Econometrics Nalan Basturk Erasmus University Rotterdam Econometric Institute basturk@ese.eur.nl http://people.few.eur.nl/basturk/ Introduction Course Introduction Course Organization Motivation Introduction Today Regression Linear Regression Ordinary Least Squares Linear regression model Gauss-Markov conditions and the properties of OLS estimators Example: individual wages Goodness-of-fit 1 / 42 2 / 42 Lecture 1‚ 3 September 2013 Applied Econometrics Introduction Course Introduction Applied
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prices‚ the annual rate of the Treasury bill‚ and S&P 500 index from Jan. 1991 to Sep. 1999. The OLS estimates of the above regression are reported in the following Table 2.1. Dependent Variable: DLOG(GOLD)-TBILL/12 Sample (adjusted): 1990M05 1999M09 Included observations: 113 after adjustments Variable C DLOG(SP500)-TBILL/12 R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Coefficient -0.006314 -0.087523 0.018504 0.009662 0.024662 0
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Taipei European SchoolMath Portfolio | VINCENT CHEN | Gold Medal Heights Aim: To consider the winning height for the men’s high jump in the Olympic games Years | 1932 | 1936 | 1948 | 1952 | 1956 | 1960 | 1964 | 1968 | 1972 | 1976 | 1980 | Height (cm) | 197 | 203 | 198 | 204 | 212 | 216 | 218 | 224 | 223 | 225 | 236 | Height (cm) Height (cm) As shown from the table above‚ showing the height achieved by the gold medalists at various Olympic games‚ the Olympic games were not held in
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Chapter 8: Cost Estimation Strategic Role of Cost Estimation * Cost Estimationthe development of a well-defined relationship b/t a cost object and its cost drivers for the purpose of predicting the cost * Facilitates strategic mgmt is 2 ways * Helps predict future costs * Helps identify key cost drivers for a cost object and which driver is most useful * Using Cost Estimation to Predict future costs * Strategic mgmt requires accurate estimates for the
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Introduction to Structural Equation Modeling (Path Analysis) SGIM Precourse PA08 May 2005 Jeffrey L. Jackson‚ MD MPH Kent Dezee‚ MD MPH Kevin Douglas‚ MD William Shimeall‚ MD MPH Traditional multivariate modeling (linear regression‚ ANOVA‚ Poisson regression‚ logistic regression‚ proportional hazard modeling) is useful for examining direct relationships between independent and dependent variables. All share a common format: Dependent Variable = Independent variable1 + Independent Variable2 + Independent
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TEST REVIEW – We will work these problems in class. Data Files for these problems will be disclosed in class during the review. On TEST 3‚ you will be asked to perform hypothesis tests‚ find confidence intervals‚ and conduct regression analyses. Be prepared to also interpret information on any Excel print-outs. Test 3 will be a multiple-choice test. 1. A federal agency responsible for enforcing laws governing weights and measures n=1 routinely inspects packages to determine whether the
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Select one: Market research Delphi method Gamma method Executive opinion Naïve method Which of the following is a causal forecasting method? Select one: Moving average Trend adjusted exponential smoothing Weighted moving average Linear regression
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analysis for a multiple linear regression model are similar to those for a simple linear regression model. However‚ they are much more important for the multiple linear regression models because of the lack of good graphical representations of the data set and the fitted model. In simple linear regression a plot of the response variable against the input variable showing the data points and the fitted regression line provides a good graphical summary of the regression analysis. With the higher
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cultural diversity of a country‚ and gender equality scale affects the percentage of women in parliament. I would expect when controlling for political knowledge‚ the more equal in opportunity a country is‚ the more women there are in parliament. My regression analysis propounds my hypotheses. While there are some shortcomings to my research‚ for example‚ I would have liked to the use age as an independent variable but it was not available in the dataset. Furthermore‚ my dependent variable only measures
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13 4 Regression model and diagnostics ......................................................... 14 4.1 Simple linear regression ....................................................................................... 14 4.2 Why the gas pipeline dummy is underestimated.................................................. 15 4.3 Robustness testing ................................................................................................ 19 Appendix A Alternative regression models ...
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