Presentation on theme: "Regression Analysis. Introduction Derive the α and β Assess the use of the T-statistic Discuss the importance of the Gauss- Markov assumptions Describe."— Presentation transcript:
Introduction Derive the α and β Assess the use of the T-statistic Discuss the importance of the Gauss- Markov assumptions Describe the problems associated with autocorrelation, how to measure it and possible remedies Introduce the problem of heteroskedasicity
Values and Fitted Values
Deriving the α and β The aim of a least squares regression is to minimize the distance between the regression line and error terms (e).
The Slope Coefficient (β)
T-test When conducting a t-test, we can use either a 1 or 2 tailed test, depending on the hypothesis We usually use a 2 tailed test, in this case our alternative hypothesis is that our variable does not equal 0. In a one tailed test we would stipulate whether it was greater than or less than 0. Thus the critical value for a 2 tailed test at the 5% level of significance is the same as the critical value for a 1 tailed test at the 2.5% level of significance.
T-test We can also test whether our coefficient equals 1.
Gauss-Markov Assumptions There are 4 assumptions relating to the error term. The first is that the expected value of the error term is zero The second is that the error terms are not correlated The third is that the error term has a constant variance The fourth is that the error term and explanatory variable are not correlated.
Gauss-Markov assumptions More formally we can write them as:
Additional Assumptions There are a number of additional assumptions such as normality of the error term and n (number of observations) exceeding k (the number of parameters). If these assumptions hold, we say the estimator is BLUE
BLUE Best or minimum variance Linear or straight line Unbiased or the estimator is accurate on average over a large number of samples. Estimator
Consequences of BLUE If the estimator is not BLUE, there are serious implications for the regression, in particular we can not rely on the t-tests. In this case we need to find a remedy for the problem.
Autocorrelation Autocorrelation occurs when the second Gauss-Markov assumption fails. It is often caused by an omitted variable In the presence of autocorrelation the estimator is not longer Best, although it is still unbiased. Therefore the estimator is not BLUE.
Durbin-Watson Test This tests for 1 st order autocorrelation only In this case the autocorrelation follows the first-order autoregressive process
Durbin-Watson Test- decision framework 0dldu244-dub-dl Zone of indecision
DW Statistic The DW test statistic lies between 0 and 4, if it lies below the dl point, we have positive autocorrelation. If it lies between du and 4-du, we have no autocorrelation and if above 4-dl we have negative autocorrelation. The dl and du value can be found in the DW d- statistic tables (at the back of most text books)
Lagrange Multiplier (LM) Statistic Tests for higher order autocorrelation The test involves estimating the model and obtaining the error term. Then run a second regression of the error term on lags of itself and the explanatory variable: (the number of lags depends on the order of the autocorrelation, i.e. second order)
LM Test The test statistic is the number of observations multiplied by the R-squared statistic. It follows a chi-squared distribution, the degrees of freedom are equal to the order of autocorrelation tested for (2 in this case) The null hypothesis is no autocorrelation, if the test statistic exceeds the critical value, reject the null and therefore we have autocorrelation.
Remedies for Autocorrelation There are 2 main remedies: The Cochrane-Orcutt iterative process An unrestricted version of the above process
Heteroskedasticity This occurs when the variance of the error term is not constant Again the estimator is not BLUE, although it is still unbisased it is no longer Best It often occurs when the values of the variables vary substantially in different observations, i.e. GDP in Cuba and the USA.
Conclusion The residual or error term is the difference between the fitted value and actual value of the dependent variable. There are 4 Gauss-Markov assumptions, which must be satisfied if the estimator is to be BLUE Autocorrelation is a serious problem and needs to be remedied The DW statistic can be used to test for the presence of 1 st order autocorrelation, the LM statistic for higher order autocorrelation.