INSTRUMENTAL VARIABLES
|
|
- Roy Houston
- 6 years ago
- Views:
Transcription
1 INSTRUMENTAL VARIABLES Technical Track Sessin IV Sergi Urzua University f Maryland
2 Instrumental Variables and IE Tw main uses f IV in impact evaluatin: 1. Crrect fr difference between assignment f treatment and actual treatment E.g. Randmized Assignment with nn-cmpliers E.g. Fuzzy Regressin Discntinuity 2. Lk fr exgenus variatin (ex-pst) t evaluate the impact f a prgram in absence f a prspective design. Here: General Principles behind IV and an example with a fcus n use (1)
3 An example t start ff with Say we wish t evaluate a vluntary jb training prgram Any unemplyed persn is eligible (Universal eligibility) Sme peple chse t register (Participants) Other peple chse nt t register (Nn-participants) Sme simple ways t evaluate the prgram: Randm sample cntaining treatment status (P), exgenus cntrls (X) and utcme (Y). First alternative: T cmpare situatin f participants and nn-participants after the interventin. We already learned this estimatr wuld be biased!
4 Vluntary jb training prgram Say we decide t cmpare utcmes fr thse wh participate t the utcmes f thse wh d nt participate: A simple mdel t d this: y = α + β 1 P + β 2 x + ε P = 1 If persn participates in training 0 If persn des nt participate in training x = Cntrl variables (exgenus & bserved) Why wuld this nt be crrect? 2 prblems: Decisin t participate in training is endgenus (e.g. based n an unmeasurable characteristic). Variables that we mit (e.g. unmeasured) but that are imprtant P and ε are crrelated
5 What can we d t slve this prblem? We estimate: y = β 0 + β 1 x + β 2 P + ε S the prblem is the crrelatin between P and ε Intuitin f IV: Hw abut we replace P with smething else that is similar t P but is nt crrelated with ε
6 Back t the jb training prgram P = participatin ε = that part f utcmes that is nt explained by prgram participatin r by bserved characteristics Instrumental variable will be a variable Z that is: (1) Clsely related t participatin P. [i.e. Crr ( Z, P ) > 0] (2) but desn t directly affect peple s utcmes Y, except thrugh its effect n participatin. [i.e. Crr ( Z, ε ) = 0 ] Hard t cme up with such a variable ex-pst but if we anticipate this prblem, we can plan fr it
7 Generating an instrumental variable Encuragement design: - Say that a scial wrker visits persns t encurage them t participate. She nly visits 50% f persns n her rster, and She randmly chses whm she will visit If she is effective, many peple she visits will enrll. There will be a crrelatin between receiving a visit and enrlling. - But visit des nt have direct effect n utcmes (e.g. incme) except frm its effect thrugh enrllment in the training prgram. Randmized encuragement r prmtin visits can be a useful instrumental variable.
8 Characteristics f an instrumental variable Define a new variable Z Z = 1 If persn was randmly chsen t receive the encuragement visit frm the scial wrker 0 If persn was randmly chsen nt t receive the encuragement visit frm the scial wrker Crr ( Z, P ) > 0 Peple wh receive the encuragement visit are mre likely t participate than thse wh dn t Crr ( Z, ε ) = 0 N crrelatin between receiving a visit and benefit t the prgram apart frm the effect f the visit n participatin. Z therefre satisfies the cnditins fr being an instrumental variable
9 Tw-stage least squares (2SLS) Remember the riginal mdel with endgenus P: Step 1 y = β 0 + β 1 x + β 2 P + ε Regress the endgenus variable P n the instrumental variable(s) Z and ther exgenus variables P = δ 0 + δ 1 x + δ 2 Z + τ Calculate the predicted value f P fr each bservatin: P^ Since Z and x are nt crrelated with ε, neither will be Yu will need ne instrumental variable fr each ptentially endgenus regressr. P^
10 Tw-stage least squares (2SLS) Step 2 Regress y n the predicted variable P and the ther exgenus variables y = β 0 + β 1 x + β 2 + ε Nte: The standard errrs f the secnd stage OLS need t be crrected because P^ is a generated regressr. In Practice: Use STATA ivreg cmmand, which des the tw steps at nce and reprts crrect standard errrs. Intuitin: By using Z t predict P, we cleaned P f its crrelatin with η It can be shwn that (under certain cnditins) β 2,IV yields a cnsistent estimatr f γ 2 (large sample thery) P^
11 Example: Training & Earnings Cnsider the mdel: y = β 0 + β 2 P + ε Randm Sample f 10,000 bservatins Data cntains (y, P, Z ) 6,328 individuals with D=1 & 3,618 with D=0.
12 Example: Training & Earnings Cnsider the mdel: y = β 0 + β 2 P + ε First Strategy (Participants vs. Nn-participants) E(Y1 D=1) = E(Y0 D=0) = Thus, δ = E(Y1 D=1) - E(Y0 D=0) = *** Yu might cnclude then that the effect f the prgram is negative. Selectin bias?
13 Example: Training & Earnings Cnsider the mdel: y = β 0 + β 2 P + ε Let intrduce the instrument Z: Crr(Z,D)=0.37*** Pr(D=1 Z=1)=0.82 Pr(D=1 Z=0)= 0.45 Cv(y,Z) Cv(P,Z) E(Y Z 1) E(Y Z 0) E(P Z 1) E(P Z 0) 0.210
14 Example: Was it real? Cnsider the mdel: y = β 0 + β 2 P + ε I generated the data: Y1(u)= ε1(u) Y0(u)= ε0(u) P = 1 if Z(u) Y0(u)>0, =0 therwise Y(u) = Y1(u) * P(u) + Y0(u) * (1-P(u)) THUS, I KNOW THE TRUE AVERAGE TREATMENT EFFECT
15 Example: Was it real? Cnsider the mdel: y = β 0 + β 2 P + ε In ur fake data, we bserve (D,Z,Y1,Y0,Y) Treatment Effect= E(Y1 D=1)-E(Y0 D=1) = 0.2 Selectin Bias = E(Y0 D=1)-E(Y0 D=0) = δ = E(Y1 D=1)-E(Y0 D=0) = 0.2+(-1.423) = IV gt it right (IV=0.21) This is nt rcket science!
16 Nn ecnmetric intuitin: Illustratin frm vluntary jb training prgram Ppulatin eligible fr jb training prgram Randm Sample Randmized assignment Standard Infrmatin nly Mnthly incme 1 year later = 700 Standard Infrmatin + Encuragement visit Mnthly incme 1 year later = % take-up 75% take-up Questin: what is the impact f the jb training prgram n the mnthly incme f participants?
17 Standard Infrmatin Package nly Mnthly incme 1 year later = 700 Standard + Additinal Infrmatin Package Mnthly incme 1 year later = % take-up 75% take-up Questin: what is the impact f the jb training prgram? Stage 1: Take-up difference between well infrmed and nt well infrmed :... Stage 2a: Incme difference between the well infrmed and nt well infrmed grup:.. Stage 2b: Impact f participatin: Incme difference scaled by take-up difference:
18 Reminder and a wrd f cautin crr (Z,ε) =0 If crr (Z, ε) 0, Bad instrument Finding a naturally gd instrument is hard! But yu can build ne yurself with a randmized encuragement design crr (Z,P) 0 If crr (Z, P) 0 Weak instruments : the crrelatin between Z and P needs t be sufficiently strng. If nt, the bias stays large even fr large sample sizes.
19 Reminder and a wrd f cautin: Hetergeneity It is pssible t shw that, in the cntext f hetergeneus effects, the IV apprach might NOT prvide meaningful results. Hwever, we can still evaluate using structural mdels. Example: Evaluating the impact f financial intermediatin
20 References Heckman, J., E. Vytlacil, S. Urzua (2006). Understanding instrumental Variables in Mdels with Essential Hetergeneity, Review f Ecnmics and Statistics, v88, n3. Heckman, J., S. Urzua(2010) Cmparing IV With Structural Mdels: What Simple IV Can and Cannt. Jurnal f Ecnmetrics, Vl. 156(1), 2010 Angrist, J. D. and A. Krueger (2001). Instrumental Variables and the Search fr Identificatin: Frm Supply and Demand t Natural Experiments, Jurnal f Ecnmic Perspectives, 15(4). Angrist, J. D., G. W. Imbens and D. B. Rubin (1996). Identificatin f Causal Effects Using Instrumental Variables, Jurnal f the American Statistical Assciatin, Vl. 91, 434. Angrist, J., Bettinger, E., Blm, E., King, E. and M. Kremer (2002). Vuchers fr Private Schling in Clmbia: Evidence frm a Randmized Natural Experiment, American Ecnmic Review, 92, 5. Imbens, G. W. and J. D. Angrist, (1994). Identificatin and Estimatin f Lcal Average Treatment Effects. Ecnmetrica, 62(2). Newman, J., M. Pradhan, L. B. Rawlings, G. Ridder, R. Ca, J. L. Evia, (2002). An Impact Evaluatin f Educatin, Health, and Water Supply Investments by the Blivian Scial Investment Fund., Wrld Bank Ecnmic Review, vl. 16(2).
CAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank
CAUSAL INFERENCE Technical Track Sessin I Phillippe Leite The Wrld Bank These slides were develped by Christel Vermeersch and mdified by Phillippe Leite fr the purpse f this wrkshp Plicy questins are causal
More informationEvaluating enterprise support: state of the art and future challenges. Dirk Czarnitzki KU Leuven, Belgium, and ZEW Mannheim, Germany
Evaluating enterprise supprt: state f the art and future challenges Dirk Czarnitzki KU Leuven, Belgium, and ZEW Mannheim, Germany Intrductin During the last decade, mircecnmetric ecnmetric cunterfactual
More informationInternal vs. external validity. External validity. This section is based on Stock and Watson s Chapter 9.
Sectin 7 Mdel Assessment This sectin is based n Stck and Watsn s Chapter 9. Internal vs. external validity Internal validity refers t whether the analysis is valid fr the ppulatin and sample being studied.
More informationMATCHING TECHNIQUES. Technical Track Session VI. Emanuela Galasso. The World Bank
MATCHING TECHNIQUES Technical Track Sessin VI Emanuela Galass The Wrld Bank These slides were develped by Christel Vermeersch and mdified by Emanuela Galass fr the purpse f this wrkshp When can we use
More informationMATCHING TECHNIQUES Technical Track Session VI Céline Ferré The World Bank
MATCHING TECHNIQUES Technical Track Sessin VI Céline Ferré The Wrld Bank When can we use matching? What if the assignment t the treatment is nt dne randmly r based n an eligibility index, but n the basis
More informationREGRESSION DISCONTINUITY (RD) Technical Track Session V. Dhushyanth Raju Julieta Trias The World Bank
REGRESSION DISCONTINUITY (RD) Technical Track Sessin V Dhushyanth Raju Julieta Trias The Wrld Bank These slides cnstitute supprting material t the Impact Evaluatin in Practice Handbk : Gertler, P. J.;
More informationGetting Involved O. Responsibilities of a Member. People Are Depending On You. Participation Is Important. Think It Through
f Getting Invlved O Literature Circles can be fun. It is exciting t be part f a grup that shares smething. S get invlved, read, think, and talk abut bks! Respnsibilities f a Member Remember a Literature
More informationSIZE BIAS IN LINE TRANSECT SAMPLING: A FIELD TEST. Mark C. Otto Statistics Research Division, Bureau of the Census Washington, D.C , U.S.A.
SIZE BIAS IN LINE TRANSECT SAMPLING: A FIELD TEST Mark C. Ott Statistics Research Divisin, Bureau f the Census Washingtn, D.C. 20233, U.S.A. and Kenneth H. Pllck Department f Statistics, Nrth Carlina State
More informationSection 11 Simultaneous Equations
Sectin 11 Simultaneus Equatins The mst crucial f ur OLS assumptins (which carry er t mst f the ther estimatrs that we hae studied) is that the regressrs be exgenus uncrrelated with the errr term This assumptin
More informationPSU GISPOPSCI June 2011 Ordinary Least Squares & Spatial Linear Regression in GeoDa
There are tw parts t this lab. The first is intended t demnstrate hw t request and interpret the spatial diagnstics f a standard OLS regressin mdel using GeDa. The diagnstics prvide infrmatin abut the
More informationCHAPTER 24: INFERENCE IN REGRESSION. Chapter 24: Make inferences about the population from which the sample data came.
MATH 1342 Ch. 24 April 25 and 27, 2013 Page 1 f 5 CHAPTER 24: INFERENCE IN REGRESSION Chapters 4 and 5: Relatinships between tw quantitative variables. Be able t Make a graph (scatterplt) Summarize the
More informationA Matrix Representation of Panel Data
web Extensin 6 Appendix 6.A A Matrix Representatin f Panel Data Panel data mdels cme in tw brad varieties, distinct intercept DGPs and errr cmpnent DGPs. his appendix presents matrix algebra representatins
More informationHypothesis Tests for One Population Mean
Hypthesis Tests fr One Ppulatin Mean Chapter 9 Ala Abdelbaki Objective Objective: T estimate the value f ne ppulatin mean Inferential statistics using statistics in rder t estimate parameters We will be
More informationBootstrap Method > # Purpose: understand how bootstrap method works > obs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(obs) >
Btstrap Methd > # Purpse: understand hw btstrap methd wrks > bs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(bs) > mean(bs) [1] 21.64625 > # estimate f lambda > lambda = 1/mean(bs);
More informationDifferentiation Applications 1: Related Rates
Differentiatin Applicatins 1: Related Rates 151 Differentiatin Applicatins 1: Related Rates Mdel 1: Sliding Ladder 10 ladder y 10 ladder 10 ladder A 10 ft ladder is leaning against a wall when the bttm
More informationCS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007
CS 477/677 Analysis f Algrithms Fall 2007 Dr. Gerge Bebis Curse Prject Due Date: 11/29/2007 Part1: Cmparisn f Srting Algrithms (70% f the prject grade) The bjective f the first part f the assignment is
More information4th Indian Institute of Astrophysics - PennState Astrostatistics School July, 2013 Vainu Bappu Observatory, Kavalur. Correlation and Regression
4th Indian Institute f Astrphysics - PennState Astrstatistics Schl July, 2013 Vainu Bappu Observatry, Kavalur Crrelatin and Regressin Rahul Ry Indian Statistical Institute, Delhi. Crrelatin Cnsider a tw
More informationKinetic Model Completeness
5.68J/10.652J Spring 2003 Lecture Ntes Tuesday April 15, 2003 Kinetic Mdel Cmpleteness We say a chemical kinetic mdel is cmplete fr a particular reactin cnditin when it cntains all the species and reactins
More informationModelling of Clock Behaviour. Don Percival. Applied Physics Laboratory University of Washington Seattle, Washington, USA
Mdelling f Clck Behaviur Dn Percival Applied Physics Labratry University f Washingtn Seattle, Washingtn, USA verheads and paper fr talk available at http://faculty.washingtn.edu/dbp/talks.html 1 Overview
More informationLesson Plan. Recode: They will do a graphic organizer to sequence the steps of scientific method.
Lessn Plan Reach: Ask the students if they ever ppped a bag f micrwave ppcrn and nticed hw many kernels were unppped at the bttm f the bag which made yu wnder if ther brands pp better than the ne yu are
More informationTuring Machines. Human-aware Robotics. 2017/10/17 & 19 Chapter 3.2 & 3.3 in Sipser Ø Announcement:
Turing Machines Human-aware Rbtics 2017/10/17 & 19 Chapter 3.2 & 3.3 in Sipser Ø Annuncement: q q q q Slides fr this lecture are here: http://www.public.asu.edu/~yzhan442/teaching/cse355/lectures/tm-ii.pdf
More informationIntroduction to Spacetime Geometry
Intrductin t Spacetime Gemetry Let s start with a review f a basic feature f Euclidean gemetry, the Pythagrean therem. In a twdimensinal crdinate system we can relate the length f a line segment t the
More informationLHS Mathematics Department Honors Pre-Calculus Final Exam 2002 Answers
LHS Mathematics Department Hnrs Pre-alculus Final Eam nswers Part Shrt Prblems The table at the right gives the ppulatin f Massachusetts ver the past several decades Using an epnential mdel, predict the
More informationFive Whys How To Do It Better
Five Whys Definitin. As explained in the previus article, we define rt cause as simply the uncvering f hw the current prblem came int being. Fr a simple causal chain, it is the entire chain. Fr a cmplex
More informationResampling Methods. Chapter 5. Chapter 5 1 / 52
Resampling Methds Chapter 5 Chapter 5 1 / 52 1 51 Validatin set apprach 2 52 Crss validatin 3 53 Btstrap Chapter 5 2 / 52 Abut Resampling An imprtant statistical tl Pretending the data as ppulatin and
More informationResampling Methods. Cross-validation, Bootstrapping. Marek Petrik 2/21/2017
Resampling Methds Crss-validatin, Btstrapping Marek Petrik 2/21/2017 Sme f the figures in this presentatin are taken frm An Intrductin t Statistical Learning, with applicatins in R (Springer, 2013) with
More informationA Regression Solution to the Problem of Criterion Score Comparability
A Regressin Slutin t the Prblem f Criterin Scre Cmparability William M. Pugh Naval Health Research Center When the criterin measure in a study is the accumulatin f respnses r behavirs fr an individual
More informationChapter 3: Cluster Analysis
Chapter 3: Cluster Analysis } 3.1 Basic Cncepts f Clustering 3.1.1 Cluster Analysis 3.1. Clustering Categries } 3. Partitining Methds 3..1 The principle 3.. K-Means Methd 3..3 K-Medids Methd 3..4 CLARA
More informationAn example to start off with
Impact Evaluation Technical Track Session IV Instrumental Variables Christel Vermeersch Human Development Human Network Development Network Middle East and North Africa Region World Bank Institute Spanish
More informationSection 11 Simultaneous Equations
Sectin 11 Simultaneus Equatins The mst crucial f ur OLS assumptins (which carry er t mst f the ther estimatrs that we hae studied) is that the regressrs be exgenus uncrrelated with the errr term This assumptin
More informationFunctional Form and Nonlinearities
Sectin 6 Functinal Frm and Nnlinearities This is a gd place t remind urselves f Assumptin #0: That all bservatins fllw the same mdel. Levels f measurement and kinds f variables There are (at least) three
More informationWriting Guidelines. (Updated: November 25, 2009) Forwards
Writing Guidelines (Updated: Nvember 25, 2009) Frwards I have fund in my review f the manuscripts frm ur students and research assciates, as well as thse submitted t varius jurnals by thers that the majr
More informationA New Evaluation Measure. J. Joiner and L. Werner. The problems of evaluation and the needed criteria of evaluation
III-l III. A New Evaluatin Measure J. Jiner and L. Werner Abstract The prblems f evaluatin and the needed criteria f evaluatin measures in the SMART system f infrmatin retrieval are reviewed and discussed.
More informationFirst Survey. Carried out by IPR feedback
First Survey Carried ut by IPR feedback Hell my name is and I am calling frm IPR Feedback. IPR Feedback is a research center hired by Bccni University t study the pinins f the citizens f Arezz regarding
More informationSimple Linear Regression (single variable)
Simple Linear Regressin (single variable) Intrductin t Machine Learning Marek Petrik January 31, 2017 Sme f the figures in this presentatin are taken frm An Intrductin t Statistical Learning, with applicatins
More informationLab 1 The Scientific Method
INTRODUCTION The fllwing labratry exercise is designed t give yu, the student, an pprtunity t explre unknwn systems, r universes, and hypthesize pssible rules which may gvern the behavir within them. Scientific
More informationAP Statistics Practice Test Unit Three Exploring Relationships Between Variables. Name Period Date
AP Statistics Practice Test Unit Three Explring Relatinships Between Variables Name Perid Date True r False: 1. Crrelatin and regressin require explanatry and respnse variables. 1. 2. Every least squares
More informationEric Klein and Ning Sa
Week 12. Statistical Appraches t Netwrks: p1 and p* Wasserman and Faust Chapter 15: Statistical Analysis f Single Relatinal Netwrks There are fur tasks in psitinal analysis: 1) Define Equivalence 2) Measure
More informationIn the OLG model, agents live for two periods. they work and divide their labour income between consumption and
1 The Overlapping Generatins Mdel (OLG) In the OLG mdel, agents live fr tw perids. When ung the wrk and divide their labur incme between cnsumptin and savings. When ld the cnsume their savings. As the
More informationTHE LIFE OF AN OBJECT IT SYSTEMS
THE LIFE OF AN OBJECT IT SYSTEMS Persns, bjects, r cncepts frm the real wrld, which we mdel as bjects in the IT system, have "lives". Actually, they have tw lives; the riginal in the real wrld has a life,
More informationCollocation Map for Overcoming Data Sparseness
Cllcatin Map fr Overcming Data Sparseness Mnj Kim, Yung S. Han, and Key-Sun Chi Department f Cmputer Science Krea Advanced Institute f Science and Technlgy Taejn, 305-701, Krea mj0712~eve.kaist.ac.kr,
More informationEnd of Course Algebra I ~ Practice Test #2
End f Curse Algebra I ~ Practice Test #2 Name: Perid: Date: 1: Order the fllwing frm greatest t least., 3, 8.9, 8,, 9.3 A. 8, 8.9,, 9.3, 3 B., 3, 8, 8.9,, 9.3 C. 9.3, 3,,, 8.9, 8 D. 3, 9.3,,, 8.9, 8 2:
More informationLarge Sample Hypothesis Tests for a Population Proportion
Ntes-10.3a Large Sample Hypthesis Tests fr a Ppulatin Prprtin ***Cin Tss*** 1. A friend f yurs claims that when he tsses a cin he can cntrl the utcme. Yu are skeptical and want him t prve it. He tsses
More informationLecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff
Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeff Reading: Chapter 2 STATS 202: Data mining and analysis September 27, 2017 1 / 20 Supervised vs. unsupervised learning In unsupervised
More informationCONSTRUCTING STATECHART DIAGRAMS
CONSTRUCTING STATECHART DIAGRAMS The fllwing checklist shws the necessary steps fr cnstructing the statechart diagrams f a class. Subsequently, we will explain the individual steps further. Checklist 4.6
More informationExcessive Social Imbalances and the Performance of Welfare States in the EU. Frank Vandenbroucke, Ron Diris and Gerlinde Verbist
Excessive Scial Imbalances and the Perfrmance f Welfare States in the EU Frank Vandenbrucke, Rn Diris and Gerlinde Verbist Child pverty in the Eurzne, SILC 2008 35.00 30.00 25.00 20.00 15.00 10.00 5.00.00
More informationLecture 2: Supervised vs. unsupervised learning, bias-variance tradeoff
Lecture 2: Supervised vs. unsupervised learning, bias-variance tradeff Reading: Chapter 2 STATS 202: Data mining and analysis September 27, 2017 1 / 20 Supervised vs. unsupervised learning In unsupervised
More informationENSC Discrete Time Systems. Project Outline. Semester
ENSC 49 - iscrete Time Systems Prject Outline Semester 006-1. Objectives The gal f the prject is t design a channel fading simulatr. Upn successful cmpletin f the prject, yu will reinfrce yur understanding
More informationWhat is Statistical Learning?
What is Statistical Learning? Sales 5 10 15 20 25 Sales 5 10 15 20 25 Sales 5 10 15 20 25 0 50 100 200 300 TV 0 10 20 30 40 50 Radi 0 20 40 60 80 100 Newspaper Shwn are Sales vs TV, Radi and Newspaper,
More informationAdmin. MDP Search Trees. Optimal Quantities. Reinforcement Learning
Admin Reinfrcement Learning Cntent adapted frm Berkeley CS188 MDP Search Trees Each MDP state prjects an expectimax-like search tree Optimal Quantities The value (utility) f a state s: V*(s) = expected
More informationREADING STATECHART DIAGRAMS
READING STATECHART DIAGRAMS Figure 4.48 A Statechart diagram with events The diagram in Figure 4.48 shws all states that the bject plane can be in during the curse f its life. Furthermre, it shws the pssible
More informationPattern Recognition 2014 Support Vector Machines
Pattern Recgnitin 2014 Supprt Vectr Machines Ad Feelders Universiteit Utrecht Ad Feelders ( Universiteit Utrecht ) Pattern Recgnitin 1 / 55 Overview 1 Separable Case 2 Kernel Functins 3 Allwing Errrs (Sft
More informationDetermining the Accuracy of Modal Parameter Estimation Methods
Determining the Accuracy f Mdal Parameter Estimatin Methds by Michael Lee Ph.D., P.E. & Mar Richardsn Ph.D. Structural Measurement Systems Milpitas, CA Abstract The mst cmmn type f mdal testing system
More informationYou need to be able to define the following terms and answer basic questions about them:
CS440/ECE448 Sectin Q Fall 2017 Midterm Review Yu need t be able t define the fllwing terms and answer basic questins abut them: Intr t AI, agents and envirnments Pssible definitins f AI, prs and cns f
More informationThis project has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement number
This prject has received funding frm the Eurpean Unin s Hrizn 2020 research and innvatin prgramme under grant agreement number 727524. Credit t & http://www.h3uni.rg/ https://ec.eurpa.eu/jrc/en/publicatin/eur-scientific-andtechnical-research-reprts/behaviural-insights-appliedplicy-eurpean-reprt-2016
More informationHow do scientists measure trees? What is DBH?
Hw d scientists measure trees? What is DBH? Purpse Students develp an understanding f tree size and hw scientists measure trees. Students bserve and measure tree ckies and explre the relatinship between
More informationChapter 5: The Keynesian System (I): The Role of Aggregate Demand
LECTURE NOTES Chapter 5: The Keynesian System (I): The Rle f Aggregate Demand 1. The Prblem f Unemplyment Keynesian ecnmics develped in the cntext f the Great Depressin Sharp fall in GDP High rate f unemplyment
More informationMaximum A Posteriori (MAP) CS 109 Lecture 22 May 16th, 2016
Maximum A Psteriri (MAP) CS 109 Lecture 22 May 16th, 2016 Previusly in CS109 Game f Estimatrs Maximum Likelihd Nn spiler: this didn t happen Side Plt argmax argmax f lg Mther f ptimizatins? Reviving an
More informationWe can see from the graph above that the intersection is, i.e., [ ).
MTH 111 Cllege Algebra Lecture Ntes July 2, 2014 Functin Arithmetic: With nt t much difficulty, we ntice that inputs f functins are numbers, and utputs f functins are numbers. S whatever we can d with
More informationWRITING THE REPORT. Organizing the report. Title Page. Table of Contents
WRITING THE REPORT Organizing the reprt Mst reprts shuld be rganized in the fllwing manner. Smetime there is a valid reasn t include extra chapters in within the bdy f the reprt. 1. Title page 2. Executive
More informationCHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India
CHAPTER 3 INEQUALITIES Cpyright -The Institute f Chartered Accuntants f India INEQUALITIES LEARNING OBJECTIVES One f the widely used decisin making prblems, nwadays, is t decide n the ptimal mix f scarce
More informationThis section is primarily focused on tools to aid us in finding roots/zeros/ -intercepts of polynomials. Essentially, our focus turns to solving.
Sectin 3.2: Many f yu WILL need t watch the crrespnding vides fr this sectin n MyOpenMath! This sectin is primarily fcused n tls t aid us in finding rts/zers/ -intercepts f plynmials. Essentially, ur fcus
More informationSAMPLING DYNAMICAL SYSTEMS
SAMPLING DYNAMICAL SYSTEMS Melvin J. Hinich Applied Research Labratries The University f Texas at Austin Austin, TX 78713-8029, USA (512) 835-3278 (Vice) 835-3259 (Fax) hinich@mail.la.utexas.edu ABSTRACT
More informationk-nearest Neighbor How to choose k Average of k points more reliable when: Large k: noise in attributes +o o noise in class labels
Mtivating Example Memry-Based Learning Instance-Based Learning K-earest eighbr Inductive Assumptin Similar inputs map t similar utputs If nt true => learning is impssible If true => learning reduces t
More informationCOMP 551 Applied Machine Learning Lecture 11: Support Vector Machines
COMP 551 Applied Machine Learning Lecture 11: Supprt Vectr Machines Instructr: (jpineau@cs.mcgill.ca) Class web page: www.cs.mcgill.ca/~jpineau/cmp551 Unless therwise nted, all material psted fr this curse
More informationSection 5.8 Notes Page Exponential Growth and Decay Models; Newton s Law
Sectin 5.8 Ntes Page 1 5.8 Expnential Grwth and Decay Mdels; Newtn s Law There are many applicatins t expnential functins that we will fcus n in this sectin. First let s lk at the expnential mdel. Expnential
More informationIN a recent article, Geary [1972] discussed the merit of taking first differences
The Efficiency f Taking First Differences in Regressin Analysis: A Nte J. A. TILLMAN IN a recent article, Geary [1972] discussed the merit f taking first differences t deal with the prblems that trends
More informationComputational modeling techniques
Cmputatinal mdeling techniques Lecture 2: Mdeling change. In Petre Department f IT, Åb Akademi http://users.ab.fi/ipetre/cmpmd/ Cntent f the lecture Basic paradigm f mdeling change Examples Linear dynamical
More informationPhysics 2010 Motion with Constant Acceleration Experiment 1
. Physics 00 Mtin with Cnstant Acceleratin Experiment In this lab, we will study the mtin f a glider as it accelerates dwnhill n a tilted air track. The glider is supprted ver the air track by a cushin
More informationThe general linear model and Statistical Parametric Mapping I: Introduction to the GLM
The general linear mdel and Statistical Parametric Mapping I: Intrductin t the GLM Alexa Mrcm and Stefan Kiebel, Rik Hensn, Andrew Hlmes & J-B J Pline Overview Intrductin Essential cncepts Mdelling Design
More informationInstructional Plan. Representational/Drawing Level
Instructinal Plan Representatinal/Drawing Level Name f Math Skill/Cncept: Divisin Prcess and Divisin with Remainders Prerequisite Skills Needed: 1.) Mastery f dividing cncrete bjects int equal grups. 2.)
More informationDepartment of Economics, University of California, Davis Ecn 200C Micro Theory Professor Giacomo Bonanno. Insurance Markets
Department f Ecnmics, University f alifrnia, Davis Ecn 200 Micr Thery Prfessr Giacm Bnann Insurance Markets nsider an individual wh has an initial wealth f. ith sme prbability p he faces a lss f x (0
More informationHow T o Start A n Objective Evaluation O f Your Training Program
J O U R N A L Hw T Start A n Objective Evaluatin O f Yur Training Prgram DONALD L. KIRKPATRICK, Ph.D. Assistant Prfessr, Industrial Management Institute University f Wiscnsin Mst training m e n agree that
More informationAssessment Primer: Writing Instructional Objectives
Assessment Primer: Writing Instructinal Objectives (Based n Preparing Instructinal Objectives by Mager 1962 and Preparing Instructinal Objectives: A critical tl in the develpment f effective instructin
More informationSUPPLEMENTARY MATERIAL GaGa: a simple and flexible hierarchical model for microarray data analysis
SUPPLEMENTARY MATERIAL GaGa: a simple and flexible hierarchical mdel fr micrarray data analysis David Rssell Department f Bistatistics M.D. Andersn Cancer Center, Hustn, TX 77030, USA rsselldavid@gmail.cm
More informationSession IV Instrumental Variables
Impact Evaluation Session IV Instrumental Variables Christel M. J. Vermeersch January 008 Human Development Human Network Development Network Middle East and North Africa Middle East Region and North Africa
More informationAP Statistics Notes Unit Two: The Normal Distributions
AP Statistics Ntes Unit Tw: The Nrmal Distributins Syllabus Objectives: 1.5 The student will summarize distributins f data measuring the psitin using quartiles, percentiles, and standardized scres (z-scres).
More informationInternship Programme of German Business for the Countries of Western Balkans. How to complete your application?
Befre applying please make sure that yu fulfil the precnditins: Undergraduate students wh are enrlled in the 5th semester r higher when applying, Master r PhD students and yung graduates wh graduated after
More informationReinforcement Learning" CMPSCI 383 Nov 29, 2011!
Reinfrcement Learning" CMPSCI 383 Nv 29, 2011! 1 Tdayʼs lecture" Review f Chapter 17: Making Cmple Decisins! Sequential decisin prblems! The mtivatin and advantages f reinfrcement learning.! Passive learning!
More informationComparing Several Means: ANOVA. Group Means and Grand Mean
STAT 511 ANOVA and Regressin 1 Cmparing Several Means: ANOVA Slide 1 Blue Lake snap beans were grwn in 12 pen-tp chambers which are subject t 4 treatments 3 each with O 3 and SO 2 present/absent. The ttal
More informationFall 2013 Physics 172 Recitation 3 Momentum and Springs
Fall 03 Physics 7 Recitatin 3 Mmentum and Springs Purpse: The purpse f this recitatin is t give yu experience wrking with mmentum and the mmentum update frmula. Readings: Chapter.3-.5 Learning Objectives:.3.
More informationWhy Don t They Get It??
Why Dn t They Get It?? A 60-minute Webinar NEURO LINGUISTIC PROGRAMMING NLP is the way we stre and prcess infrmatin in ur brains, and then frm the wrds we use t cmmunicate. By learning abut NLP, yu can
More informationCHM112 Lab Graphing with Excel Grading Rubric
Name CHM112 Lab Graphing with Excel Grading Rubric Criteria Pints pssible Pints earned Graphs crrectly pltted and adhere t all guidelines (including descriptive title, prperly frmatted axes, trendline
More informationClick to edit Master title style
Impact Evaluation Technical Track Session IV Click to edit Master title style Instrumental Variables Christel Vermeersch Amman, Jordan March 8-12, 2009 Click to edit Master subtitle style Human Development
More informationExam #1. A. Answer any 1 of the following 2 questions. CEE 371 October 8, Please grade the following questions: 1 or 2
CEE 371 Octber 8, 2009 Exam #1 Clsed Bk, ne sheet f ntes allwed Please answer ne questin frm the first tw, ne frm the secnd tw and ne frm the last three. The ttal ptential number f pints is 100. Shw all
More informationMath Foundations 20 Work Plan
Math Fundatins 20 Wrk Plan Units / Tpics 20.8 Demnstrate understanding f systems f linear inequalities in tw variables. Time Frame December 1-3 weeks 6-10 Majr Learning Indicatrs Identify situatins relevant
More informationActivity Guide Loops and Random Numbers
Unit 3 Lessn 7 Name(s) Perid Date Activity Guide Lps and Randm Numbers CS Cntent Lps are a relatively straightfrward idea in prgramming - yu want a certain chunk f cde t run repeatedly - but it takes a
More informationPipetting 101 Developed by BSU CityLab
Discver the Micrbes Within: The Wlbachia Prject Pipetting 101 Develped by BSU CityLab Clr Cmparisns Pipetting Exercise #1 STUDENT OBJECTIVES Students will be able t: Chse the crrect size micrpipette fr
More informationThe standards are taught in the following sequence.
B L U E V A L L E Y D I S T R I C T C U R R I C U L U M MATHEMATICS Third Grade In grade 3, instructinal time shuld fcus n fur critical areas: (1) develping understanding f multiplicatin and divisin and
More informationCausal inference using regression on the treatment variable
CHAPTER 9 Causal inference using regressin n the treatment variable 9.1 Causal inference and predictive cmparisns S far, we have been interpreting regressins predictively: given the values f several inputs,
More informationPlease Stop Laughing at Me and Pay it Forward Final Writing Assignment
Kirk Please Stp Laughing at Me and Pay it Frward Final Writing Assignment Our fcus fr the past few mnths has been n bullying and hw we treat ther peple. We ve played sme games, read sme articles, read
More informationthe results to larger systems due to prop'erties of the projection algorithm. First, the number of hidden nodes must
M.E. Aggune, M.J. Dambrg, M.A. El-Sharkawi, R.J. Marks II and L.E. Atlas, "Dynamic and static security assessment f pwer systems using artificial neural netwrks", Prceedings f the NSF Wrkshp n Applicatins
More informationLifting a Lion: Using Proportions
Overview Students will wrk in cperative grups t slve a real-wrd prblem by using the bk Hw D yu Lift a Lin? Using a ty lin and a lever, students will discver hw much wrk is needed t raise the ty lin. They
More informationAP Statistics Notes Unit Five: Randomness and Probability
AP Statistics Ntes Unit Five: Randmness and Prbability Syllabus Objectives: 3.1 The student will interpret prbability, including the lng-term relative frequency distributin. 3.2 The student will discuss
More information**DO NOT ONLY RELY ON THIS STUDY GUIDE!!!**
Tpics lists: UV-Vis Absrbance Spectrscpy Lab & ChemActivity 3-6 (nly thrugh 4) I. UV-Vis Absrbance Spectrscpy Lab Beer s law Relates cncentratin f a chemical species in a slutin and the absrbance f that
More informationNAME: Prof. Ruiz. 1. [5 points] What is the difference between simple random sampling and stratified random sampling?
CS4445 ata Mining and Kwledge iscery in atabases. B Term 2014 Exam 1 Nember 24, 2014 Prf. Carlina Ruiz epartment f Cmputer Science Wrcester Plytechnic Institute NAME: Prf. Ruiz Prblem I: Prblem II: Prblem
More informationLyapunov Stability Stability of Equilibrium Points
Lyapunv Stability Stability f Equilibrium Pints 1. Stability f Equilibrium Pints - Definitins In this sectin we cnsider n-th rder nnlinear time varying cntinuus time (C) systems f the frm x = f ( t, x),
More informationUN Committee of Experts on Environmental Accounting New York, June Peter Cosier Wentworth Group of Concerned Scientists.
UN Cmmittee f Experts n Envirnmental Accunting New Yrk, June 2011 Peter Csier Wentwrth Grup f Cncerned Scientists Speaking Ntes Peter Csier: Directr f the Wentwrth Grup Cncerned Scientists based in Sydney,
More informationChurn Prediction using Dynamic RFM-Augmented node2vec
Churn Predictin using Dynamic RFM-Augmented nde2vec Sandra Mitrvić, Jchen de Weerdt, Bart Baesens & Wilfried Lemahieu Department f Decisin Sciences and Infrmatin Management, KU Leuven 18 September 2017,
More informationMODULE FOUR. This module addresses functions. SC Academic Elementary Algebra Standards:
MODULE FOUR This mdule addresses functins SC Academic Standards: EA-3.1 Classify a relatinship as being either a functin r nt a functin when given data as a table, set f rdered pairs, r graph. EA-3.2 Use
More information