Stack Exchange Network

Stack Exchange network consists of 174 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

Questions tagged [heteroscedasticity]

Non-constant variance along some continuum in a random process.

2
votes
1answer
34 views

Introducing random slopes in nested model improves model fit but residuals variances become unequal

I have measured boldness scores (continuous variable) across time (trials) for individuals (ID) within colonies (colony). The data is coded such that individuals 1-30 belong to one colony, 31-60 to ...
-1
votes
0answers
8 views

How do I check for heteroscedasticity in a ridge regression model?

I have created a ridge regression model using the glmnet package in R. When I plotted my model's residuals vs fitted values, I noticed a funnel shape and also a few outlier values. I removed the ...
0
votes
0answers
16 views

R - Heteroskedastic robust errors with `bife` package

I'm using a fairly new R package called bife for binary choice fixed effects model. It looks like vcovHC in the ...
0
votes
0answers
16 views

how can I model the variance with gls in a two factors model? [duplicate]

I run an two factors anova but it doesn´t have homogeneity of variance, so I´m trying to use gls to model the variance but it gives me this error, what can i do? Thanks for your help ...
0
votes
0answers
21 views

How to solve heteroskedasticity using ARCH/GARCH models? [closed]

I am estimating the following model: When I perform a Breush test and a White test I get that there is heteroskedasticity. I want to solve this using either ARCH or GARCH models, but I still do not ...
1
vote
0answers
14 views

ANOVA with post hoc for non-normal data with unequal variance

I have 200-some variables with 25,000 observations in each of 4 categories. For each variable individually, I need to identify where we have variability between categories. Therefore, I need an ...
2
votes
2answers
274 views

Why use OLS when it is assumed there is heteroscedasticity?

So I'm slowly going through the Stock and Watson book and I'm a bit confused on how to deal with the issue of homoscedacity/heteroscedacity. Specifically, it is mentioned that economic theory tells ...
0
votes
0answers
27 views

Standard deviation varying with $x$ in linear regression

I understand that Bayesian regression reinterprets a linear regression: $$Y=\beta X + \epsilon$$ in terms of probability distribtions: $$Y = N(X\beta,\sigma^2)$$ However, it seems to me that this ...
0
votes
1answer
20 views

Intuitive explanation for Studentized Residuals

I'm currently testing the homoscedasticity assumption of multiple linear regression. This can be done by plotting studentized residual plots. The problem with residual plots is even if the variance of ...
0
votes
0answers
2 views

Double-bounded dichotomous choice

I need help. I am using a Double-bounded dichotomous choice model and I do not find tests of heteroscedasticity or tests for the assumption of linear relationship between the logit of the result and ...
0
votes
0answers
21 views

What to do when there is heteroskedasticity in an ANCOVA model?

What to do when there is heteroskedasticity in an ANCOVA model? Is there a correction similar to Welch ANOVA? Or is better to use a OLS regression model? Should we try to solve heteroskedasticity by ...
1
vote
0answers
42 views

Exponential heteroskedasticity

Assume that the form of heteroskedasticity is exponential such that $u_{i} = e^{0.5X_{i}\gamma}\upsilon_{i}$ where it is assumed that $\upsilon_{i} \mid X_{i} \sim N\left(0,1\right)$ and $X_{i}$ ...
0
votes
0answers
40 views

Heteroskedasticity in cross sectional models vs. in time series models

Going through the paper by Robert Engle GARCH 101: An Introduction to the Use of Arch/Garch Models in Applied Econometrics. I read the paragraph which said: The warnings about heteroskedasticity ...
0
votes
0answers
17 views

Homoscedasticity - binary independent variable [duplicate]

I am currently doing a a) linear regression with independent variable being binary, dependent continuous b) multiple regression with independent variables being binary and continuous, dependent ...
2
votes
1answer
28 views

WLS vs Dummy variable coding for heteroscedasticity

I am a beginner level stat learner (with graduate training in Applied Math) I have just read a sage book which states the following: "Dummy variables help address the issue of heteroscedasticity in ...
0
votes
1answer
32 views

How to use White-test to check if the heteroscedasticity has been effectively dealt with by a WLS?

Consider an OLS model with $n$ observations and $p$ explanatory variables (including an intercept term) $$y=X\beta + \epsilon$$ We may use a White test to (approximately) check for the presence of ...
1
vote
0answers
17 views

Standardizing Variable for a Regression of X on ratio Y/Z: What would you do?

I have a 3 variables X,Y,Z and I'd like to observe the effect of X on Y/Z. (If you're interested in the flavor text of this question: X is the capacity of air conditioners (BTU), Y is the price, and ...
0
votes
0answers
8 views

Estimate variance of next element in heteroskedastic time series

I have a time series $X$ of which I can reasonably assume the mean is constant at zero. However, I know it to be heteroscedastic. Given $X_1$ through $X_N$, I'd like to estimate the variance $\sigma_{...
1
vote
1answer
34 views

Heteroskedasticity-consistent standard errors

See https://en.wikipedia.org/wiki/Heteroscedasticity-consistent_standard_errors. Assume the model of interest is the linear regression model. If the errors are heteroskedastic, $\hat{\sigma}^2_i = \...
4
votes
1answer
45 views

In linear regression, data is highly skewed, transformation doesn't work..!

I have dataset with 9524 observations / 97 variables. Most of variables are numerical, and some of factor variables (Yes/no or several levels) I want to perform multiple linear regression with ...
0
votes
0answers
21 views

Construct Confidence Curve in R in Change Point analysis

I am trying to reproduce the journal article "Confidence distributions for change-points and regime shifts" (on page 16 top left hand corner) Firstly, I generated random sample using i) N(1,1) and ii)...
2
votes
0answers
39 views

Robust Regression in MATLAB's robustfit: what is the optimal weight function to tackle heteroskedasticity?

I'm currently performing a linear regression analysis and encountered a fair amount of heteroskedasticity. Increases in predicted values go along with decreases in residual variance. Otherwise, the ...
1
vote
0answers
9 views

significance testing of unequality of coefficient of variation in multiple samples

I'm performing a study on inter-doctor variation. For that, I'm studying a diagnostic instrument that has around 100 items that all have to be rated. Some are dichotomous, some ordinal. Several ...
0
votes
0answers
45 views

Diagonal straight lines in residual vs predicted values: can it be fixed with bootstrap resampling?

I am studying a health-related-quality-of life scale and I run a multiple linear model for each of its subscales. For a few of these subscales I came across the pattern of several diagonal straight ...
1
vote
1answer
30 views

Assumptions of linear fit; linearity and homoscedasticity

I'm reading about the assumptions of taking a linear fit between two variables from here, and that source says: For diagnosing non-linearity: nonlinearity is usually most evident in a plot of ...
0
votes
1answer
29 views

Interpreting a plot about heteroskedasticity

Can anyone tell me whether this graph shows heteroscedasticity? If the graph shows heteroscedasticity, how can I solve it?
0
votes
0answers
17 views

Considering heteroskedasticity in Cp approach to adjusting training error rate in regression

I have been introduced to $C_p$ as a way to adjust the training error rate to account for bias due to overfitting regression models. $C_p$ is defined as such: $C_p = \frac{1}{n} (RSS + 2d\hat{\sigma}...
0
votes
0answers
5 views

What happens to the Brown-Forsythe test when the data is weighted?

I wish to run a Levene and Brown-Forsythe test to determine whether its safe to assume that different groups have the same variance of a given variable (say variance of wages across different regions ...
0
votes
0answers
41 views

Assuming heteroscedasticity produces terrible QQ plot

I created a model using the lm function and obtained a model with an AIC of 390516.1 and the following QQ-plot: However, the Breusch-Pagan Test returned a p-value ...
2
votes
1answer
148 views

Assumption of homoscedasticity/equal variance violated in a 2-way ANOVA

I'm analyzing results from an experiment where 56 samples were tested for a specific response to electrical stimulation. Electrical stimulation was done at 2 different stimulation frequencies, and at ...
0
votes
0answers
21 views

Estimation in case of data dependent noise

I am trying to estimate $a$ and $b$ in the below linear model $$y = ax + b + \epsilon$$ where $x \in R^n$ and $y \in R^n$ are given, and $\epsilon$ depends on the parameters and the $x$. Also, it is ...
0
votes
0answers
19 views

I can perform an F-Test if Bartlett and Levene fail, but Shapiro-Wilk Pass?

I'm going through the assumptions on an F-test and I want to make sure I've gone through each item so that my results are valid. Data is normally distributed; The samples are independent from one ...
2
votes
0answers
40 views

divergence of beta estimates between OLS and regression with ARIMA error

I have physiological time-series data: ~60k observations per channel, ~100 Hz sampling. I will model individual channels with ~20 regressors. Under OLS, given temporal autocorrelation in the data, ...
2
votes
1answer
80 views

Should VAR(1) and VAR(1)-GARCH(1,1) give equal point forecasts out of sample?

I have a VAR(1) with heteroscedastic errors, so I used the rmgarch package for R to estimate a VAR(1)-GARCH(1,1). After that I performed an out-sample forecast for ...
2
votes
1answer
36 views

How to check if a process has constant variance?

I am using KPSS test to verify if my process has constant variance around the mean, but I am not sure if this is the correct test for my case. In KPSS the null hypothesis is that the process is ...
0
votes
0answers
28 views

Compare forecast interval between ARIMA and ARIMA/GARCH

I tried to compute parameters of ARIMA/GARCH in two step. The first one is to build ARIMA and then fit GARCH using iid Gaussian MLE estimation. The second one is to construct ARIMA/GARCH ...
2
votes
2answers
91 views

How to find the weight of the weighted least squares regression analysis?

As the title, I am having trouble to the find weight at the weighted least squares estimation. I found that some people use weights like wts <- 1/fitted(lm(abs(residuals(regmodel.1)) ~ x))^2 or ...
0
votes
0answers
39 views

Logit Models - Testing for Heteroskedasticity in R

I am testing a logit model; I can use either stats::glm or glmx::hetglm. To choose over these two functions, first, I have to test for heteroskedasticity LM2 (Davidson and MacKinnon, 1984) as it is ...
1
vote
0answers
17 views

Straddles across earnings [on hold]

Hi everyone I have a data set composed of about 25 variables in which the goal is to predict how much the stock will move after it reports earnings. I am getting alot of heteroskedasticity in the data....
1
vote
1answer
76 views

Dealing with heteroscedasticity when dependent variable is already log-transformed

I have already log-transformed the dependent variable but there is still heteroscedasticity in the residual-fitted plot. What one usually does in situations like ...
1
vote
0answers
3 views

Quantify group homogeneity

I have a set of data which are responses to ten questions. I have done some text analysis which gave me 10 similarity matrices (one for each question) between all responses for each question. I ...
1
vote
0answers
49 views

Spatial Lag Model and Heteroscedasticity

I am using Spatial Lag Models with the form yi = ρWyi + βXi + εi, and am estimating these in R using spdep::lagsarlm. However, Breusch-Pagan-Tests using ...
0
votes
1answer
34 views

Does taking logs to supress hetroskedasticity only work for the dependent variable?

I have been told that by logging variables in a regression that hetroskedasticity of errors can be reduced. Is this the case also if only my dependent variable is logged?
0
votes
1answer
70 views

Assumption of ARIMA and relation to ARCH/GARCH model?

I only have a very basic understanding of time series analysis. As I am learning ARIMA and then ARCH/GARCH models, I have some subtle (at least for me) questions on the common procedure to build such ...
0
votes
1answer
417 views

Fligner-Killeen test of homogeneity of variances interpretation

I have two samples that I want to verify that variances are equals in order to apply Wilcoxon rank sum test that assume that the variance are equals. Here a boxplot As you can see the variance ...
1
vote
0answers
43 views

Homoscedasticity test for repeated measures ANOVA, SPSS

My experiment has three treatments, looking at different measures of animal behaviour in response to each treatment. Firstly, is testing for homoscedasticity for a ...
1
vote
0answers
30 views

Do I need to check for heteroskedasticity/heteroscedasticity only when performing regression analyses?

I don't know if this is a silly question but I haven't been able to find precise answer anywhere. I'm building a linear regression model in R to predict a variable of interest $y$, but there are also ...
0
votes
0answers
12 views

Do I need to check for heteroskedasticity/heteroscedasticity only when performing regression analyses? ncvTest() and bptest() in R

I don't know if this is a silly question but I haven't been able to find precise answer anywhere. I'm building a linear regression model in R to predict a variable of interest, but there are also ...
3
votes
2answers
108 views

Linear Regression: How can the error term have variance?

I seem to be missing something fundamental about the structure of the linear regression model. Suppose we have a response variable $Y$ and $p$ predictor variables $X_1$ to $X_p$. For a particular ...
0
votes
1answer
23 views

Mixed effects model (?) with known data uncertainty

I have been given data from a number of replicate experiments, each of which provides both a value and a standard error. I would like to arrive at a single estimate of this value and its standard ...