'Ols' presentation slideshows

4. Using panel data

4. Using panel data. 4.1 The basic idea 4.2 Linear regression 4.3 Logit and probit models 4.4 Other models. 4.1 The basic idea. Panel data = data that are pooled for the same companies across time.

959 views • 72 slides

OLS at Bellevue College: Innovative Associate Degree for Adults with Cognitive Disabilities

OLS at Bellevue College: Innovative Associate Degree for Adults with Cognitive Disabilities. OLS at Bellevue College: Innovative Associate Degree for Adults with Cognitive Disabilities. Student Video from website will be inserted here.

386 views • 30 slides

Multiple Regression Analysis

Multiple Regression Analysis. y = b 0 + b 1 x 1 + b 2 x 2 + . . . b k x k + u 6. Heteroskedasticity. What is Heteroskedasticity. Recall the assumption of homoskedasticity implied that conditional on the explanatory variables, the variance of the unobserved error, u , was constant

604 views • 28 slides

Adaptive Centering with Random Effects in Studies of Time-Varying Treatments

Adaptive Centering with Random Effects in Studies of Time-Varying Treatments. Stephen W. Raudenbush University of Chicago December 11, 2006. Adaptive Centering with Random Effects in Studies of Time-Varying Treatments by Stephen W. Raudenbush University of Chicago Abstract.

367 views • 27 slides

Converging Technologies

OTEN Learning Support Site (OLS) for Online Distance Learners. Open Training & Education Network (OTEN) TAFE NSW - WSI. Paula E. Williams A/CEO Learning Technologies Unit (LTU). Converging Technologies. Background information. OTEN is the major DE provider in TAFE NSW

360 views • 27 slides

SOC 206 Lecture 1

SOC 206 Lecture 1. Statistics, Causation Simple Regression. Context of Discovery vs. Justification. Famous distinction by Hans Reichenbach Discovery: How do we come up with ideas Justification: How can we demonstrate that they are true. WHAT IS STATISTICS. Statistics is a language

358 views • 27 slides

2.5 Variances of the OLS Estimators

2.5 Variances of the OLS Estimators. -We have proven that the sampling distribution of OLS estimates (B 0 hat and B 1 hat) are centered around the true value -How FAR are they distributed around the true values? -The best estimator will be most narrowly distributed around the true values

269 views • 24 slides

Problems in Regression Analysis

Problems in Regression Analysis. Heteroscedasticity Violation of the constancy of the variance of the errors. Cross-sectional data Serial Correlation Violation of uncorrelated error terms Time-series data. Heteroscedasticity.

843 views • 23 slides

i) the greater the covariance between X and Y, the larger the (absolute value of) the slope

So. are how the computer determines the size of the intercept and the slope respectively in an OLS regression. The OLS equations give a nice, clear intuitive meaning about the influence of the variable X on the size of the slope, since it shows that:.

320 views • 23 slides

Multiple Regression Analysis

Multiple Regression Analysis. y = b 0 + b 1 x 1 + b 2 x 2 + . . . b k x k + u 6. Heteroskedasticity. What is Heteroskedasticity. Recall the assumption of homoskedasticity implied that conditional on the explanatory variables, the variance of the unobserved error, u , was constant

339 views • 22 slides

Montecarlo Simulation

Montecarlo Simulation. LAB NOV 27 2009 ECON 4550. Montecarlo Simulations. Monte Carlo simulation is a method of analysis based on artificially recreating a chance process (usually with a computer), running it many times, and directly observing the results

285 views • 21 slides

OLS SHORTCOMINGS

OLS SHORTCOMINGS. Preview of coming attractions. QUIZ. What are the main OLS assumptions? On average right Linear Predicting variables and error term uncorrelated No serial correlation in error term Homoscedasticity + Normality of error term. OLS assumptions consequences.

284 views • 18 slides

OLS SHORTCOMINGS

OLS SHORTCOMINGS. Preview of coming attractions. QUIZ. What are the main OLS assumptions? On average right Linear Predicting variables and error term uncorrelated No serial correlation in error term Homoscedasticity + Normality of error term. OLS assumptions consequences.

278 views • 17 slides

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215 views • 16 slides

Assumption MLR.3 Notes (No Perfect Collinearity)

Assumption MLR.3 Notes (No Perfect Collinearity). Perfect Collinearity can exist if: One variable is a constant multiple of another Logs are used inappropriately One variable is a linear function of two or more other variables

334 views • 16 slides

Assumption MLR.3 Notes (No Perfect Collinearity)

Assumption MLR.3 Notes (No Perfect Collinearity). Perfect Collinearity can exist if: One variable is a constant multiple of another Logs are used inappropriately One variable is a linear function of two or more other variables

170 views • 16 slides

Government Structure & spending

Government Structure & spending. Campbell, Rebecca and Geoffrey K. Turnball (2003). On Government Structure and Spending: The Effects of Management Form and Separation of Powers. Urban Studies , Vol. 40, No. 1, 23-24. Scott Stauffer ECON 539, Winter 2009. Government Structure & Spending.

243 views • 11 slides

Tips and Tricks

Tips and Tricks. 2012. Tips and Tricks. Hidden Microsoft tools in MS Word Text to Speech programs (Text Aloud) Electronic Feedback tools (StyleWriter) Voice Recognition (Dragon) Collaboration tools Gadgets and SW tools General tips and tricks Q&A. Hidden Microsoft tools in MS Word.

396 views • 11 slides

167 views • 9 slides

Economics 310

Economics 310. Lecture 4 Least-Squares Efficiency. Shazam Omitted Variable Example. |_sample 1 20 |_read x1 x2 x3 3 VARIABLES AND 20 OBSERVATIONS STARTING AT OBS 1 |_* Generate 1000 samples with true model: Y=10+3X1-5X2+4X3+e

179 views • 6 slides