Tagged Questions

1
vote
2answers
47 views

One predictor is a linear combination of another

If I try and fit the linear regression lm(y~V1+V3,data=x) with data: ...
2
votes
0answers
96 views

VIF values and interactions in multiple regression

I am running a multiple regression of the form y~a+b+c+ab+ac+bc I have checked the VIF values for the direct effects - should I check them for the interactions? I am assuming not as that would ...
2
votes
0answers
70 views

Non negative least squares with minimal colinearity

I am trying to fit a dataset using the standard NNLS (non-negative least squares) approach. Formally: $\min_x ||Ax-b||^2_2$ s.t. $x\ge0$ This is a quadratic program and can be solved optimally. The ...
0
votes
0answers
134 views

Highly Collinear Independent Variables of Interest

Suppose I am interested in the follows: I have county-level data. For each county, I know the share of the population that was born from one parent and the share of the population that was born from ...
0
votes
3answers
318 views

“Wrong Sign” On Regression Coefficients - Hierarchical Multiple Linear Regression

I am analyzing my data on the relationship between spirituality and negative emotional states (depression, anxiety, and stress) using a hierarchical multiple linear regression. Everything seemed to be ...
5
votes
1answer
1k views

How to deal with high correlation among predictors in multiple regression?

I found a reference in an article that goes like: According to Tabachnick & Fidell (1996) the independent variables with a bivariate correlation more than .70 should not be included in ...
3
votes
0answers
435 views

Time series forecasting using autoregressive and linear terms in R

I have real daily market data which I'm looking at to create a model for forecasting. The model that I created (below) used autoregressive terms within a linear regression. I was sharing this with a ...
0
votes
0answers
1k views

How to interpret R-squared in multiple regression with more sets of dummies and continuous variables?

I have a problem with a multiple regression I performed: model without constant term; one dependent continuous variable; first set of dummies: derived from 2 continuous variables, I used the median ...
3
votes
1answer
220 views

At what VIF level should you switch from OLS to ridge-regression?

Regarding multicollinearity, is it recommended to use ridge-regression if you have some covariates with VIF values around 10 in the OLS model? What would be the best VIF level to use to decide ...
4
votes
1answer
173 views

Multiple regression with missing predictor variable

Suppose we are given a set of data of the form $(y,x_{1},x_{2},\cdots, x_{n})$ and $(y,x_{1},x_{2},\cdots, x_{n-1})$. We are given the task of predicting $y$ based on values of $x$. We estimate two ...
9
votes
2answers
2k views

How to deal with collinearity issue when performing variable selection?

I've got a dataset with 9 continuous independent variables that I'm trying to select between to fit a model to a single percentage (dependent) variable, Score. ...
1
vote
0answers
212 views

Abusing Linear Models under Multicollinearity: Simulation for 'realistic' movement of predictors

I have a reasonable understanding of why multicollinearity is a problem is regression models, along the lines of this excellent post. To summarise my understanding, for a regression model of $y = ...
8
votes
6answers
2k views

Multicollinearity when individual regressions are significant, but VIFs are low

I have 6 variables ($x_{1}...x_{6}$) that I am using to predict $y$. When performing my data analysis, I first tried a multiple linear regression. From this, only two variables were significant. ...
1
vote
1answer
278 views

Assessing multicollinearity of factors

I have a multifactor model (with 7 factors currently) and 754018 observations. In order to check for multicollinearity issues as the model grows I wrote an R script to compute a correlation matrix ...
26
votes
6answers
7k views

How can a regression be significant yet all predictors be non-significant?

My multiple regression analysis model has a statistically significant F value however all beta values are statistically non-significant. All the regression assumptions are met. No multicollinearity ...
8
votes
2answers
4k views

VIF, condition Index and eigenvalues

I am currently assessing multicollinearity in my datasets. What threshold values of VIF and condition index below/above suggest a problem? VIF: I have heard that VIF $\geq 10$ is a problem. After ...