Questions tagged [heteroscedasticity]
Non-constant variance along some continuum in a random process.
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test for homogeneity and heterogeneity in clustering
I want to check if there is a way (or test) to verify homogeneity among and heterogeneity between clusters, besides the almost 30 clustering indices that are available (see NbClust). I am also aware ...
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1answer
29 views
ARIMA GARCH model
According to what I have found so far, in order to implement ARIMA we need to have a stationary (constant mean and variance) transformed data set. In addition, I have also seen that the square of the ...
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Does $\mathbb E\epsilon^2 = const$ and $\mathbb V\epsilon = const$ are equivalent conditions for homoskedasticity?
Wooldridge states in his book that homoskedasticity requires $$\mathbb V[\epsilon\mid x] = const.$$ But I often encounter that $\mathbb E[\epsilon^2\mid x] = const$ is required for homoskedasticity ...
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1answer
40 views
Polynomial regression - non-constant residual variance
I have the following regression task.
The dataset below is a list of costs for certain levels of a covariate variable.
...
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21 views
ARIMA stabilization process
Initially, before apply arma model stationarity conditions must hold. According to that,time series data must have same variance and mean with normal distribution. If raw data is not normal then box ...
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0answers
9 views
How does heteroskedasticity affect the validity of R squared and other metrics?
I apologise for the trivial question, but I have got myself confused about how heteroskedasticity affects OLS regression and would be very thankful for your help.
In standard OLS, homoskedasticity is ...
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1answer
22 views
Is one-way ANOVA robust to violations of homoscedasticity?
I read here that if group sizes are equal, ANOVA is robust against the violation of the assumptions of normality and homoscedasticity.
I am wondering if this is the case, and if so why?
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1answer
65 views
Analysis of variance with not normally distributed residuals : how important is normality?
I am using gls and anova to analyse my data. I use gls to aply weights. I have one factor (tree genotype) and I analyse its influence on soil content.
Here is an exemple of my data with one variable (...
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3answers
193 views
Paired t-test means that the variances of the 2 samples are the same?
Do paired samples imply that they have the same variance?
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1answer
38 views
What to do when Breusch-Pagan shows heteroscedasticity but plot looks it might not?
So I'm trying to run a Least-Squares Means with a covariate. However when I run Breusch-Pagan test it shows heteroscedasticity even after either tukey or log2 transformation, however the plots seem ...
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4 views
Estimation of variance in GMM
In GMM, the efficient weight matrix minimizes the asymptotic variance of the GMM estimator by setting:
$$ W_T^{opt} = S_T^{-1}$$
where $S_T$ is an estimator of the asymptotic variance of the moments,...
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12 views
Is there any method except Box-Cox transformations? [duplicate]
I always see that in order to reduce heteroscedasticity we can employ Box-cox transformations. But this totally useful if variance is a function of mean like $u_t^5$ or $u_t^2$....What should we do ...
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Using coeftest results in predict.lm() in R [closed]
I am analyzing a dataset in which the variance of the error term in my regression is not constant for all observations.
For this, I re-built the model, estimating heteroskedasticity-robust (Huber-...
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1answer
41 views
Does it make sense to do Levene's test in relation to ANCOVA?
In relation to another question, Ben Bolker writes that
if you have all-categorical predictors, you can test for heteroscedasticity (and other issues such as non-Normality) by
dividing the ...
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0answers
19 views
Optimal Power Transformation to Reduce Heteroscedasticity in Time Series
I would like to ask: what method should we employ if the variance in time series behaves like a high order (such as $au_t^5+bu_t^4+cu_t^3$) polynomial function with respect to mean? On internet, I ...
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2answers
168 views
Is there any difference between heteroscedasticity and homoscedasticity?
It seems these terms are confusing. Some experts think that these terms have a contrasting meaning which is incorrect. Is there someone who can justify the interpretation.
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LMM (nested data) -> Heteroscedasticity
I want to apply the lmer function to test some fixed factors (treatment, body size of females, body size of males) and some random effects (females, males, location of male, location of female) on the ...
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1answer
29 views
Interaction term to deal with heteroscedasticity
The variance in my dependent variable changes with a changing value in an important independent variable and is hence probably distorting the measured effect of the treatment. Can I combat this form ...
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18 views
Heteroskedasticity in linear probability models
I have a class on linear probability models.
We want to estimate a model $y=\beta x$ where both $y$ and $x$ can be either $0$ or $1$, so that the conditional expectation function can be expressed in ...
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1answer
37 views
Non-normal distribution and heterogenous variances
I have a data set in which I measured a continuous variable (positive, continuous data) in response to different treatments(15 different pathogens) and I am unsure how to statistically analyse the ...
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2answers
64 views
Papers About Permutation Version of Welch's t-test
Permutation tests seem to provide a promising alternative for the unpaired t-test, requiring fewer assumptions. However, the core assumption of the permutation test, exchangeability, implies ...
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0answers
23 views
OLS Heteroskedasticity correction
I have a data and was trying to correct for heteroskedasticity (which is significant as per Breush Pagan test). However, after using the robust command in stata, my standard errors of almost all the ...
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16 views
How to check Constant Variance for a linear regression with a Binary predictor?
I am running a linear regression with a continuous outcome, one binary dependent variable (Main predictor), and two continuous dependent variable. How do I check constant variance of this model in R?
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1answer
150 views
In the presence of heteroskedasticity, is quantile regression more appropiate than OLS?
..for understanding the relationship between a dependent and independent variables, given that quantile regression makes no assumptions about the distribution of the residual.
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1answer
139 views
Dealing with heteroscedasticity and non-normality in a mixed model
I am trying to fit a mixed model (person as random effect) on data which has heteroscedasticity and non-normality. I log-transformed the Y-variable but it did not fix the problem. Normality and ...
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3answers
50 views
How to detect Heteroscedasticity in a residual plot?
In this residual plot, both the increase and the decrease in the y variables are observed. In this case, how do you conclude whether heteroscedasticity exist or not? I am not sure if I can just simply ...
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0answers
169 views
Failure to replicate calculation of PCA residuals in linear regression with heteroscedasticity
In their preprint, Rocha et al. suggest a new type of residual for linear regression models with heteroscedasticity. They call their new residual PCA residuals. I have tried to replicate some of their ...
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2answers
102 views
In linear regression, why are raw least squares residuals heteroskedastic?
In my course notes on a regression course with regards to the detection of heteroskedasticity there's the following quote:
"Because the least-squares residuals have unequal variances even in the ...
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0answers
70 views
Bayesian spatial autoregressive (SAR) model with heteroskedasticity in R
In socio-economic data, I always found heteroskedasticity that can't be solved using transformation.I had read a paper "Spatial autoregressive models with unknown heteroskedasticity:A comparison of ...
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17 views
heteroscedasticity and sample size [duplicate]
How to explain the situation that heteroskedasticity disappear as the sample size grows larger? is there any evidence shows that the existence of heteroskedasticity has something to do with the sample ...
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0answers
28 views
White test for heteroscedasticity of simple linear regression in R [duplicate]
The question is straightforward: How to implement White test (a test for heteroscedasticity) for a simple linear regression model (lm object) in R?
I have tried "whites.htest(var.model)", however, ...
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0answers
45 views
Robust Gamma Regression
I am modeling some spectroscopic data where the response of the instrument to the size of the input is strictly positive and non-linear. Gamma regression seems like a good choice to explain the data, ...
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63 views
Application of Box-cox transformation consecutively
as far as I have searched even we can obtain optimal lambda value to transform data to normal distributed with constant variance in box cox transformation method we may have not proper normal ...
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0answers
17 views
OLS question about standard errors and Heteroscedasticity
In the case of OLS, what stops one from modelling heteroscedasticity and providing predictor dependent coefficients. So, different ranges of the predictor with their own standard errors? Is this ...
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0answers
15 views
Check homogeneity in Cox Proportional Hazard Regression
I'm investigating the associations of a quartile variable (let's say: X, consists of 1st/2nd/3rd/4th quartiles based on categorization of a continuous variable) with survival in Cox Proportional ...
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1answer
37 views
Is the optimal lag length for the Hansen and Hodrick and Newey West robust standard errors the same?
Is the optimal lag length for the Hansen and Hodrick and Newey West robust standard errors the same?
I have read in Greene that the optimal is $T^{1/4}$ for Newey-West, is this the same for Hansen ...
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How can I account for heteroscedasticity and residuals that are not normally distributed in multiple linear regression?
I am conducting a series of multiple linear regressions. I am using the same IVs to predict four different DVs. When testing for the assumptions of regression I realised that the models for two of ...
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186 views
Fixed effects - correcting for autocorrelation and heteroskedasticity, panel data analysis in R
I have a datset of 25 counties over 11 years, with response variable unemployment ( in %), and 6 explanatory variables (proportion with high school, some economic indicators, etc).
After some tests ...
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GARCH, EGARC, GJR-GARCH EViews
I'm using Eviews to model and forecast volatilities for 6 different stock markets( it's for my dissertation). I found serial correlation in almost every log-return and even after running the GARCH ...
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Generalized Least Squares and Heteroskedasticity
I am trying to model a OLS where I know that the hetero-skedasticity is like this
$E(\epsilon^2)$ = $\sigma_i^2$ = $\delta_0$ + $\delta_1*X_{i2}$
So, I was using the concept of feasible generalized ...
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0answers
61 views
auto-correlation and OLS regression
I was trying to find the OLS estimator for the model:
$Y$ = $\beta_0$ + $\beta_1X_{1t}$ + $\beta_2X_{2t}$ +.......+ $\beta_5X_{5t}$ + $e$
t = 1,2,3 ......, 50 time ordered observations
X is a full ...
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25 views
In a fixed effects regression, will the residuals be uncorrelated with the estimated fixed effects?
I have been getting a lot of helpful answers from StackExchange so I'm posting my first question, hoping to get some help with a hw question.
I understand generally a fixed effects variable is like ...
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0answers
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mixed ANOVA alterantive for inhomogeneity of variances in SPSS
I am trying to compute a mixed ANOVA with
one within-subjects factor (two different types of stimulus categories) and
one between-subjects factors (three participant groups).
My problem is that the ...
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0answers
38 views
How to prevent heteroskedastic models from overfitting?
I'm trying to fit neuroscience data using a Gaussian Process, but noticed that it behaves poisson-like (var = mean).
Since classic GP models assume iid noise, I figured I could get a better fit by ...
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0answers
31 views
How to check the assumption of homogeneity of variance visually using box-plots
Can anyone confirm if APA no longer recommends using statistical tests for checking assumptions?
If so, what are the alternatives? I have been told to check visually but I am unsure how to check ...
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1answer
47 views
Is this a valid Gaussian Process kernel?
$\mathcal{K}\Big( \; (x,y), (x',y') \; \Big) = \sigma_f^2 \exp{ \frac{(x-x')^2}{2l^2 \cdot (y+y')^2} } $, where $l > 0$
The variance associated with each training point (given by a vector) is a ...
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1answer
77 views
Linear regression with error dispersion dependent on the independent variable
Suppose $y=ax+z$ where $x, y, z$ are random variables with range in $\mathbf R$, $\mathbf E[x]=0$, the probability distribution $p(z|x)$ is
1) normal distribution $N(0,\sigma(x)^2)$ with mean $0$ ...
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1answer
48 views
Constant Variance Assumption in Linear Regression
It seems to me that the following plot of "Residuals Vs. Fitted Values" violates the assumption of constant variance, since for lower fitted values, there are fewer points whereas for higher fitted ...
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0answers
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Calculating sandwich estimator
Considering design matrix $X \in \mathbb{R}^{n\times p}$ $(n>p)$ and response $y\in \mathbb{R}^{n}$. The sandwich estimator can be calculated directly using
$$(X^TX)^{-1}X^T diag(r^2) X (X^TX)^{-...
