Nick Bloom, Labor Topics, Spring 2010 LABOR TOPICS Nick Bloom Peers at Work.

Slides:

Advertisements

Similar presentations

Objectives 10.1 Simple linear regression

Advertisements

Discussion of Michael Ehrmann’s “Targeting Inflation from Below: How Do Inflation Expectations Behave?” Reflections on 25 Years of Inflation Targeting.

10-3 Inferences.

2.2 Correlation Correlation measures the direction and strength of the linear relationship between two quantitative variables.

Nick Bloom, Macro Topics, Spring Comments on: “Investment and Employment Dynamics in the Short-Run and the Long-Run”, Avinash Dixit, OEP (1997)

CMP3265 – Professional Issues and Research Methods Research Proposals: n Aims and objectives, Method, Evaluation n Yesterday – aims and objectives: clear,

Poverty and Income Distribution in Ethiopia: By Abebe Shimeles, PhD.

Section 4.4 Creating Randomization Distributions.

Ensemble Learning (2), Tree and Forest

PAI786: Urban Policy Class 2: Evaluating Social Programs.

4.1 Introducing Hypothesis Tests 4.2 Measuring significance with P-values Visit the Maths Study Centre 11am-5pm This presentation.

1. An Overview of the Data Analysis and Probability Standard for School Mathematics? 2.

Copyright © Cengage Learning. All rights reserved. 13 Linear Correlation and Regression Analysis.

Prediction concerning Y variable. Three different research questions What is the mean response, E(Y h ), for a given level, X h, of the predictor variable?

Empirical Research Methods in Computer Science Lecture 2, Part 1 October 19, 2005 Noah Smith.

Model Building III – Remedial Measures KNNL – Chapter 11.

Ch 8 Estimating with Confidence. Today’s Objectives ✓ I can interpret a confidence level. ✓ I can interpret a confidence interval in context. ✓ I can.

Statistical Bootstrapping Peter D. Christenson Biostatistician January 20, 2005.

Introduction to Inferential Statistics. Introduction  Researchers most often have a population that is too large to test, so have to draw a sample from.

Quantitative Skills 1: Graphing

Bootstrapping (And other statistical trickery). Reminder Of What We Do In Statistics Null Hypothesis Statistical Test Logic – Assume that the “no effect”

Microeconometric Modeling William Greene Stern School of Business New York University.

Slide 1 Estimating Performance Below the National Level Applying Simulation Methods to TIMSS Fourth Annual IES Research Conference Dan Sherman, Ph.D. American.

A Comparison of Statistical Significance Tests for Information Retrieval Evaluation CIKM´07, November 2007.

Phylogenetic Analysis. General comments on phylogenetics Phylogenetics is the branch of biology that deals with evolutionary relatedness Uses some measure.

From Theory to Practice: Inference about a Population Mean, Two Sample T Tests, Inference about a Population Proportion Chapters etc.

Slide Slide 1 Copyright © 2007 Pearson Education, Inc Publishing as Pearson Addison-Wesley. Lecture Slides Elementary Statistics Tenth Edition and the.

Matching Estimators Methods of Economic Investigation Lecture 11.

Topic (vi): New and Emerging Methods Topic organizer: Maria Garcia (USA) UNECE Work Session on Statistical Data Editing Oslo, Norway, September 2012.

Managerial Economics Demand Estimation & Forecasting.

Feature Engineering Studio September 23, Let’s start by discussing the HW.

+ Chapter 12: More About Regression Section 12.1 Inference for Linear Regression.

Simple Linear Regression. The term linear regression implies that  Y|x is linearly related to x by the population regression equation  Y|x =  +  x.

Chapter 9 Probability. 2 More Statistical Notation  Chance is expressed as a percentage  Probability is expressed as a decimal  The symbol for probability.

Slide Slide 1 Section 6-5 The Central Limit Theorem.

Resampling techniques

Question paper 1997.

© 2011 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.

Lynn Lethbridge SHRUG November, What is Bootstrapping? A method to estimate a statistic’s sampling distribution Bootstrap samples are drawn repeatedly.

Statistics for Decision Making Basic Inference QM Fall 2003 Instructor: John Seydel, Ph.D.

Cluster validation Integration ICES Bioinformatics.

Biostatistics Case Studies 2006 Peter D. Christenson Biostatistician Session 2: Correlation of Time Courses of Simultaneous.

The Practice of Statistics Third Edition Yates, Moore & Starnes Chapter 10: Estimating with Confidence.

Some Alternative Approaches Two Samples. Outline Scales of measurement may narrow down our options, but the choice of final analysis is up to the researcher.

Robust Regression. Regression Methods  We are going to look at three approaches to robust regression:  Regression with robust standard errors  Regression.

Chapter 7: The Distribution of Sample Means. Frequency of Scores Scores Frequency.

Copyright © Cengage Learning. All rights reserved. The Binomial Probability Distribution and Related Topics 6.

Special Topics in Educational Data Mining HUDK5199 Spring term, 2013 March 6, 2013.

Do-Now 11/12/15 Get out your claims paragraph from Tuesday. Put a Star * next to your claim Underline your evidence Put a squiggly line under your analysis.

Diagnostic verification and extremes: 1 st Breakout Discussed the need for toolkit to build beyond current capabilities (e.g., NCEP) Identified (and began.

VizTree Huyen Dao and Chris Ackermann. Introducing example

STAT03 - Descriptive statistics (cont.) - variability 1 Descriptive statistics (cont.) - variability Lecturer: Smilen Dimitrov Applied statistics for testing.

Chapter 8 Interval Estimates For Proportions, Mean Differences And Proportion Differences.

1/61: Topic 1.2 – Extensions of the Linear Regression Model Microeconometric Modeling William Greene Stern School of Business New York University New York.

A Binary Linear Programming Formulation of the Graph Edit Distance Presented by Shihao Ji Duke University Machine Learning Group July 17, 2006 Authors:

Lecture 7: Bivariate Statistics. 2 Properties of Standard Deviation Variance is just the square of the S.D. If a constant is added to all scores, it has.

The inference and accuracy We learned how to estimate the probability that the percentage of some subjects in the sample would be in a given interval by.

Bootstrapping James G. Anderson, Ph.D. Purdue University.

STA248 week 121 Bootstrap Test for Pairs of Means of a Non-Normal Population – small samples Suppose X 1, …, X n are iid from some distribution independent.

Bootstrapping Byrne Chapter 12. Bootstrapping Quick distinction: – Population distribution – Sample distribution – Sampling distribution.

Analyzing Redistribution Matrix with Wavelet

Combining Random Variables

RES 723 Enthusiastic Study/snaptutorial.com

Methods of Economic Investigation Lecture 12

Sampling Distribution

Sampling Distribution

QQ Plot Quantile to Quantile Plot Quantile: QQ Plot:

Chapter 7: The Distribution of Sample Means

I can determine the different sampling techniques used in real life.

Bootstrapping and Bootstrapping Regression Models

Presentation transcript:

Nick Bloom, Labor Topics, Spring 2010 LABOR TOPICS Nick Bloom Peers at Work

Nick Bloom, Labor Topics, Spring 2010 Comments on Mas and Moretti (2008) Great paper: innovative way to address an interesting topic – peer effects within workplaces Huge dataset High frequency productivity measurement Interesting setting Points to think about and learn from this I want to discuss: (A) Reflection problem (B) Bootstrap (C) Presentation (graphics and robustness test)

Nick Bloom, Labor Topics, Spring 2010 Reflection problems (1/2) One of the central issues in addressing spillovers is distinguishing these from: (A) Unobserved shocks (B) Sorting (selection effects) These issues are often called the “Reflection Problem” after Manski (1993). Ways to deal with this are: (A) Controlling for (instrumenting) unobserved shocks - Do this here using very long-run data, so no SR shocks (B) Having random matching by pairs - Do this here by claiming the shift matching in random (and then test this)

Nick Bloom, Labor Topics, Spring 2010 Reflection problems (2/2) (C) Using a distance metric to put more structure on the estimation - They do this with the facing/behind till distinction Overall I think they do a convincing job of addressing the key issue in the “peer effects” and “spillovers” literature

Nick Bloom, Labor Topics, Spring 2010 Bootstrap and robustness tests One issue they faced was in generating appropriate standard- errors in the regressions. They had a generated regressor (predicted Θ i ) in the second step – this has error around it so needs its SE adjusted Easiest way to do this is Bootstrap – keep re-drawing (with replacement) from the original data to look at the distribution of the coefficients. - Idea is treats the sample as the population Very computationally intensive (need to re-estimate everything 1000 times over) so they did a more complex Bayesian alternative

Nick Bloom, Labor Topics, Spring 2010 Presentation – graphics and robustness They used fantastic graphics to prove their results – if you can always have some graphs of results, particularly with this kind of sharp discountinuity effect They also always tested their key claims – for example that assignment of people to shifts is “random” - If you ever make a claim in a paper always try to test this as much more convincing