Simultaneous equation methods (econometrics) | Regression analysis
In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment. Intuitively, IVs are used when an explanatory variable of interest is correlated with the error term, in which case ordinary least squares and ANOVA give biased results. A valid instrument induces changes in the explanatory variable but has no independent effect on the dependent variable, allowing a researcher to uncover the causal effect of the explanatory variable on the dependent variable. Instrumental variable methods allow for consistent estimation when the explanatory variables (covariates) are correlated with the error terms in a regression model. Such correlation may occur when: 1. * changes in the dependent variable change the value of at least one of the covariates ("reverse" causation), 2. * there are omitted variables that affect both the dependent and independent variables, or 3. * the covariates are subject to non-random measurement error. Explanatory variables that suffer from one or more of these issues in the context of a regression are sometimes referred to as endogenous. In this situation, ordinary least squares produces biased and inconsistent estimates. However, if an instrument is available, consistent estimates may still be obtained. An instrument is a variable that does not itself belong in the explanatory equation but is correlated with the endogenous explanatory variables, conditionally on the value of other covariates. In linear models, there are two main requirements for using IVs: * The instrument must be correlated with the endogenous explanatory variables, conditionally on the other covariates. If this correlation is strong, then the instrument is said to have a strong first stage. A weak correlation may provide misleading inferences about parameter estimates and standard errors. * The instrument cannot be correlated with the error term in the explanatory equation, conditionally on the other covariates. In other words, the instrument cannot suffer from the same problem as the original predicting variable. If this condition is met, then the instrument is said to satisfy the exclusion restriction. (Wikipedia).
Introduction to Estimation Theory
http://AllSignalProcessing.com for more great signal-processing content: ad-free videos, concept/screenshot files, quizzes, MATLAB and data files. General notion of estimating a parameter and measures of estimation quality including bias, variance, and mean-squared error.
From playlist Estimation and Detection Theory
EstimatingRegressionCoefficients.1.EstimatingResidualVariance
This video is brought to you by the Quantitative Analysis Institute at Wellesley College. The material is best viewed as part of the online resources that organize the content and include questions for checking understanding: https://www.wellesley.edu/qai/onlineresources
From playlist Estimating Regression Coefficients
Maximum Likelihood Estimation Examples
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Three examples of applying the maximum likelihood criterion to find an estimator: 1) Mean and variance of an iid Gaussian, 2) Linear signal model in
From playlist Estimation and Detection Theory
Compare Linear and Exponential Functions
This video compares linear and exponential functions. http://mathispower4u.com
From playlist Introduction to Exponential Functions
Discrete & Continuous Random Variables (Full Length)
I define and compare the two types of Random Variables in AP Statistics...Discrete & Continuous. The formulas for finding the mean and standard deviation of a discrete random variables are introduced, and I also review the old mean and standard deviation formulas that the calculators does
From playlist AP Statistics
Uncertainty in Climate Change, with William Nordhaus
William Nordhaus, Yale University, gives a lecture during the YCEI conference, "Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists".
From playlist Uncertainty in Climate Change: A Conversation with Climate Scientists and Economists
Discrete & Continuous Variables Part 1
I define and compare the two types of Random Variables in AP Statistics...Discrete & Continuous. The formulas for finding the mean and standard deviation of a discrete random variables are introduced, and I also review the old mean and standard deviation formulas that the calculators does
From playlist AP Statistics
Statistical Rethinking Winter 2019 Lecture 18
Lecture 18 of the Dec 2018 through March 2019 edition of Statistical Rethinking: A Bayesian Course with R and Stan. Varying slopes, non-centered parameterization, instrumental variables, social relations model.
From playlist Statistical Rethinking Winter 2019
Lecture 8: Private and Social Returns to Education
MIT 14.771 Development Economics, Fall 2021 Instructor: Esther Duflo View the complete course: https://ocw.mit.edu/courses/14-771-development-economics-fall-2021 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61kvh3caDts2R6LmkYbmzaG Covers empirical evidence on the
From playlist MIT 14.771 Development Economics, Fall 2021
Visually Explained: Kalman Filters
A visual introduction to Kalman Filters and to the intuition behind them. ----------------------------------------------- Timestamps: 0:00 Intro 4:30 Kalman Filters 5:37 Prediction Step 7:14 Update Step ----------------------------------------------- Typos: - at 3:00. A car going a
From playlist Visually Explained
Data Assimilation: Interesting Past, Bright Future - Ghil - Workshop 2 - CEB T3 2019
Michael Ghil (ENS/UCLA, FR/USA) / 12.11.2019 Data Assimilation: Interesting Past, Bright Future ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/ Twitter : h
From playlist 2019 - T3 - The Mathematics of Climate and the Environment
Jean-Pierre Florens: Inverse problems in econometrics - Lecture 2/4
Recording during the thematic month on statistics - Week 2 : "Mathematical statistics and inverse problems" the 9 February, 2016 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide
From playlist Probability and Statistics
Reliability 1: External reliability and rater reliability and agreement
In this video, I discuss external reliability, inter- and intra-rater reliability, and rater agreement.
From playlist Reliability analysis
Statistical Rethinking Winter 2019 Lecture 07
Lecture 07 of the Dec 2018 through March 2019 edition of Statistical Rethinking: A Bayesian Course with R and Stan. This lecture covers the back-door criterion and introduction to Chapter 7, overfitting, cross-validation, and information criteria.
From playlist Statistical Rethinking Winter 2019
Excel Statistical Analysis 23: Discrete Probability Distributions: Terms and Definitions
Download Excel File: https://excelisfun.net/files/Ch05-ESA.xlsm PDF notes file: https://excelisfun.net/files/Ch05-ESA.pdf Learn about: Topics: 1. (00:00) Introduction. 2. (00:40) Define: Random Variable. 3. (01:40) Define: Discrete and Continuous Random Variables. 4. (03:55) Define: Probab
From playlist Excel Statistical Analysis for Business Class Playlist of Videos from excelisfun
Statistics 5_1 Confidence Intervals
In this lecture explain the meaning of a confidence interval and look at the equation to calculate it.
From playlist Medical Statistics
Causal inference with binary outcomes subject to both missingness and misclassification - Grace Yi
Virtual Workshop on Missing Data Challenges in Computation Statistics and Applications Topic: Causal inference with binary outcomes subject to both missingness and misclassification Speaker: Grace Yi Date: September 9, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
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Forelæsning med Per B. Brockhoff. Kapitler:
From playlist DTU: Introduction to Statistics | CosmoLearning.org
Dr Francois Lanusse - Exploring the Cosmos with Deep Learning
The main challenge of modern Cosmology is to answer pressing questions on the physical nature of dark matter and dark energy, which despite accounting for ~95% of the Universe today remain complete mysteries. This is what motivates a new generation of cosmological surveys which will map th
From playlist Turing Seminars