Assignment 1: Demand Estimation Due Week 3 and worth 200 points Imagine that you work for the maker of a leading brand of low-calorie‚ frozen microwavable food that estimates the following demand equation for its product using data from 26 supermarkets around the country for the month of April. For a refresher on independent and dependent variables‚ please go to Sophia’s Website and review the Independent and Dependent Variables tutorial‚ located at http://www.sophia.org/tutorials/independent-and-dependent-variables--3
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Title: The Role of a Concentration Ellipsoid as a Geometric Tool in Interpreting the Efficiency of Econometric Estimators Author: Preetha Rajan Supervisor: Dr. Alan J. Rogers Date of submission: 21st November‚ 2011 A dissertation submitted in part fulfilment of the requirements for the degree of Bachelor of Commerce (Honours) in Economics‚ The University of Auckland‚ 2011. Abstract This dissertation‚ by making use of important geometric and econometric concepts such as a ‘linear manifold’
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relatively new methodology to cope with dynamic panel data models‚ where every contract in the market is organized as a panel. The GMM-type estimator for dynamic panel-data of Arellano [1] is therefore implemented. The results show appropriate specification residual tests and demonstrate firstly the importance of volatility serial correlation. This estimator also shows that the change in daily trading volume is the major determinant of intra-day volatility. In contrast to the segmented analyses‚
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(Boston College) IVs and Panel Data Feb 2009 1 / 43 Instrumental variables estimators Regression with Instrumental Variables What are instrumental variables (IV) methods? Most widely known as a solution to endogenous regressors: explanatory variables correlated with the regression error term‚ IV methods provide a way to nonetheless obtain consistent parameter estimates. Although IV estimators address issues of endogeneity‚ the violation of the zero conditional mean assumption caused
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some data sets are constructed by intentionally oversampling different parts of the population. 2. Ordinary least squares and instrumental variable estimation * In what case the omitted variable can result in the asymptotic bias of an estimator? When the effect of an omitted variable is negligible? Consider following model‚ which assumes additive effect of
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ECON 140 Section 13‚ November 28‚ 2013 ECON 140 - Section 13 1 The IV Estimator with a Single Regressor and a Single Instrument 1.1 The IV Model and Assumptions Consider the univariate linear regression framework: Yi = β0 + β1 Xi + ui Until now‚ it was assumed that E (ui |Xi ) = 0‚ i.e. conditional mean independence. Let’s relax this assumption and allow the covariance between Xi and ui to be dierent from zero. Our problem here is that ui is not observed. Doing OLS
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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 Econometrics Introduction Course Introduction About Me Nalan Basturk Assistant
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reasoning.) 1. Assume we have a simple linear regression model: . Given a random sample from the population‚ which of the following statement is true? a. OLS estimators are biased when BMI do not vary much in the sample. b. OLS estimators are biased when the sample size is small (say 20 observations). c. OLS estimators are biased when the error u captures perseverance and self‐ control‚ and you believe that people who are perseverant and have more self‐ control are less likely overweight
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Comparison And Analysis Of Channel Estimation Algorithms In OFDM Systems Vineetha Mathai Dept. of Electronics Engineering‚ Madras Institute of Technology‚ Chennai-600044‚ India. e-mail: vineethamathai@yahoo.in Abstract—The channel estimation can be performed for analyzing effect of channel on signal by either inserting pilot tones into all of the subcarriers of OFDM symbols with a specific period or inserting pilot tones into each OFDM symbol. The block type pilot channel estimation has been
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Ziemer‚ R.F. and M.E. Wetzstein‚ 1983‚ A Stein-rule method for pooling data‚ Economics Letters 11‚ 137–143. Ziliak‚ J.P.‚ 1997‚ Efficient estimation with panel data when instruments are predetermined: An empirical comparison of moment-condition estimators‚ Journal of Business and Economic Statistics 15‚ 419–431. Ziliak‚ J.P. and T.J. Kniesner‚ 1998‚ The importance of sample attrition in life cycle labor supply estimation‚ Journal of Human Resources 33‚ 507–530.
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