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Unformatted text preview: Dougherty: Introduction to Econometrics 3e Study Guide Chapter 11 Models using time series data Overview This chapter introduces the application of regression analysis to time series data, beginning with static models and then proceeding to dynamic models with lagged variables used as explanatory variables. It is shown that multicollinearity is likely to be a problem in models with unrestricted lag structures and that this provides an incentive to use a parsimonious lag structure, such as the Koyck distribution. Two models using the Koyck distribution, the adaptive expectations model and the partial adjustment model, are described, together with well- known applications to aggregate consumption theory, Friedman's permanent income hypothesis in the case of the former and Brown's habit persistence consumption function in the case of the latter. The chapter concludes with a discussion of prediction and stability tests in time series models. Learning outcomes After working through the corresponding chapter in the text, studying the corresponding slideshows, and doing the starred exercises in the text and the additional exercises in this guide, you should be able to: explain why multicollinearity is a common problem in time series models, especially dynamic ones with lagged explanatory variables • • • • • • • • • explain the restrictions implicit in a model whose lag structure has a Koyck distribution fit a model with a Koyck lag structure describe the assumptions made by the adaptive expectations and partial adjustment models demonstrate that any model with a lagged dependent variable as an explanatory variable could in principle be interpreted as either describe the assumptions made by the Friedman permanent income hypothesis consumption function when it is fitted using time series data demonstrate that the expected value of forecast error is zero when predictions are made after a model has been fitted using OLS, provided that the regression model assumptions are valid construct confidence intervals for predictions perform tests of predictive failure and coefficient stability. Additional exercises A11.1 The following production functions were fitted using the data of Cobb and Douglas (1928), Y t , K t , and L t , being index number series for real output, real capital input, and real labor input, respectively, for the manufacturing sector of the United States for the period 1899–1922, with 1899=100 (standard errors in parentheses): = –4.86 + 0.16 K t Y ˆ t + 0.92 L t R 2 = 0.94 (14.54) (0.04) (0.15) = –0.18 + 0.23 log K t Y ^ log t + 0.81 log L t R 2 = 0.96 (0.43) (0.06) (0.15) © Christopher Dougherty, 2007 The material in this book has been adapted and developed from material originally produced for the degrees and diplomas by distance learning offered by the University of London External System ( www.londonexternal.ac.uk ) Dougherty: Introduction to Econometrics 3e Study Guide (The data are reproduced in Exercise 12.6). Provide an interpretation of the coefficients of both...
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This note was uploaded on 05/26/2010 for the course ECON 301 taught by Professor Öcal during the Spring '10 term at Middle East Technical University.
- Spring '10