= Ordinary Least Squares = '''Ordinary Least Squares''' ('''OLS''') is a linear regression method. It minimizes root mean square errors. <> ---- == Univariate == Given one independent variable and one dependent (outcome) variable, the OLS model is specified as: {{attachment:model.svg}} It is estimated as: {{attachment:estimate.svg}} This model describes (1) the mean observation and (2) the marginal changes to the outcome per unit changes in the independent variable. The derivation can be seen [[Statistics/OrdinaryLeastSquares/Univariate|here]]. ---- == Multivariate == Given ''k'' independent variables, the OLS model is specified as: {{attachment:mmodel.svg}} It is estimated as: {{attachment:mestimate.svg}} More conventionally, this is estimated with [[LinearAlgebra|linear algebra]] as: {{attachment:matrix.svg}} The derivation can be seen [[Statistics/OrdinaryLeastSquares/Multivariate|here]]. ---- == Estimated Coefficients == The '''Gauss-Markov theorem''' demonstrates that (with some assumptions) the OLS estimations are the '''best linear unbiased estimators''' ('''BLUE''') for the regression coefficients. The assumptions are: 1. Linearity 2. [[Statistics/Exogeneity|Exogeneity]] 3. Random sampling 4. No perfect [[LinearAlgebra/Basis|multicolinearity]] 5. [[Statistics/Homoskedasticity|Homoskedasticity]] The variances for each coefficient are: {{attachment:homo1.svg}} Note that the standard deviation of the population's parameter is unknown, so it's estimated like: {{attachment:homo2.svg}} If the homoskedasticity assumption does not hold, then the estimators for each coefficient are actually: {{attachment:hetero1.svg}} Wherein, for example, ''r,,1j,,'' is the residual from regressing ''x,,1,,'' onto ''x,,2,,'', ... ''x,,k,,''. The variances for each coefficient can be estimated with the Eicker-White formula: {{attachment:hetero2.svg}} See [[https://www.youtube.com/@kuminoff|Nicolai Kuminoff's]] video lectures for the derivation of the robust estimators. ---- CategoryRicottone