Econometrics

Source: Wikipedia. Pages: 116. Chapters: Linear regression, Daniel McFadden, Herfindahl index, Goodhart's law, Natural experiment, Official statistics, Confidence interval, Regression analysis, Neural network, Errors-in-variables models, Rubin causal model, Instrumental variable, Generalized method of moments, Structural equation modeling, Diversification, Independence of irrelevant alternatives, Vector autoregression, Heteroscedasticity, Common-cause and special-cause, Autoregressive conditional heteroskedasticity, Theil index, Simultaneous equations model, Unit root, Seemingly unrelated regressions, Cliodynamics, Poisson regression, Cliometrics, Difference in differences, Eric Ghysels, Loss function, Durbin-Watson statistic, Probit, Hedonic index, Survivorship bias, Taleb distribution, Heckman correction, Atkinson index, Delta method, Reference class forecasting, Measuring economic worth over time, Asymptotic theory, Tobit model, Propensity score matching, Endogeneity, Constant elasticity of substitution, Multinomial logit, Reduced form, Average treatment effect, Parameter identification problem, Cointegration, Econometric model, Hedonic regression, Trend stationary, Event study, Divisia monetary aggregates index, Latent variable, Panel data, Heteroscedasticity-consistent standard errors, Prais-Winsten transformation, General matrix notation of a VAR(p), Methodology of econometrics, Regression discontinuity design, Political forecasting, Local independence, Spectrum continuation analysis, Chow test, Bootstrapping, Mixed data sampling, Idempotent matrix, Market facilitation index, Genetic algorithms in economics, Centre for Structural Econometrics, National Longitudinal Surveys, Generalized entropy index, Panel analysis, Vuong's closeness test, Variance decomposition, Hausman test, Dynamic factor, The World Economy: Historical Statistics, Dummy variable, Multidimensional panel data, Log-linear modeling, Bayesian VAR, Limited dependent variable, BHHH algorithm, Taguchi loss function, Specification, Pollyanna Creep, Economic statistics, Cochrane-Orcutt estimation, Censored regression model, Observational equivalence, Econometric Society, Frisch-Waugh-Lovell theorem, Experimetrics, Box-Pierce test, Spatial econometrics, Review of Economics and Statistics, Gerschenkron effect, Bayesian econometrics, Sargan test, Financial econometrics, Epps effect, Method of simulated moments, Generalized Tobit, Observable variable, Multinomial probit, Econometric Reviews, Test for structural change. Excerpt: In statistics, linear regression is an approach to modeling the relationship between a scalar variable y and one or more variables denoted X. In linear regression, data are modeled using linear functions, and unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly, linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X, which is the domain of multivariate analysis. Linear regression was the first type of regression analysis to be studied rigorously, and to be used extensively ...