# Generalized least squares heteroskedasticity

The steps for obtaining a feasible **generalized** **least** **squares** estimator for are: 1. Estimate (8.25) by **least** **squares** and compute the **squares** of the **least** **squares** residuals . 2. Estimate by applying **least** **squares** to the equation β 12,, ,ββK K ˆ2 e i αα α 12,,,K S 2 122 ln ˆ ez zv ii S=α+α + +α +L iSi. if engagement rings.

an object inheriting from class "gls", representing a **generalized** **least** **squares** fitted linear model. model. a two-sided linear formula object describing the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right. model. Changes to the model - see update.formula for details. Σ or estimate Σ empirically. An example of the former is Weighted **Least Squares** Estimation and an example of the later is Feasible GLS (FGLS). Weighted **Least Squares** Estimation (WLS) Consider a **general** case of **heteroskedasticity**. Var(ui) = σi σωi 2= 2. Then, = Ω Ω =. You can find info on Autocorrelation and **Generalized** **Least** **Squares** Regression with Excel at: Introductory Econometrics Chapter 20: Autocorrelation ... there will still be **heteroskedasticity** present in the data. I'm trying to fit an Amemiya GLS line to find the slope of the data points along the way to see when it chages. S. STATISTICS New.

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to. In statistics, **generalized** **least** **squares** (GLS) is a technique for estimating the unknown parameters in a linear regression model. The GLS is applied when the variances of the observations are unequal ( heteroscedasticity ), or when there is a certain degree of correlation between the observations.

**Generalized least squares** estimator (GLS) that directly takes into account **heteroskedasticity** (as well as cross-sectional and serial correlations in the estimation) – GLS estimator regarded as. The estimation procedure is usually called as weighted **least squares**. Let W 1 then the weighted **least squares** estimator of is obtained by solving normal equation (' ) 'X WX X W ˆ y which gives ˆ (' ) 'X WX X W 1 y. Sep 23, 2020 · **Generalized Least Squares** (GLS) is a large topic. This article serves as a short introduction meant to “set the. · Remedy 2: **Generalized Least Square** (GLS) and Feasible GLS (FGLS) Instead of accepting an inefficient OLS estimator and correcting the standard errors, we can correct **Heteroskedasticity** or Autocorrelation by using a fully efficient estimator (i.e., unbiased and with the **least** variance) using **Generalized Least Square** (GLS).

Enter the email address you signed up with and we'll email you a reset link. 1 day ago · Since we are using an estimate of ρ, the approach used is known as the feasible **generalized** **least** **squares** (FGLS) or estimated **generalized** **least** **squares** (EGLS). Using the Durbin-Watson coefficient. The sample autocorrelation coefficient r is the correlation between the sample estimates of the residuals e 1, e 2, , e n-1 and e 2, e 3, , e n. 2020. sulting regression model may have heteroscedasticity and/or correction problems and thus, weighted **least** **squares** estimation or alternative estimation methods should be used. In this study, we consider **generalized** **least** **squares** and weighted **least** **squares** estimation methods, based on an easily calculated approximation of the covariance.

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**Generalized** **Least** **Squares** **Generalized** **Least** **Squares**. Show Source; Quantile regression; Recursive **least** **squares**; Example 2: Quantity theory of money ... Let's assume that the data is heteroskedastic and that we know the nature of the **heteroskedasticity**. We can then define sigma and use it to give us a GLS model. First we will obtain the. When heteroscedasticity is detected by any appropriate statistical test, then appropriate. Σ or estimate Σ empirically. An example of the former is Weighted **Least** **Squares** Estimation and an example of the later is Feasible GLS (FGLS). Weighted **Least** **Squares** Estimation (WLS) Consider a general case of **heteroskedasticity**. Var (ui) = σi σωi 2= 2.

2022. 6. 10. · 5.2. Feasible **Generalized Least Squares** . Without A4, how can we know which unbiased estimator is the most efficient? then w w (full-rank matrix) is the Cholesky decomposition of Ω−1 Ω − 1 (full-rank matrix) In other words, w w is the **squared** root of Ω Ω ( **squared** root version in matrix) Then, the transformed equation (IGLS) will have the. 2017. . 2020. 11. 26. · **Generalized Least Squares Generalized Least Squares** Consider the linear regression model y = Xb+u, E(uu>) = W, (1) where W is a positive deﬁnite N N matrix which may depend on X. Suppose we know W, or at **least** can estimate it consistently. To obtain an efﬁcient estimator of b, we transform the model so that it satisﬁes the conditions of the Gauss-Markov..

LECTURE 11:

GENERALIZED LEAST SQUARES(GLS) In this lecture, we will consider the model y = Xβ+ εretaining the assumption Ey = Xβ. However, we no longer have the assumption V(y) = V(ε) = σ2I. Instead we add the assumption V(y) = V where V is positive definite. Sometimes we take V = σ2Ωwith tr Ω= N As we know, = (X′X)-1X′y. What is E ?.

Σ or estimate Σ empirically. An example of the former is Weighted **Least Squares** Estimation and an example of the later is Feasible GLS (FGLS). Weighted **Least Squares** Estimation (WLS) Consider a **general** case of **heteroskedasticity**. Var(ui) = σi σωi 2= 2. Then, = Ω Ω =.

(Estimated) **Generalized** **Least** **Squares** Regression Model for **Heteroskedasticity** Background on GLS The GLS model is a generalization of OLS regression, which relaxes the assumption that the errors are homoskedastic and uncorrelated.

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In this article I discuss **Heteroskedasticity** in ordinary **least squares** (OLS), starting from **general** explanation followed by a few tests of.

I don't know how you performed your search, but there are a number of SAS procedures which employ **generalized** **least** **squares** estimation methods. Since you don't tell us anything about the problem other than that you want some way to deal with **heteroskedasticity**, it is a bit difficult to advise on what methods to employ.

amlogic roadmap 2022. **Generalized** **least** **squares** estimator (GLS) that directly takes into account **heteroskedasticity** (as well as cross-sectional and serial correlations in the estimation) - GLS estimator regarded as more. Sep 23, 2020 · **Generalized** **Least** **Squares** (GLS) is a large topic. This article serves as a short introduction meant to "set the scene" for GLS mathematically.

**Generalized Least Squares**. **Generalized least squares** (GLS) is a method for fitting coefficients of explanatory variables that help to predict the outcomes of a dependent random variable. As its name suggests, GLS includes ordinary **least squares** (OLS) as a special case. GLS is also called “ Aitken ’ s estimator, ” after A. C. Aitken (1935). LECTURE 11: **GENERALIZED** **LEAST** **SQUARES** (GLS) In this lecture, we will consider the model y = Xβ+ εretaining the assumption Ey = Xβ. However, we no longer have the assumption V (y) = V (ε) = σ2I. Instead we add the assumption V (y) = V where V is positive definite. Sometimes we take V = σ2Ωwith tr Ω= N As we know, = (X′X)-1X′y. What is E ?. Learn how to eliminate the problem of **heteroscedasticity** by the method of **generalized least squares** . jbl j216a manual; oc71 pinout; psp homebrew emulators; pathfinder 2e golarion; why does he stare at me so intensely without smiling; wheel bushing; kimber 84m extended magazine; tech house music 2021. **Generalized** **least** **squares** regression or weighted **least** **squares** regression in the presence of **heteroskedasticity**. On the other hand, if you are interested in determining how the quantiles of the conditional distribution of the dependent variable vary with the independent variables, then you would address that via quantile regression.

20 hours ago · Multiple regression use a weighted **least** **squares** procedure to find the slope & y intercept of the best line through the following data 3 This report describes the results obtained by applying weighted multiple linear regression to estimate the parameter connected with an additive-by-additive epistatic interaction Linear regression can create a predictive model on. Learn how to eliminate the problem of **heteroscedasticity** by the method of **generalized least squares** . jbl j216a manual; oc71 pinout; psp homebrew emulators; pathfinder 2e golarion; why does he stare at me so intensely without smiling; wheel bushing; kimber 84m extended magazine; tech house music 2021. 1 day ago · Since we are using an estimate of ρ, the approach used is known as the feasible **generalized least squares** (FGLS) or estimated **generalized least squares** (EGLS). Using the Durbin-Watson coefficient. The sample autocorrelation coefficient r is the correlation between the sample estimates of the residuals e 1, e 2, , e n-1 and e 2, e 3, , e n. 2022. 2019. 12. 31. · Analisis Masalah Heteroskedastisitas Menggunakan **Generalized** **Least** **Square** dalam ... This study aims to examine the use of the GLS method in overcoming heteroscedasticity in regression analysis.

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**Generalized** **Least** **Squares** (GLS) is a large topic. This article serves as a short introduction meant to "set the scene" for GLS mathematically. There's plenty more to be covered, including (but not limited to): A more in-depth dive into WLS. Methods and approaches for specifying covariance matrix. 22. · **Generalized least squares** regression or weighted **least squares** regression in the presence of **heteroskedasticity** . On the other hand, if you are interested in determining how the quantiles of the conditional distribution of the dependent variable vary with the independent variables, then you would address that via quantile regression. If the form of the **heteroskedasticity** is known, it can be corrected (via appropriate transformation of the data) and the resulting estimator, **generalized least squares** (GLS), can be shown to be BLUE. This chapter is devoted to explaining these points. **Heteroskedasticity** can best be understood visually.. 2017. 5. 10.

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I don't know how you performed your search, but there are a number of SAS procedures which employ **generalized least squares** estimation methods. Since you don't tell us anything about the problem other than that you want some way to deal with **heteroskedasticity**, it is a bit difficult to advise on what methods to employ. Template:Multiple issues. Template:Regression bar In statistics, **generalized** **least** **squares** (GLS) is a technique for estimating the unknown parameters in a linear regression model. The GLS is applied when the variances of the observations are unequal (heteroscedasticity), or when there is a certain degree of correlation between the observations. In these cases ordinary **least** **squares** can be.

Conclusion. Overall, the weighted ordinary **least** **squares** is a popular method of solving the problem of heteroscedasticity in regression models, which is the application of the more general concept of **generalized** **least** **squares**. WLS implementation in R is quite simple because it has a distinct argument for weights.

11. 26. · **Generalized Least Squares Generalized Least Squares** Consider the linear regression model y = Xb+u, E(uu>) = W, (1) where W is a positive deﬁnite N N matrix which may depend on X. Suppose we know W, or at **least** can estimate it consistently. To obtain an efﬁcient estimator of b, we transform the model so that it satisﬁes the. 22. · **Generalized least squares** regression or weighted **least squares** regression in the presence of **heteroskedasticity** . On the other hand, if you are interested in determining how the quantiles of the conditional distribution of the dependent variable vary with the independent variables, then you would address that via quantile regression.

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**Heteroscedasticity** is a problem because ordinary **least squares** (OLS) regression assumes that the residuals come from a population that has homoscedasticity, which means constant variance. When **heteroscedasticity** is present in a regression analysis, the results of the analysis become hard to trust. Specifically, **heteroscedasticity** increases the.

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2019. 6. 7. · Corrections for **heteroscedasticity** : We can use different specification for the model. Weighted **Least Squares** method is one of the common statistical method. This is the generalization of ordinary **least square** and linear regression in which the errors co-variance matrix is allowed to be different from an identity matrix. **Generalized least squares** estimator (GLS) that directly takes into account **heteroskedasticity** (as well as cross-sectional and serial correlations in the estimation) – GLS estimator regarded as.

(Estimated) **Generalized** **Least** **Squares** Regression Model for **Heteroskedasticity** Background on GLS The GLS model is a generalization of OLS regression, which relaxes the assumption that the errors are homoskedastic and uncorrelated. In the presence of heteroskedastic errors, regression using Feasible **Generalized** **Least** **Squares** (FGLS) offers potential efficiency gains over Ordinary **Least** **Squares** (OLS). However, FGLS adoption remains limited, in part because the form of **heteroskedasticity** may be misspecified. We investigate machine learning methods to address this concern, focusing on Support Vector Regression. Monte Carlo.

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In statistics, **generalized** **least** **squares** (GLS) is a technique for estimating the unknown parameters in a linear regression model. The GLS is applied when the variances of the observations are unequal ( heteroscedasticity ), or when there is a certain degree of correlation between the observations. In these cases ordinary **least** **squares** can be. This is known as **Generalized** **Least** **Squares** (GLS), and for a known innovations covariance matrix, ... As is typical with econometric models, the innovations include some degree of both heteroscedasticity and autocorrelation. The goal of a regression analysis is to recover the coefficients as accurately as possible from the simulated data. (Estimated) **Generalized Least Squares** Regression Model for **Heteroskedasticity** Background on GLS The GLS model is a generalization of OLS regression, which relaxes the assumption that the errors are homoskedastic and uncorrelated.

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Σ or estimate Σ empirically. An example of the former is Weighted **Least** **Squares** Estimation and an example of the later is Feasible GLS (FGLS). Weighted **Least** **Squares** Estimation (WLS) Consider a general case of **heteroskedasticity**. Var(ui) = σi σωi 2= 2. Then, = Ω Ω =. 2007. 5. 10. · LECTURE 11: **GENERALIZED** **LEAST** **SQUARES** (GLS) In this lecture, we will consider the model y = Xβ+ εretaining the assumption Ey = Xβ. However, we no longer have the assumption V(y) = V(ε) = σ2I. Instead we add the assumption V(y) = V where V is positive definite. Sometimes we take V = σ2Ωwith tr Ω= N As we know, = (X′X)-1X′y.

. **Generalized** **Least** **Squares** (GLS) is a large topic. This article serves as a short introduction meant to "set the scene" for GLS mathematically. There's plenty more to be covered, including (but not limited to): A more in-depth dive into WLS. Methods and approaches for specifying covariance matrix. 4.2. **Generalized** **least** **squares**. To obtain the **generalized** **least** **squares** estimates in equation (8.27) on page 207 of the text we can use EViews weighted **least** **squares** option, with weighting series σ-1 = 1/SIGHAT. The Equation specification and LS & TSLS options are given by. These selections yield the following output. 5.

estimate ordinary **least** **squares** (OLS) followed by robust (to conditional **heteroskedasticity** and arbitrary within cluster correlation) variance, or she can make all the additional relevant restrictive assumptions which make the GLS estimator best linear unbiased (BLUE) and she can proceed with GLS estimation with non-robust variance. **GENERALIZED** **LEAST** **SQUARES** ESTIMATION OF ARMA MODELS. L. KAVALIERIS. University of Otago, Australian National University and Nigerian Educational Research and Development Council. Search for more papers by this author.

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In this article I discuss **Heteroskedasticity** in ordinary **least squares** (OLS), starting from **general** explanation followed by a few tests of. 1 day ago · Since we are using an estimate of ρ, the approach used is known as the feasible **generalized least squares** (FGLS) or estimated **generalized least squares** (EGLS). Using the Durbin-Watson coefficient. The sample autocorrelation coefficient r is the correlation between the sample estimates of the residuals e 1, e 2, , e n-1 and e 2, e 3, , e n. 2022.

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Heteroskedastic Regression. When a linear regression presents evidence of **heteroskedasticity**, the usual approach in **least**-**squares** is to use a **generalized least**-**squares** (GLS) estimator in place of the usual OLS. The virtue of GLS is that we do not need to know anything about why the data are heteroskedastic in order to estimate the model by GLS. 1 day ago · Since we are using an estimate of ρ, the approach used is known as the feasible **generalized least squares** (FGLS) or estimated **generalized least squares** (EGLS). Using the Durbin-Watson coefficient. The sample autocorrelation coefficient r is the correlation between the sample estimates of the residuals e 1, e 2, , e n-1 and e 2, e 3, , e n. 2022.

1 day ago · Since we are using an estimate of ρ, the approach used is known as the feasible **generalized least squares** (FGLS) or estimated **generalized least squares** (EGLS). Using the Durbin-Watson coefficient. The sample autocorrelation coefficient r is the correlation between the sample estimates of the residuals e 1, e 2, , e n-1 and e 2, e 3, , e n.

In most cases, there is no strong theory as to what the **heteroskedasticity** should look like. This is why many empirical economists just "punt" and just use robust standard errors. If you are confident in your model and you know 0, 1, and 2, then you can just use **generalized** **least** **squares** . Instead of running the model yi 0 1x1i 2x2i i. Start studying **Heteroskedasticity** and **Generalized Least Square**.Learn vocabulary, terms, and more with flashcards, games, and other study tools.. Feb 23, 2019 · **Heteroscedasticity** is a problem because ordinary **least squares** (OLS) regression assumes that the residuals come from a population that has homoscedasticity, which means constant variance. When.

Learn how to eliminate the problem of **heteroscedasticity** by the method of **generalized least squares** . jbl j216a manual; oc71 pinout; psp homebrew emulators; pathfinder 2e golarion; why does he stare at me so intensely without smiling; wheel bushing; kimber 84m extended magazine; tech house music 2021. 4.6.3 **Generalized Least Squares** (GLS) The **general** idea behind GLS is that in order to obtain an efficient estimator of ˆβ β ^, we need to transform the model, so that the transformed model satisfies the Gauss-Markov theorem (which is defined by our (MR.1)- (MR.5) assumptions). Then, estimating the transformed model by OLS yields efficient.

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1 day ago · Since we are using an estimate of ρ, the approach used is known as the feasible **generalized** **least** **squares** (FGLS) or estimated **generalized** **least** **squares** (EGLS). Using the Durbin-Watson coefficient. The sample autocorrelation coefficient r is the correlation between the sample estimates of the residuals e 1, e 2, , e n-1 and e 2, e 3, , e n.

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We examine the predictive ability of economic policy uncertainty on stock returns of selected OPEC countries. In order to deal with certain statistical properties of the predictors, which include serial correlation, persistence, conditional **heteroskedasticity**, and endogeneity effects, wse utilize the Feasible Quasi-**Generalized** **Least** **Squares** (FQGLS) estimator in order to obtain accurate. The second approach is to use the **generalized** **least** **squares** estimator (GLS) that directly takes into account **heteroskedasticity**, and cross-sectional and serial correlations in the estimation. It is well known that GLS is more efficient than OLS. (Estimated) **Generalized Least Squares** Regression Model for **Heteroskedasticity** Background on GLS The GLS model is a generalization of OLS regression, which relaxes the assumption that the errors are homoskedastic and uncorrelated.

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Learn how to eliminate the problem of **heteroscedasticity** by the method of **generalized least squares** . jbl j216a manual; oc71 pinout; psp homebrew emulators; pathfinder 2e golarion; why does he stare at me so intensely without smiling; wheel bushing; kimber 84m extended magazine; tech house music 2021. The steps for obtaining a feasible **generalized** **least** **squares** estimator for are: 1. Estimate (8.25) by **least** **squares** and compute the **squares** of the **least** **squares** residuals . 2. Estimate by applying **least** **squares** to the equation β 12,, ,ββK K ˆ2 e i αα α 12,,,K S 2 122 ln ˆ ez zv ii S=α+α + +α +L iSi. if engagement rings. **GENERALIZED** **LEAST** **SQUARES** ESTIMATION OF ARMA MODELS. L. KAVALIERIS. University of Otago, Australian National University and Nigerian Educational Research and Development Council. Search for more papers by this author.

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It is not possible to obtain F statistics that are robust to **heteroskedasticity** of an unknown form. c. The Ordinary **Least Square** estimators are not the best linear unbiased estimators if **heteroskedasticity** is present. Consider the following regression model: yi = B0 +B 1xi + ui. Sep 23, 2020 · **Generalized Least Squares** (GLS) is a large topic. This article serves as a short. I don't know how you performed your search, but there are a number of SAS procedures which employ **generalized least squares** estimation methods. Since you don't tell us anything about the problem other than that you want some way to deal with **heteroskedasticity**, it is a bit difficult to advise on what methods to employ.

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**Generalized** **least** **squares** regression or weighted **least** **squares** regression in the presence of **heteroskedasticity**. On the other hand, if you are interested in determining how the quantiles of the conditional distribution of the dependent variable vary with the independent variables, then you would address that via quantile regression.

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**Generalized least squares** estimator (GLS) that directly takes into account **heteroskedasticity** (as well as cross-sectional and serial correlations in the estimation) – GLS estimator regarded as.

If the form of the **heteroskedasticity** is known, it can be corrected (via appropriate transformation of the data) and the resulting estimator, **generalized least squares** (GLS), can be shown to be BLUE. This chapter is devoted to explaining these points. **Heteroskedasticity** can best be understood visually.. 2017. 5. 10. Introduction. Semiparametric Feasible **Generalized** **Least** **Squares** (FGLS) offers potential efficiency gains in the presence of **heteroskedasticity** without knowledge of its functional form. Consistency, asymptotic normality, and first-order asymptotic efficiency have been established when using nearest-neighbors ( Carroll, 1982 ), kernel regression.

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When our data have heteroscedasticity and when we know the structure of that heteroscedasticity, we can use **generalized** **least** **squares**. This is just OLS applied to the normalized data. In practice, I suspect that knowing precisely Ω \boldsymbol{\Omega} Ω is rare, although perhaps it can be estimated from held out data. Another approach, which.

Start studying **Heteroskedasticity** and **Generalized Least Square**.Learn vocabulary, terms, and more with flashcards, games, and other study tools.. Feb 23, 2019 · **Heteroscedasticity** is a problem because ordinary **least squares** (OLS) regression assumes that the residuals come from a population that has homoscedasticity, which means constant variance. When. .

amlogic roadmap 2022. **Generalized least squares** estimator (GLS) that directly takes into account **heteroskedasticity** (as well as cross-sectional and serial correlations in the estimation) - GLS estimator regarded as more. Sep 23, 2020 · **Generalized Least Squares** (GLS) is a large topic. This article serves as a short introduction meant to “set the scene” for GLS mathematically. Quasi-**generalized** **least** **squares** regression estimation with spatial data ... Asymptotics for ls, gls, and feasible gls statistics in an ar (1) model with conditional **heteroskedasticity**. J. Econometrics, 169 (2) (2012), pp. 196-210. Article Download PDF View Record in Scopus Google Scholar. Baltagi and Pirotte, 2011. B.H. Baltagi, A. Pirotte.

amlogic roadmap 2022. **Generalized** **least** **squares** estimator (GLS) that directly takes into account **heteroskedasticity** (as well as cross-sectional and serial correlations in the estimation) - GLS estimator regarded as more. Sep 23, 2020 · **Generalized** **Least** **Squares** (GLS) is a large topic. This article serves as a short introduction meant to "set the scene" for GLS mathematically. (Estimated) **Generalized** **Least** **Squares** Regression Model for **Heteroskedasticity** Background on GLS The GLS model is a generalization of OLS regression, which relaxes the assumption that the errors are homoskedastic and uncorrelated.

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Lecture 24{25: Weighted and **Generalized** **Least** **Squares** 36-401, Fall 2015, Section B 19 and 24 November 2015 Contents 1 Weighted **Least** **Squares** 2 2 **Heteroskedasticity** 4 2.1 Weighted **Least** **Squares** as a Solution to **Heteroskedasticity** . . .8 2.2 Some Explanations for Weighted **Least** **Squares** . . . . . . . . . .11 3 The Gauss-Markov Theorem 12.