Ols Definition and 17 Threads

  1. F

    I Nonlinear Least Squares or OLS for Nonlinear Models?

    hello, I understand that the method of ordinary least squares (OLS) is about finding the coefficients that minimize the sum ##\Sigma (y_{observed} -g(X))^2## where ##g(X)## is the statistical model chosen to fit the data. Beside OLS, there clearly other coefficient estimation methods (MLE...
  2. F

    I Can Alternative Least Squares Methods Be Used in Linear Regression?

    Hello, Simple linear regression aims at finding the slope and intercept of the best-fit line to for a pair of ##X## and ##Y## variables. In general, the optimal intercept and slope are found using OLS. However, I learned that "O" means ordinary and there are other types of least square...
  3. L

    MHB Levels of Measurement, Basic OLS Regression Questions

    Hello soon to be saviors, 😊I have two really simple questions that I have already answered but the teacher wants more info. I am really stumped and I am not looking for the answers so much as an explanation on how to better answer the questions. I will copy and paste the problems and my answers...
  4. M

    A Using standard deviation values as independent variables

    Hey. I am planning on doing some research, where I predict a change based on different types of risk. The question is simple. Can I use values of standard deviation as independent variables in a linear regression analysis (OLS)? The standard deviation values over time will be calculated in...
  5. M

    A Robustness of time series analysis

    I have a time series model constructed by using ordinary least square (linear). I am supposed to provide some general comments on how one would improve the robustness of the analysis of a time series model (in general). Are there any general advice apart from expanding data, making it more...
  6. M

    A Indirect effect and spuriousity

    Say one has a regression result (ols) with significant coefficients for all independent variables. Then a new variable (Z) is added. This new variable is either something that reveals a spurious relationship among one of the initially included variables (x) and the dependent variable (y), or...
  7. M

    A Does centering variables for regression always result in unchanged coefficients?

    I am studying mean-centering for multiple linear regression (ols). Specifically I'm talking about the situation when there is interaction. When centering variables for a regression analysis, my literature tells me that the coefficients do not change? But when there is some sort of interaction...
  8. M

    A Centering variables, linear regression

    I am working with multiple regression with two independent variables, and interaction between them. the expression is: y = b1x1 + b2x2 and b3x1x2 The question is: does one center both independent variables at the same time, when checking for the significance of the effect of the independent...
  9. J

    The linear in linear least squares regression

    It is my understanding that you can use linear least squares to fit a plethora of different functions (quadratic, cubic, quartic etc). The requirement of linearity applies to the coefficients (i.e B in (y-Bx)^2). It seems to me that I can find a solution such that a coefficient b_i^2=c_i, in...
  10. U

    MHB OLS standard error that corrects for autocorrelation but not heteroskedasticity

    Question: By mapping the OLS regression into the GMM framework, write the formula for the standard error of the OLS regression coefficients that corrects for autocorrelation but *not* heteroskedasticity. Furthermore, show that in this case, the conventional standard errors are OK if the $x$'s...
  11. Z

    How to Calculate OLS Estimator with Given β0 in 50 Data Points?

    Homework Statement ΣiYi = 500; ΣiXi = 150; ΣiYi2 = 17000; ΣiXi2 = 4000; ΣiXiYi = 8000; n = 50. β0 = 3. Derive formula for OLS estimator of β1 and find estimateHomework Equations I'm new to OLS and I'm not sure where to go from: Σ(Yi - 3 - b1Xi)^2 I was able to walk through the steps in my...
  12. C

    OLS Estimator, derivation sigmahat(beta0hat)

    Good day, in the lectures of emperical economic research of my uni, we got to the topic of Linear Regression with one regressor. There I encountered upon: {\hat{\sigma }_{\hat{\beta }_{0 }}}^{2 }=\frac{1 }{n }\cdot \frac{\text{var }{\left( {\left[ 1 -{\left( \frac{\mu _{x }}{E {\left( {X _{i...
  13. C

    Having trouble understanding variance of OLS estimator

    So in computing the variance-covariance matrix for β-hat in an OLS model, we arrive at VarCov(β-hat)=(σ_ε)^2E{[X'X]^-1} However, I'm incredulous as to how X is considered non-stochastic and how we can just eliminate the expectation sign and have VarCov(β-hat)=(σ_ε)^2[X'X]^-1 I'm...
  14. P

    How to OLS several lines at once?

    Hello PhysicsForums.com! I've got several sets of data that are all intended to represent the same ideal data set. I need to fit a regression to said sets of data - but have no idea of how to go about it. All multiple-regression literature I can find reads to the tune of: given...
  15. M

    OLS regression - using an assumption as the proof?

    Hi, My question is about a common procedure used to find minimum and maximum values of a function. In many problems we find the first derivative of a function and then equate it to zero. I understand the use of this method when one is trying to find the minimum or maximum value of the...
  16. M

    Assumptions behind the OLS regression model?

    Hi, In many statistics textbooks I read the following text: “A models based on ordinary linear regression equation models Y, the dependent variable, as a normal random variable, whose mean is linear function of the predictors, b0 + b1*X1 + ... , and whose variance is constant. While...
  17. O

    How Do OLS and LAD Regression Methods Differ?

    Hi I'mwondering what's the difference between least squares method with least absolute deviation method. Assume we have y=ax+b+s where s isdeviation. Is the step to calculate even a and b is different. I read that those two methods are almost the same but hardly found a real good explanation...
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