Lecture 9: Panel Data Model (Chapter 14, Wooldridge Textbook)
2 Panel Data Panel data is obtained by observing the same person, firm, county, etc over several periods. Unlike the pooled cross sections, the observations for …
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Basic Statistical Notation - Miami University
www.fsb.miamioh.eduSome Useful Intuitions Let c denotes a constant number, and x and y denote two random variables 1. The expectation or mean value (E or ) means \average".It measures the central tendency of the distribution of a random variable. 2. By …
Chapter 7, Dummy Variable - Miami University
www.fsb.miamioh.edu3. Sample mean is the estimate for population mean, so we have the following interpre-tation for the estimated coefficients in (2) ˆ 0 = ¯yD=0 (8) ˆ 1 = ¯yD=1 ¯yD=0 (9) where ¯yD=0 denotes the average Y in the sub-sample for which D = 0; y¯D=1 denotes the average Y in the sub-sample for which D = 1: Equation (2) provides a simple way to carry out a comparison of means test (or …
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Matrix Algebra for OLS Estimator - Miami University
www.fsb.miamioh.eduIndependent Variables • Suppose there are k independent variables and a constant term. In the spreadsheet there are k +1 columns and n rows. • Mathematically that spreadsheet corresponds to an n × (k +1) matrix, denoted by X : X = 1 x11::: x1k 1 x21::: x2k 1 xn1::: xnk where xij is the i-th observation of the j-th independent variable. 4
Lecture: IV and 2SLS Estimators (Wooldridge’s book chapter 15)
www.fsb.miamioh.eduIt is straightforward to account for heteroskedasticity. The robust variance-covariance matrix for bˆ 2SLS allowing for heteroskedasticity is robust var-cov(bˆ 2SLS)= X 0PX 1 X PWPX X PX 1 where W = E(UU0): To estimate the meat in the middle of that sandwich, using X0PWbPX = Xb0WbXb= n å i=1 uˆ2 i bx ibx 0 i where ˆu denotes the 2SLS ...
Monique L. Ueltschy Murfield - Miami University
www.fsb.miamioh.eduMonique L. Ueltschy Murfield Current Vita Updated February 20, 2018 Page 6 of 9 6 Murfield, Monique L. Ueltschy and Norton Jr., William I., Emergent Leadership in Logistics Teams: The Development of a Measurement Model.
Lecture 5a: ARCH Models - Miami University
www.fsb.miamioh.eduConsider the first order autoregressive conditional heteroskedasticity (ARCH) process rt = σtet (5) et ∼ white noise(0, 1) (6) σt = √ ω + α1r2 t 1 (7) where rt is the return, and is assumed here to be an ARCH(1) process. et is a white noise with zero mean and variance of one. et may or may not follow normal distribution. 7
Lecture 8a: Spurious Regression
www.fsb.miamioh.eduThe traditional statistical theory holds when we run regression using (weakly or covariance) stationary variables. For example, when we regress one stationary series onto another stationary series, the coefficient will be close to zero and insignificant if the two series are independent.
Lecture 7a: Vector Autoregression (VAR)
www.fsb.miamioh.eduThe goal of structural VAR analysis is to obtain B, which is not unique (for a bivariate system Ω has 3 unique elements, while B has 4 elements to be determined). 3. The Sims (1980) structural VAR imposes the restriction that B ... Instead, we simulate the impulse response for VAR(p) 28.
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