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UVA - TCC - 315
Class Contacts Professor Matthew M. Mehalik Office Hours: W 2:00 5:00 pm TR 3:30 5:00 pm Office: Thornton A233 Email: mehalik@virginia.edu Phone: 982-2004Class Contacts Prof. Michael E. Gorman Office: Thornton A237 Email: meg3c@virginia.edu Pho
UVA - TCC - 315
B. Garrey & M. Gorman (4/18/02) HYPOTHETICAL CASE: AN ENGINEER MOVING FROM ONE COMPANY TO ANOTHER1Innovative Solutions is the market leader in the field of medical adhesives. Followfast, Inc. has only a small percentage of the medical adhesive ma
UVA - TCC - 315
Biz Plan Basics: Purposes organize and develop thinking what are the pieces, and how do the connect? strategic planning fundraising management/strategic partner development A story and a roadmap Avoiding "Ready, Fire, Aim" Business Pl
UVA - TCC - 315
B. GARREY TIPS ON HOW TO CHANGE JOBS WITHOUT GETTING SUED 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. Be open and honest. Do not take any company property. Return all company property, including materials at issue. Do not recruit from your former employer. Don't
UVA - TCC - 315
B. GARREYEXCERPT FROM PROPRIETARY RIGHTS AND CONFIDENTIALITY AGREEMENT FOR XYZ CORP. DEFINITIONS: "Confidential Information" means all business information, technological information, intellectual property, trade secrets and other information belon
UVA - TCC - 315
B. GARREY & M . GORMAN HYPOTHETICAL CASE: GROUP OF ENGINEERS MOVING FROM ONE COMPANY TO ANOTHER1You are an engineer for VATECH Solutions. You have worked there for ten years. You have just been offered a job by Wahoo Enterprises. Wahoo Enterprise
UVA - TCC - 315
B. GarreySUGGESTED INSERT TO ANY EMPLOYEE AGREEMENT CONTAINING POST EMPLOYMENT RESTRICTIONSIf I am unable to obtain employment consistent with my abilities and education, within one month after termination of my employment with ABC, solely becaus
UVA - TCC - 315
Please contact me if you need any more info or contacts. atulk@virginia.edu -Atul Khosla India's Biggest Problems 1. Lack of clean water and sanitation 2. Poor infrastructure for public health services and high cost of ordinary medicine. No education
UVA - STS - 401
UNDERGRADUATE THESIS PROJECT PRE-PROPOSAL School of Engineering and Applied Science University of VirginiaA Novel Boolean Satisfiability (SAT) Solver: Design and Implementation of an Efficient SAT-Solver Via Distributed ComputingSubmitted by Erwi
UVA - TCC - 402
CONTENTS OF THE FINAL BINDERInsert this sheet unbound in the very front of the final binder. The items should be ordered as they are listed below. Individual documents should have page numbers, but there is no need to merge the documents so that the
Idaho - ECE - 562
Chapter Four (Datta) 4.1 Basis functions as a computational tool We have already seen that I can write any wavefunction in terms of basis functions ( r ) = mum ( r )m =1 N(4.1.1)Two sets of basis functions that we have used extensively are: a)
UVA - TCC - 315
BusinessPlansSTS315/Psych418 LarryG.Richards April13,2006LarryGRichards1Creatingasuccessful businessplanChapter5ofThePortableMBAinEntrepreneurship,DavidE. GumpertTheSuccessfulBusinessPlan: SecretsandStrategies,Rhonda Adams LarryGRichar
UVA - TCC - 315
BlueTrakClement Song - Shan Wu-University of VirginiaAnimal TrackingAnimal tracking is useful in many areas Protecting endangered species Migration patterns Habitats LifestyleUses of Animal Tracking Habitatanalysis of animals Study
UVA - TCC - 315
The Invention of the Telephone: Our Group's ProcessesGroup 3:Tristan Becker, Anna Capetanakis, Cobey Potter, Markus WeisnerTCC 315: Invention and DesignLife Cycle:Group 3:Tristan Becker, Anna Capetanakis, Cobey Potter, Markus WeisnerTCC 315
UVA - TCC - 315
Group 1 ProcessesRemember Me?Approaching the Problem.- Brainstorming Carbon, Photophone, Liquid, MagnetoCarbon TransmitterParallel Lead SeriesCollected Materials First draft of caveat Constructed Prototype Tested with a light bulbCrushed
UVA - TCC - 315
Gr oup #5s Reflect ion on t he I nvent ion Pr ocessThomas, L eslie, Tim & EvanChoice of DesignI nit ial Pat hNew (Secondar y ) Pat hDivision of L abor Done accor ding t o our st r engt hs Tim, Evan, Thomas mor e t echnical L eslie did t h
UVA - TCC - 315
Group 2 The Cool GroupMike Andrews Brett Dickey Richard Gunderson Aprotim Sanyal Kevin WuProcess[insert chart]BrainstormGenerated ideas: Photophone Capacative phone MagnetophoneInvestigate and Research Splitup into two teams Investi
UVA - TCC - 315
B. GARREY & M . GORMAN HYPOTHETICAL CASE: GROUP OF ENGINEERS MOVING FROM ONE COMPANY TO ANOTHER1You are an engineer for VATECH Solutions. You have worked there for ten years. You have just been offered a job by Wahoo Enterprises. Wahoo Enterprise
UVA - TCC - 315
B. Garrey & M. Gorman (4/18/02) HYPOTHETICAL CASE: AN ENGINEER MOVING FROM ONE COMPANY TO ANOTHER1Innovative Solutions is the market leader in the field of medical adhesives. Followfast, Inc. has only a small percentage of the medical adhesive ma
UVA - TCC - 315
Improve Bell's TelephoneResearched Various DesignsProposed ImprovementsCapacitance PhonePhotophone w/ direct obstructionResearched physics behind conceptResearched feasibility of direct obstruction conceptParallel Plate capacitor: dista
UVA - STS - 401
UNDERGRADUATE THESIS PROJECT PROPOSAL School of Engineering and Applied Science University of VirginiaThe Design and Implementation of a Boolean Satisfiability Solver Driven By Distributed Computing Submitted by Erwin P. Gianchandani Computer Scien
UVA - CS - 451
Faces:Face 0-> Pointer 0x152010 -> Edge 0x1a0210Face 1-> Pointer 0x152020 -> Edge 0x1a0270Face 2-> Pointer 0x152030 -> Edge 0x1a02d0Face 3-> Pointer 0x152040 -> Edge 0x1a0330Face 4-> Pointer 0x15
Iowa State - ECON - 674
Econ 674 Spring 2009 Professor Bunzel Due date: Beginning of class January 29.Problemset 11. For each of the following verify that the solution in fact satises the dierence equation. The symbols c; c0 , and a0 denote constants: (Hint, to verify yo
Iowa State - ECON - 674
Lecture 25 Unit Root Tests III The Ng-Perron Unit Root Test (Improved Finite Sample Performance)The ADF and PP unit root tests are known (from MC simulations) to suffer potentially severe finite sample power and size problems. 1. Power The ADF and
Iowa State - ECON - 674
Lecture 9The Generalized Method of Moments: III Review: GMM for the Linear Regression Model yt = xt' + t , t = 1,.,T where xt and are kx1, T > k. Or, in more compact form, y = X + , E() = 0 and E(') = . Some or even all of elements of xt can be corr
Iowa State - ECON - 674
Lecture 18 Testing for ARCH Effects in a Time Series Engle's LM Test: Consider a stationary time series, xt. Assume that if xt is conditionally heteroskedastic, then it has an ARCH(m) form, i.e., xt = ht1/2vt , vt ~ i.i.d. N(0,1) ht = + 1xt-12 + .
Iowa State - ECON - 674
Lecture 23 The Dickey-Fuller Test We have seen that the dynamic behavior of I(1) processes is quite different from the behavior of I(0) processes the way we go about defining and estimating the trend and cyclical components of a time series may de
Iowa State - ECON - 674
Lecture 29 Cointegration IV Johansen's MLE of the CI SpaceAssume that yt is an n-dimensional I(1) process with VEC form: yt = C1yt-1 +.+ Cp-1yt-p+1 + C0yt-1 + t where t ~ w.n. () C0 = -BA', A is an nxh matrix, h < n, which spans the CI space of y (
Iowa State - ECON - 674
Lecture 8 The Generalized Method of Moments: II Review: GMM for the Linear Regression Model yt = xt' + t , t = 1,.,T where xt and are kx1, T > k. Or, in more compact form, y = X + , E() = 0 and E(') = . Some or even all of elements of xt can be corr
Iowa State - ECON - 674
Lecture 22 Spurious and Cointegrating Regressions The time series regression theory and application developed in Econ 672 and 674 have assumed that the time series we are working with are stationary (or trend stationary). If we are working with diff
Iowa State - ECON - 674
Lecture 30 Inference in the VECMRecall the VEC representation of a ndimensional cointegrated system with cointegrating rank h yt = D + C1yt-1 +.+ Cp-1yt-p+1 + C0yt-1 + t where t ~ w.n. () C0 = -BA', A is an nxh matrix, h < n, which spans the CI sp
Iowa State - ECON - 674
Lecture 17 Regression Models with ARCH(m) Errors: Estimation and Inference Assume yt = xt + t where xt is a a kx1 vector of weakly exogenous regressors (which allows for lagged dependent variables and includes the AR(p) model as a special case) t
Iowa State - ECON - 674
Lecture 11 GMM in the General CaseAssume that an economic or statistical model implies the m moment conditions: E(gt() = 0 for all t where is an unknown kx1 parameter vector, and gt(.) = [g1t(.) . gmt(.)]',m > k. Examples: Linear and nonlinear re
Iowa State - ECON - 674
Lecture 10 Estimating Nonlinear Regression ModelsReferences: Greene, Econometric Analysis, Chapter 10Consider the following regression model: yt = f(xt, ) + t t = 1,.,T xt is kx1 for each t, is an rx1constant vector, t is an unobservable error p
Iowa State - ECON - 674
Lecture 24 Unit Root Tests, II The initial DF unit root tests assumed that under the unit root null hypothesis, the first differences in the series are serially uncorrelated. Since first differences of most macroeconomic time series are serially cor
Iowa State - ECON - 674
Bootstrap and SubsamplingHelle BunzelISUFebruary 24, 2009Helle Bunzel (ISU)Bootstrap and SubsamplingFebruary 24, 20091 / 21The Setup IWe have a population of observations: f(yi , xi )gn=1 , where xi is i potentially a vector. For now w
Iowa State - ECON - 674
Economics 674 Macroeconometrics Spring 2009 Course Syllabus Instructor: Helle Bunzel 373 Heady Hall 294-6163 hbunzel@iastate.edu Office Hours: T,R 11:00-12:00, F 10-11 and by appointment Textbooks: There is not a required textbook for the class. Howe
Iowa State - ECON - 674
Lecture 13 Threshold Autoregressions: IReferences Enders, Applied Economic Time Series, Chapter 7 B. Hansen, Inference in TAR models, Studies in Nonlinear Dynamics and Econometrics, 1997. B. Hansen, Testing for Linearity, Journal of Economics
Iowa State - ECON - 674
Econ 674 Spring 2009 Professor Bunzel Due date: Beginning of class March 3.Problemset 21. Find the Yule-Walker equations for the AR(2) process Xt = and show thatk1 Xt 3 2 312 + Xt 9 5 212+ "tjkj=16 21jkj+1 32. This problem
Iowa State - ECON - 674
IntroductionDierence EquationsA look backDierence Equations IIThe Lag OperatorDierence Equations IIIIntroduction to Time Series AnalysisHelle BunzelISUJanuary 30, 2009Helle Bunzel Introduction to Time Series AnalysisISUIntroduct
Iowa State - ECON - 674
The simple random walk does not have a tendency to increase or decrease over time since its changes are serially uncorrelated and have zero mean. Starting from an initial value y0, yt = y0 + (1 + . + t) E(yt) = E(y0) E(ytyt-1,yt-2,.) = yt-1 (a rw is
Iowa State - ECON - 674
Lecture 12 Testing for Structural Breaks I. Known Breakpoint (Chow Tests) II. Unknown BreakpointI. Testing for Structural Change of Known TimingA. Linear Regression Models Chow, Econometrica, 1960 B. Nonlinear Econometric Models (including, but n
Iowa State - ECON - 674
Lecture 11 GMM in the General CaseAssume that an economic or statistical model implies the m moment conditions: E(gt() = 0 for all t where is an unknown kx1 parameter vector, and gt(.) = [g1t(.) . gmt(.)]',m > k. Examples: Linear and nonlinear r
Iowa State - ECON - 674
Characterizing Forecast Uncertainty Prediction Intervals The estimated AR (and VAR) models ^ generate point forecasts of yt+s , y t + s ,t . Under our assumptions the point forecasts are asymtotically unbiased but biased in finite samples. (What doe
Iowa State - ECON - 674
Lecture 10 Estimating Nonlinear Regression ModelsReferences: Greene, Econometric Analysis, Chapter 10Consider the following regression model: yt = f(xt, ) + t t = 1,T xt is kx1 for each t, is an rx1constant vector, t is an unobservable error pro
Iowa State - ECON - 674
Linear Time Series Models A (discrete) time series [a (discrete) stochastic process] is a sequence of random numbers (or vectors) indexed by the integers: y0,y1,. { yt } 0 y1,y2,. { yt }1 .,y-1,y0,y1,. { yt } - The objective of time series analysis
Iowa State - ECON - 674
AR and VAR Model Specification Issues 1. How should you select which variables to include in the VAR? For example, if you want to use the VAR to jointly forecast inflation and the output gap then clearly you would need to include those two variables
Iowa State - ECON - 674
Economics 674 Fall 2006 Project 1 Clarida, Gertler, and Gali (2000, QJE) formulate a model of the federal funds rate in period t, rt, which can be written in the form (*) rt = 0 + 1 E t + k t + 2 E xt + q t + (L)rt-1 + t()()where t is th
Iowa State - ECON - 674
Lecture 15 Threshold Autoregressions: III Inference on the threshold parameters, Testing for TAR effects and # of regimesAs noted in the previous lecture, standard inference procedures regarding the intercept and slope parameters in the TAR mod
Iowa State - ECON - 674
Lecture 27 Cointegration:II Testing for Cointegration There are two basic approaches that are commonly to test for cointegration. Residual Based Tests H0: no CI vs. HA: CI Use single-equation regression residuals Engle-Granger; Phillips-Ouliaris L
Iowa State - ECON - 674
Economics 674 Fall 2006 Project 3 (Due: Friday, December 1) Let t denote the inflation rate in period t. In the following problems, use the same inflation rate data you used in Projects 1 and 2 and the sample period 1960:I-2006:I. Report all pertinen
Iowa State - ECON - 674
Economics 674 Fall 2006 Project 2 Fit the following version of the Clarida, Gertler, Gali model by GMM rt = 0 + 1t+1 + 2xt+1 + 3rt-1 + 4rt-2 + t where rt is the federal funds rate in period t, t is the inflation rate in period t, xt is the output ga
Iowa State - ECON - 674
Lecture 21 The Hodrick-Prescott Filter Hodrick and Prescott, JMCB, 1997In contrast to the trend-cycle decompositions we have talked about so far, the Hodrick-Prescott filter is a model-free based approach to decomposing a time tseries into its tren
Iowa State - ECON - 674
Lecture 30 Inference in the VECMRecall the VEC representation of a ndimensional cointegrated system with cointegrating rank h yt = D + C1yt-1 +.+ Cp-1yt-p+1 + C0yt-1 + t where t ~ w.n. () C0 = -BA', A is an nxh matrix, h < n, which spans the CI sp
Iowa State - ECON - 674
Lecture 32 Structural VARs: II Consider the following VAR of y1t, the growth rate of real GDP, and y2t, the inflation rate: yt = A0 + A1yt-1 + t where t ~ w.n.(). We assume that t = Cvt where vt = [v1t v2t]' is a serially and contemporaneously uncor
Iowa State - ECON - 674
Lecture 29 Cointegration IV Johansen's MLE of the CI SpaceAssume that yt is an n-dimensional I(1) process with VEC form: yt = C1yt-1 +.+ Cp-1yt-p+1 + C0yt-1 + t where t ~ w.n. () C0 = -BA', A is an nxh matrix, h < n, which spans the CI space of y (
Iowa State - ECON - 207
ECONOMICS 207 FALL 207 LABORATORY EXERCISE 9 1 Problem 1: A = 4 2 3 C11 a. Form the matrix 4 C21 C31 2 2 2 5 4 C12 C22 C32 3 1 2 5 23 C13 C23 5 C33b. jAjc. A11d. Suppose we have the following set of equations: 32 3 2 3 2 1 2 1 x1 3 4 2 5
Iowa State - CRP - 274
An Introduction to Economic Impact AssessmentDave Swenson Regional Scientist Department of Economics Iowa State University February, 2002Regional analysts are often asked to assess local economic conditions. As regional and national economies chan
Iowa State - ECON - 207
ECONOMICS 207 SPRING 2009 LABORATORY EXERCISE 2 20th January 2009 Problem 1: Do the following long divisions: a.208 13b.15414c.1421351d. 687273Problem 1: Simplify, add, subtract, multiply or divide the following fractions. Express the
Iowa State - ECON - 486
Econ 486XReading List and Course Outline Science and Technology and Economic GrowthHuffmanFall 2003I. Introduction 1. Leshner, A. I. "Public Engagement with Science," Science 299(Feb 14, 2003): 97. 2. Jones, C.I. "The Facts of Economic Growth,