Questions tagged [heteroscedasticity]
Non-constant variance along some continuum in a random process.
778 questions
-2
votes
0answers
9 views
How to interpret results for white tests?
I am trying to test for the incorrect functional form, so I ran some tests for my regression equation which has 10 independent variables and 1 dependent variable.
There are two sets of results shown ...
3
votes
1answer
135 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.
1
vote
1answer
42 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 ...
1
vote
3answers
38 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 ...
3
votes
0answers
114 views
+100
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 ...
7
votes
2answers
77 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 ...
1
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0answers
44 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 ...
0
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0answers
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 ...
1
vote
0answers
25 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, ...
6
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0answers
39 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, ...
0
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0answers
41 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 ...
2
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0answers
12 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 ...
0
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0answers
9 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 ...
0
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0answers
14 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 ...
0
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0answers
36 views
0
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0answers
7 views
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 ...
0
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0answers
33 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 ...
0
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0answers
9 views
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 ...
0
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0answers
15 views
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 ...
0
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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 ...
0
votes
0answers
21 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 ...
1
vote
0answers
8 views
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 ...
0
votes
0answers
25 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 ...
0
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0answers
18 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 ...
1
vote
1answer
37 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 ...
3
votes
1answer
51 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 $0$ ...
0
votes
1answer
38 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 ...
2
votes
0answers
24 views
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
$$(X^TX)^{-1}X^T diag(r^2) X (X^TX)^{-...
1
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0answers
11 views
Stationarity of ADL(p, q) with heteroskedasticity
Suppose I have the model
$$y_t = \alpha_0 + \alpha_1 y_{t-1} + ... + \alpha_p y_{t-p} + \beta_0 x_t + ... + \beta_q x_{t-q} + \epsilon_t,$$ where $\{x_t\}$ is a stationary process and $\epsilon_t$ has ...
1
vote
1answer
47 views
How would heteroskedasticity look like with negative correlation?
I hope I formulated the question correctly, but I am purely interested in heteroskedasticity: cov($e_i$,$e_j$) and not in autocorrelation with the dependent variables.
So positive correlation of the ...
1
vote
0answers
10 views
Generating multivariate heteroskedastic data
I am trying to estimate a VAR model with heteroskedastic error terms.
$e_{it}$ is given by $η_{i,t} √h_{ii,t}$, where $η_{i,t}$ is iid, N(0,1).
I am trying to get $e_{it}$
Does anyone have any ...
1
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0answers
11 views
Appropriate method to assess scatter or variation in groups with dramatically different means
I am working on a product fabrication process, and I want to demonstrate the consistency of my product. To do this, I am trying to measure the variation in the extent to which a biological material is ...
1
vote
0answers
8 views
Wide variety of results in Breusch-Pagan Tests on simulated data?
I wanted to see how the results of the BP-test would come out. This stemmed from a debate with someone who claimed that the BP-test would start rejecting the null hypothesis of homoskedasticity even ...
0
votes
0answers
27 views
why do residuals of the model show heteroscedasticity when plotted against time
Following up on question
Dealing with heteroscedasticity in mixed models
I collected crop yield data for many years across multiple locations, which are nested under provinces and some associated ...
0
votes
0answers
27 views
Lagrange Multiplier Statistic When Testing for Heteroskedasticity
I had a debate with another student and wanted to get some more perspective. She brought up a fascinating point for when we test for heteroskedasticity through the Breusch-Pagan Test. Recall,
$$LM = ...
3
votes
1answer
74 views
Dealing with heteroscedasticity in mixed models
I collected crop yield data for many years across multiple locations, which are nested under provinces and some associated weather data. I am interested in making a model and then using new weather ...
0
votes
0answers
10 views
Why does the Breusch Pagan test use unstandardized residuals and predicted values?
If I understand correctly, there are different ways to test for heteroscedasticity. I first learned to do it by looking at a scatterplot of standardized residuals vs. standardized predicted values. I ...
1
vote
1answer
72 views
Breusch–Pagan test: what exactly is the z-variable
I want to perform a Breusch-Pagan-Test. I did calculate some very small examples following the procedure described below, just to understand what the test does:
found here: (https://ipfs.io/ipfs/...
3
votes
1answer
46 views
Implement VAR model in R with HAC corrected standard errors [closed]
I have fitted a VAR model in R (with function VAR) and would like to use HAC corrected standard errors. How is that possible?
2
votes
2answers
100 views
What are the assumptions of a Gamma GLM or GLMM for hypothesis testing?
What are the assumptions when doing hypothesis testing using a Gamma GLM or GLMM? Are the residuals suppose to be normally distributed and is heteroscedasticity a concern like the Gaussian (normal) ...
5
votes
1answer
39 views
Use of supportive inferential statistics (e.g. Levene's and normality tests) in underpowered samples
We know that underpowered statistics greatly increase the probability of a type II error (by definition), meaning a greater chance of failing to reject the null hypothesis despite the existence of a '...
1
vote
2answers
64 views
Do these standardized residuals show heteroskedasticity?
I'm practising in the individuation of heteroskedasticity from the standardized residuals.
I know that, if the time series is homoskedastic, the spread of the residuals should be constant and random ...
0
votes
0answers
56 views
Unable to remove heteroscadasticity
This is my model:
lm(PCTUI~year+statefips+factor_agecat+factor_sexcat+factor_racecat+factor_iprcat, data = train)
I have factorized the categorical variable. ...
4
votes
1answer
96 views
How to perform over-dispersion test where null is quasi-Poisson
If I understand correctly, a quasi Poisson regression assumes roughly that
$$
\mbox{E}\left[y\left|x\right.\right] = \exp{\left(x^{\top}\beta\right)},
\quad
\mbox{VAR}\left(y\left|x\right.\right) = \...
1
vote
0answers
33 views
Testing for Heteroscedasticity of Multivariate Multiple Regression [closed]
I want to test for heteroscedasticity on a regression model with multiple dependent variables using R. I want to see if the indenpendent variable has an effect on the variance of all of the dependent ...
0
votes
0answers
37 views
Which one of these is correct for linear regression?
Only one of these is supposed to be the correct one for simple linear regression. Which pair of plots would you say has constant variance and normal distribution? I feel like none of them have both ...
0
votes
0answers
50 views
Heteroskedasticity robust confidence interval
I have used Chebyshev's Inequality to construct confidence intervals. I know that Chebyshev's Inequality works for any distribution as long as the second moment exists. But I have got a comment that ...
1
vote
0answers
26 views
Heteroskedasticity testing
Im estimating the carhart 4 factor model. Im testing for heteroskedasticity to see whether i need to use adjusted standard errors, but i am finding conflicted results. All but one test (ARCH) are ...
1
vote
1answer
23 views
why homogeneity test is used for ANOVA not for t test?
Why the homogeneity test must be curried out for ANOVA test but not for t-test when sample size is more than 30 ?
0
votes
0answers
35 views
Heteroscedasticity and non-normaility in VAR(3) model
After specifying my VAR model, I run several diagnostic tests. While the serial.test indicates that there is no Autocorrelation,
...
