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Introduction to Applied Econometrics

by: Rhett Bergnaum

Introduction to Applied Econometrics ECON 4233

Marketplace > University of Oklahoma > Economcs > ECON 4233 > Introduction to Applied Econometrics
Rhett Bergnaum
GPA 3.53


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This 12 page Class Notes was uploaded by Rhett Bergnaum on Monday October 26, 2015. The Class Notes belongs to ECON 4233 at University of Oklahoma taught by Staff in Fall. Since its upload, it has received 24 views. For similar materials see /class/229252/econ-4233-university-of-oklahoma in Economcs at University of Oklahoma.


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Date Created: 10/26/15
ECON 423301 Lecture 21 November 25 2003 Coverage Chapter 6 pages 133139 Assignment Chapter 6 pages 133139 Class questions 1 Provide de nitions for the sample counterparts of the following terms a Mean b Variance c Autocorrelations 2 Theoretically the autocorrelations of a White Noise process are zero For data we wish to make statements about whether the autocorrelations can statistically be distinguished from zero a Under the assumption that a series is White Noise what is the distribution of each sample autocorrelation Use this distribution to calculate the 95 con dence interval under the assumption that the series is WN b From above notice that we can tell whether or not a given autocorrelation is equal to zero by checking if the autocorrelation is in the 95 con dence interval about zero If it is not we reject the hypothesis that the autocorrelation is zero which allows us to infer that the series under investigation is NOT White Noise The problem is that this is only a statement about a single autocorrelation Suppose a series has one autocorrelation that cannot be distinguished from zero If the series is White Noise ALL of the autocorrelations are zero i Use the relationship between normal random variables and a chisquare random variable to calculate the BoxPierce test statistic What is the distribution of this test statistic ii De ne the LjungBox Q statistic 3 This question relates to the example on pages 137139 The rst 3 sample autocorrelations associated with the Canadian employment indeX are given by 9 SAC 1 0949 SAC 2 0877 SAC 3 0795 Notice that there are a total of 128 observations Use the LjungBox Qstatistic associated with the hypothesis that the rst 3 autocorrelations are zero Very carefully write the null and alternative hypothesis calculate the test statistic and write the 95 critical value the size of the test is 5 Indicate whether or not you reject the null hypothesis b Refer to your answer in part 3a Would it appear that the Canadian employment is described as White Noise De ne the sample partial autocorrelations Refer to the sample autocorrelations and sample partial autocorrelations on page 139 associated with the Canadian employment indeX Provide a guess on the appropriate model for this series Lecture 4 ECON 423301 September 9 2003 Coverage Chapter 1 the Appendix Reading assignment Continue reading chapters 1 2 and 3 We will start on chapter 4 on Thursday Class questions 1 Last time we went over the multivariate regression model and suggested that we will use ordinary least squares to estimate the model What are the classical assumptions associated with the linear regression model 2 Please refer to the first part of the handout that contains EViews output The handout contains output from a regression of the Danish real interest rate on a constant one lag of the Danish real interest rate and the real interest rate in Germany An exercise that is of immense importance to investors is the value of the real interest rate in the future Thus we may wish to develop a model to forecast the Danish real interest rate The regression model depicted may be one alternative We wish to test the hypothesis that the German real interest rate is an important component of the model a Carefully state the null and alternative hypothesis for the test involving significance of the German real interest rate coefficient b Construct a tstatistic for the above hypothesis What is the 95 critical value if we assume normality use the provided tables c Given the above do we reject or fail to reject the null hypothesis 3 De ne the following terms in relation to regression output a Sum of squared errors b Standard Error of Regression c Total sum of squares d R2 4 There are two ways to test the joint signi cance of the coef cients in a regression One method involves using the sum of squared errors for a general model and then comparing this value to the sum of squared errors for a model where only the constant is used as a regressor a What is the test statistic for the signi cance of the regression based on the sum of squared errors What is the null hypothesis for this test and what is the distribution of the test statistic b When would you reject the hypothesis stated in part a Provide some intuition for your result Use the attached output to construct a test of the null hypothesis that z 3 c based on the sum of squared errors from each regression d An alternative way to test the above hypothesis is through the use of the R2 statistic Generally show the calculated test statistic for the above hypothesis using the R2 statistic Finally indicate what the test statistic for this example is and indicate whether or not you reject the null hypothesis ECON 423301 Lecture 7 Please note that we used the class period on September 18 2003 to review the problem sets September 23 2003 Coverage Chapter 4 Assignment Chapter 4 89102 Chapter 5 Class questions 1 Suppose that the value of Y at time t is given by the following model Y1 050 20Timet at where stN0l a What is the optimal forecast for Y m based on information we know at time T Suppose that T200 What is the actual value of your twostep ahead forecast b Suppose the estimated residual variance is equal to one Calculate the 95 con dence interval associated for your calculated forecast c Provide the general hstep ahead forecast based on part a Demonstrate that this forecast is the optimal hstep ahead forecast 2 De ne the following terms a Data generating process b Consistency as it relates to model selection c Asymptotic efficiency as it relates to model selection 3 The following questions relate to the various selection criteria a De ne the condition necessary for the SIC to be larger than the AIC b Which criterion is consistent How about asymptotically ef cient c Suppose we are deciding between several competing models All else equal what condition will we employ in choosing the appropriate model ECON 423 301 Lecture 9 September 30 2003 Coverage Chapter 5 Assignment Chapter 6 Class questions 1 Suppose the data generating process for Yt is given by Yt 30D1t SIDZt 8t where stN0l This equation is based on the speci cation given in the previous lecture question number 3 Suppose that we are currently in the second half of the year 2003 and wish to forecast the value of Y one and two periods into the future the rst and second half of 2004 Further suppose that the estimated residual variance is equal to 62 Calculate the one and two period forecasts and associated confidence intervals Lecture 10 ECON 423301 October 2 2003 In class we will consider modeling housing starts in the United States using the data provided by the author of our textbook Francis Diebold General instructions on model building using EViews In general the first step is to generate a time series plot of the data This will allow you to determine the characteristics of the series you are trying to model 1 Depicting the graph Among the toolbars at the top of the screen nd an option labeled Quick Select this option A new set of option appears Select Graph and Line graph Comments The predominant feature of the data is an observed cycle It is somewhat difficult to see with a plot of the entire graph Thus let s focus on the last five years of data Change the sample to 199001 to 199412 Now plot the graph The plot tells us that housing starts seem to decline in December and January and then pick up around spring Thus seasonal affects are likely Another important feature of the graph is the fact that there is no obvious trending behavior in the data In fact over the almost 50 years of data we see that the first observation is close to the last observation The mean also stays consistent around 100 We will verify the lack of a trend with estimation but preliminary examination of the data reveals the lack of a trend 2 Selecting the period to estimate our model with and the period to forecast Comments Again we wish to use part of the data to fit a model We will not use the last eleven months preserving this time period to compare forecasting results Thus we change the sample to 194601 to 199312 S We now consider models to forecast with The obvious seasonality in the data tells us that we might use seasonal dummies There are twelve seasons in the data corresponding to the twelve months of the year We again select Quick Of the options that appear we select Estimate Equation We now are presented with an equation dialogue box We can type in our equation in one of two ways Method 1 Type the following exactly as it appears starts seasl seas2 seas3 seas4 seasS seas6 seas7 seas8 seas9 seas10 seasll seaslZ


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