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# ECONOMIC FORECASTING ECMT 475

Texas A&M

GPA 3.63

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This 5 page Study Guide was uploaded by Mr. Aryanna Gusikowski on Wednesday October 21, 2015. The Study Guide belongs to ECMT 475 at Texas A&M University taught by Staff in Fall. Since its upload, it has received 62 views. For similar materials see /class/226094/ecmt-475-texas-a-m-university in Econometrics at Texas A&M University.

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Date Created: 10/21/15

Econometrics 475 Fall 2008 Study Guide for Exam 3 The third inclass exam will be administered on Tuesday November 25 The exam with cover Chapters 7 7 10 inclusive out of the text The format of the quiz will be short answer essay and application interpretation problems You should bring a calculator Consider any of the Concepts for Review terms listed below as fair game for de nitions Know how the autocorrelation function is derived from the auto covariance function and the distinction between the autocorrelation function and the partial autocorrelation function Given an equation in normal form be able to rewrite it in lag operator form and vice versa For an MAl process be able to demonstrate why the autocorrelation function is non zero for the first lag and zero thereafter be able to invert the MAl process into its AR representation to demonstrate under what circumstances the partial autocorrelation function should exhibit oscillating dampening Given a sample correlograrn be able to identify whether the underlying series is an ARl or MAl process Given GRETL output for specific cyclical models eg ARp MAq or ARMApq be able to construct point forecasts based on the output Given GRETL output for a specific ARIMA model including trend ARMA and possibly seasonal components be able to construct point forecasts based on the output Possible Terms for Definitions Covariance Stationarity Autocovariance Function Autocorrelation Function Partial Autocorrelation Function White Noise Unconditional Mean and Variance Conditional Mean and Variance Lag Operator ARp Process MAq Process ARMApq Process Random Walk Example Problems 1 Consider the output from the following MA2 model of the variable R71 ARMA estimates using the 180 observations 1992012006 12 Dependent variable R71 I Variable I Coef cient I Std Error I t statz39stic I p value I Ithetal 0850 00740421 114825 lt000001 I ieta 2 0451 00491974 91566 lt000001 Mean of dependent variable 177048e011 Standard deviation of dep var 552387 Mean of innovations 238732 Variance of innovations 111754e007 Akaike information criterion 343895 Schwarz Bayesian criterion 344853 obs R71 et 2006M07 144672 93303 2006M08 130509 32867 2006M09 95126 25152 2006M10 75602 39413 2006M11 85209 40371 2006M12 99036 46959 Construct point forecasts for each of the next three periods January February and March 2007 2 Consider the following ARMA2 1 model of the variable R71 Model 4 ARMA estimates using the 180 observations 199201200612 Dependent variable R71 0136594 73808 lt000001 Mean of dependent variable 177048e011 Standard deviation of dep var 552387 Mean of innovations 770081 Variance of innovations 569496e006 Akaike information criterion 332073 Schwarz Bayesian criterion 33335 obs R71 et 2006M07 144672 323498 2006M08 130509 394123 2006M09 95126 29737 2006M10 75602 30448 2006M11 85209 26774 2006M12 99036 143327 Construct point forecasts for each of the next three periods January February and March 2007 3 Consider the following ARIMA32 model of the log Retail sales including trend and seasonal effects ARMAX estimates using the 182 observations 199201200702 Dependent variable liRsales 00044 0000157936 280278 lt000001 1204 00187441 6421720 lt000001 00187663 6408083 lt000001 Mean of dependent variable 124318 Standard deviation of dep var 0242725 Mean of innovations 0000488234 Variance of innovations 000029305 Akaike information criterion 92466 Schwarz Bayesian criterion 863784 time obs liRsales et 177 2006M09 1277 001 178 2006M10 1277 004 179 2006M11 1280 002 180 2006M12 1295 002 181 2007M01 1272 003 182 2007M02 1269 001 Construct point forecasts for each of the next three periods March April and May 2007 Econometrics 475 Fall 2008 Study Guide for Exam 1 The first inclass exam will be administered on Tuesday September 23rd and will cover material from lectures and in Chapters 15 of the text The format of the exam will be shortanswer essay and applicationinterpretation problems A calculator is recommended Any of the terms listed at the end of the chapters are fair game for shortanswer de nitions Understand why graphical analysis is an important aspect of forecasting Given several data series you should be ready to show how to construct a simple moving average and an exponentially smoothed series and how these are used to forecast Understand the differences between linear quadratic and higher order polynomials and exponential trends in particular their graphical appearances and how you interpret the coefficients from the various regression equations Understand the relationship among MSE R2 Adjusted R2 and the AIC and SIC criterion and the relative merits of using one or more of these as a model selection tool Given output for a specific trend model you should be able to construct a point and interval forecast for that model Note You will not be required to calculate forecast standard errors However given the forecast standard errors you will be asked to construct interval forecasts Econometrics 475 Fall 2008 Study Guide for Exam 2 The second inclass exam will be administered on Thursday October 16 The exam will cover Chapter 6 and the first four sections of Chapter 11 The format of the exam will be shortanswer essay and application interpretation problems A calculator is recommended Any of the terms listed at the end of Chapter 6 or in the first four sections of Chapter 11 are fair game for shortanswer definitions Given output for a specific trend model with seasonal or other calendar effects you should be able to construct point forecasts for that model Be prepared to show how to test for whether seasonal or other calendar effects are significant Given output from a specific regression model with or without trend andor seasonal effects be able to construct point forecasts for that model when one 01 more of the right hand side variables may need to be forecast as well This could also involve a model with a lagged dependent variable or a distributed lag on one of the RHS variables An example is given below Consider the following model of Housing Starts based on monthly data The dependent variable is the log of of housing starts Fedfunds is the interest rate on fedfund deposits in percent and liPCExp is the log of personal expenditures on durable goods Model 1 OLS estimates using the 577 observations 195901200701 Dependent variable 17HOUST Variable C oe cient Std Error t statz39stic p value const 7235 0048464 1492854 lt000001 Fedfunds 0015 000256742 59287 lt000001 liPCExp 0033 000822155 40207 000007 Personal consumption expenditures are assumed to grow according the following growth model Model 2 OLS estimates using the 577 observations 195901200701 Dependent variable liPCExp Variable C oe cient Std Error t statz39stic p value const 3715 00101321 3666627 lt000001 time 0006 303752e05 2025027 lt000001 Assuming the Federal Reserve holds interest rates steady at 525 provide a point forecast for Housing Starts for June 2007

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