumably nobody would care about 1g of beans. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 59  The Errors That We Can Make Using Hypothesis Tests We usually reject H0 if the test statistic is statistically significant at a chosen significance level. There are two possible errors we could make: 1. Rejecting H0 when it was really true. This is called a type I error. 2. Not rejecting H0 when it was in fact false. This is called a type II error. Reality H0 is true H0 is false Significant Type I error  Result of (reject H0) = Test Insignificant Type II error ( do not  = reject H0) ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 60  The Trade-off Between Type I and Type II Errors The probability of a type I error is just , the significance level or size of test we chose. To see this, recall what we said significance at the 5% level meant: it is only 5% likely that a result as or more extreme as this could have occurred purely by chance. Note that there is no chance for a free lunch here! What happens if we reduce the size of the test (e.g. from a 5% test to a 1% test)? We reduce the chances of making a type I error... but we also reduce the probability that we will reject the null hypothesis at all, so we increase the probability of a type II error: less likely to falsely reject Reduce size → more strict → reject null of test criterion for hypothesis more likely to rejection less often incorrectly not reject So there is always a trade off between type I and type II errors when choosing a significance level. The only way we can reduce the chances of both is to increase the sample size. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 61  A Special Type of Hypothesis Test: The t-ratio Recall that the formula for a test of significance approach to hypothesis testing using a t-test was $i −  i* test statistic = SE( $i ) If the test is H 0 : i = 0 H 1 : i  0 i.e. a test that the population coefficient is zero against a two-sided alternative, this is known as a t-ratio test: $i Since  i* = 0, test stat = SE ( $i ) The ratio of the coefficient to its SE is known as the t-ratio or t-statistic. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 62  The t-ratio: An Example Suppose that we have the following parameter estimates, standard errors and t-ratios for an intercept and slope respectively. Coefficient 1.10 -4.40 SE 1.35 0.96 t-ratio 0.81 -4.63 Compare this with a tcrit with 15-3 = 12 d.f. (2½% in each tail for a 5% test) = 2.179 5% = 3.055 1% Do we reject H0: 1 = 0? (No) H0: 2 = 0? (Yes) ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 63  What Does the t-ratio tell us? If we reject H0, we say that the result is significant. If the coefficient is not “significant” (e.g. the intercept coefficient in the last regression above), then it means that the variable is not helping to explain variations in y. Variables that are not significant are usually removed from the regression model. In practice there are good statistical reasons for always having a constant y t even if it is not significant. Look at what happens if no intercept is included: xt ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 64  An Example of the Use of a Simple t-test to Test a Theory in Finance Testing for the presence and significance of abnormal returns (“Jensen’s alpha” - Jensen, 1968). The Data: Annual Returns on the portfolios of 115 mutual funds from 1945-1964. The model: R jt − R ft =  j +  j ( Rmt − R ft ) + u jt for j = 1, …, 115 We are interested in the significance of j. The null hypothesis is H0: j = 0. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 65  Frequency Distribution of t-ratios of Mutual Fund Alphas (gross of transactions costs) Source Jensen (1968). Reprinted with the permission of Blackwell publishers. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 66  Frequency Distribution of t-ratios of Mutual Fund Alphas (net of transactions costs) Source Jensen (1968). Reprinted with the permission of Blackwell publishers. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 67  Can UK Unit Trust Managers “Beat the Market”? We now perform a variant on Jensen’s test in the context of the UK market, considering monthly returns on 76 equity unit trusts. The data cover the period January 1979 – May 2000 (257 observations for each fund). Some summary statistics for the funds are: Mean Minimum Maximum Median Average monthly return, 1979-2000 1.0% 0.6% 1.4% 1.0% Standard deviation of returns over time 5.1% 4.3% 6.9% 5.0% Jensen Regression Results for UK Unit Trust Returns, January 1979-May 2000 R jt − R ft =  j +  j ( Rmt − R ft ) +  jt ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 68  Can UK Unit Trust Managers “Beat the Market”? : Results Estimates of Mean Minimum Maximum Median  -0.02% -0.54% 0.33% -0.03%  0.91 0.56 1.09 0.91 t-ratio on  -0.07 -2.44 3.11 -0.25 In fact, gross of transactions costs, 9 funds of the sample of 76 were able to significantly out-perform the market by providing a significant positive alpha, while 7 funds yielded significant negative alphas. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 69  The Overreaction Hypothesis and the UK Stock Market Motivation Two studies by DeBondt and Thaler (1985, 1987) showed that stocks which experience a poor performance over a 3 to 5 year period tend to outperform stocks which had previously performed relatively well. How Can This be Explained? 2 suggestions 1. A manifestation of the size effect DeBondt & Thaler did not believe this a sufficient explanation, but Zarowin (1990) found that allowing for firm size did reduce the subsequent return on the losers. 2. Reversals reflect changes in equilibrium required returns Ball & Kothari (1989) find the CAPM beta of losers to be considerably higher than that of winners. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 70  The Overreaction Hypothesis and the UK Stock Market (cont’d) Another interesting anomaly: the January effect. – Another possible reason for the superior subsequent performance of losers. – Zarowin (1990) finds that 80% of the extra return available from holding the losers accrues to investors in January. Example study: Clare and Thomas (1995) Data: Monthly UK stock returns from January 1955 to 1990 on all firms traded on the London Stock exchange. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 71  Methodology Calculate the monthly excess return of the stock over the market over a 12, 24 or 36 month period for each stock i: Uit = Rit - Rmt n = 12, 24 or 36 months Calculate the average monthly return for the stock i over the first 12, 24, or 36 month period: 1 n Ri =  U it n t =1 ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 72  Portfolio Formation Then rank the stocks from highest average return to lowest and from 5 portfolios: Portfolio 1: Best performing 20% of firms Portfolio 2: Next 20% Portfolio 3: Next 20% Portfolio 4: Next 20% Portfolio 5: Worst performing 20% of firms. Use the same sample length n to monitor the performance of each portfolio. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 73  Portfolio Formation and Portfolio Tracking Periods How many samples of length n have we got? n = 1, 2, or 3 years. If n = 1year: Estimate for year 1 Monitor portfolios for year 2 Estimate for year 3... Monitor portfolios for year 36 So if n = 1, we have 18 INDEPENDENT (non-overlapping) observation / tracking periods. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 74  Constructing Winner and Loser Returns Similarly, n = 2 gives 9 independent periods and n = 3 gives 6 independent periods. Calculate monthly portfolio returns assuming an equal weighting of stocks in each portfolio. Denote the mean return for each month over the 18, 9 or 6 periods for the winner and loser portfolios respectively as RpW and RpL respectively. L Define the difference between these as RDt = Rp - RpW. Then perform the regression RDt = 1 + t (Test 1) Look at the significance of 1. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 75  Allowing for Differences in the Riskiness of the Winner and Loser Portfolios Problem: Significant and positive 1 could be due to higher return being required on loser stocks due to loser stocks being more risky. Solution: Allow for risk differences by regressing against the market risk premium: RDt = 2 + (Rmt-Rft) + t (Test 2) where Rmt is the return on the FTA All-share Rft is the return on a UK government 3 month t-bill. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 76  Is there an Overreaction Effect in the UK Stock Market? Results Panel A: All Months n = 12 n = 24 n =36 Return on Loser 0.0033 0.0011 0.0129 Return on Winner 0.0036 -0.0003 0.0115 Implied annualised return difference -0.37% 1.68% 1.56% Coefficient for (3.47): ̂1 -0.00031 0.0014** 0.0013 (0.29) (2.01) (1.55) Coefficients for (3.48): ̂ 2 -0.00034 0.00147** 0.0013* (-0.30) (2.01) (1.41) ̂ -0.022 0.010 -0.0025 (-0.25) (0.21) (-0.06) Panel B: All Months Except January Coefficient for (3.47): ̂1 -0.0007 0.0012* 0.0009 (-0.72) (1.63) (1.05) Notes: t-ratios in parentheses; * and ** denote significance at the 10% and 5% levels respectively. Source: Clare and Thomas (1995). Reprinted with the permission of Blackwell Publishers. ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 77  Testing for Seasonal Effects in Overreactions Is there evidence that losers out-perform winners more at one time of the year than another? To test this, calculate the difference between the winner & loser portfolios as previously, RDt , and regress this on 12 month-of-the-year dummies: 12 RDt =  i Mi + t i =1 Significant out-performance of losers over winners in, – June (for the 24-month horizon), and – January, April and October (for the 36-month horizon) – winners appear to stay significantly as winners in March (for the 12-month horizon). ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 78  Conclusions Evidence of overreactions in stock returns. Losers tend to be small so we can attribute most of the overreaction in the UK to the size effect. Comments Small samples No diagnostic checks of model adequacy ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 79  The Exact Significance Level or p-value This is equivalent to choosing an infinite number of critical t-values from tables. It gives us the marginal significance level where we would be indifferent between rejecting and not rejecting the null hypothesis. If the test statistic is large in absolute value, the p-value will be small, and vice versa. The p-value gives the plausibility of the null hypothesis. e.g. a test statistic is distributed as a t62 = 1.47. The p-value = 0.12. Do we reject at the 5% level?...........................No Do we reject at the 10% level?.........................No Do we reject at the 20% level?.........................Yes ‘Introductory Econometrics for Finance’ © Chris Brooks 2013 80