Non-constant variance along some continuum in a random process.
2
votes
0answers
12 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, ...
0
votes
0answers
15 views
Why after WLS I still get the same result of homoscedasticity tests?
I generated such a sequence
x <- seq(1:64)
y <- 101 + x + rnorm(64, x, 3*x^(2/3))
df <- data.frame(x, y)
then I did the regression and checked for ...
2
votes
1answer
54 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
26 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
16 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 ...
0
votes
0answers
22 views
nlme error when testing for heteroscedasticity
This is the first question I've ever posted on a forum so I hope I'm following the correct protocol.
I've been teaching myself R and using it to run mixed effects models following the recommendations ...
2
votes
2answers
33 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
19 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
15 views
Straddles across earnings
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
30 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
29 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
36 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
54 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
14 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
8 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
79 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
22 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 ...
0
votes
0answers
10 views
standard error adjustment in pooled panel regression with time fixed effect
I am fitting a pooled-panel cross-sectional regression with a time fixed-effect:
\begin{align}
Y_{i,t}=a_t+X_{i,t}b+e_{i,t}.
\end{align}
But I have $E(e_{i,t}e_{j,t})\ne0$ for $i\ne j$, $E(e_{i,t}e_{i,...
0
votes
0answers
18 views
Skewed dependend variable, residual assumption violations, appropriate model
I am working with a survey variable which asks respondents to place themselves on a scale from 0 to 10 (integer) (N=1850), where both ends have a specific meaning.
Thus, treating the variable as ...
0
votes
0answers
19 views
What is the difference between HAC and PCSE?
I have data consist of 88 companies in 5 year (440 observations) and used 3 independent variables with 3 control variables (total 6 variables). I have already test the best model for my data and the ...
1
vote
0answers
25 views
Engle Granger Error Correction Model - Normality, heteroskedasticity and Autocorrelation tests
I'm building an Error Correction Model using the Engle-Granger approach with the following interest rates data:
Observations: 230
Periodicity: Monthly
I have the following model:
$$\Delta R_t = \...
0
votes
0answers
25 views
Disparity between ANOVA results and pairwise comparisons (both heteroscedastic-consistent)
I have a data set consisting of a numerical response variable (y), and two factors (A with 3 levels, and ...
0
votes
0answers
12 views
Test for Homoskedasticity and endogeneity in Panel data with Pooled OLS estimation
Which tests are valid to test for:
1) Homoskedasticity
2) Endogeneity
in a Pooled OLS model built on Panel data?
0
votes
0answers
23 views
Difference between DCC copula and factor copula models
I'd like to see if I understood this correctly (probably not). Assuming I have a data set $[y_1, \dots y_d]$ of returns and I wish to model their dependence through a copula :
DCC-copula (Engle 2002)...
5
votes
1answer
37 views
Heteroscedasticity that depends on the regression parameters
Consider a vector of observations $\mathbf{Y}$ that can be modeled as
\begin{equation}
\mathbf{Y} \sim \mathcal{N}( \mathbf{H}\boldsymbol{\beta} , \boldsymbol{\Sigma} )
\end{equation}
with $\mathbf{...
1
vote
0answers
23 views
Bivariate probit : is there a heteroscedastic version of the model?
I know there exists a version of the simple probit model which is robust to heteroscedasticity (the heteroscedastic probit model). Is there an equivalent for the bivariate probit model? Is there a way ...
0
votes
0answers
21 views
Can modelling GARCH make the standardised residuals worse in terms of heteroscedasticity?
I have a number of time series' that I would like to run PCA and then K-Means clustering on in order to find groups of similar behaving variables.
To do this I am trying to first apply an ARMA-GARCH ...
1
vote
1answer
68 views
ARIMA & Conditional Heteroskedasticity
How to deal with conditional heteroskedasticity in ARIMA model?
ARCH test on ARIMA model indicates the presence of conditional heteroskedasticity and ARIMA forecasts are therefore incorrect.
Is ...
2
votes
1answer
53 views
How is homogeneity of variances in residuals a requirement for ANOVA, when ANOVA is a test that involves analysis of variance?
I know this is a basic question about ANOVA, but I don't understand why it's important to have homogeneity of variance across different groups within a factor, when you are 'analyzing the variance' to ...
0
votes
1answer
26 views
Testing for ARCH Process
I have sales data that I think would be best modeled with an ARCH model.
I believe the following is true, but I'm not sure:
If the residuals are regressed against the sales data using the equation $...
1
vote
1answer
20 views
Transformation between log and reciprocal powers for regression
Left shows the response transformed with $\frac{1}{gestation^{1/4}}$ and right shows the natural log of gestation. (Original graph of gestation vs age looked just like the transformed graph with the ...
0
votes
0answers
25 views
Finding transformation to meet linearity and homoscedasticity
In the picture below, what transformation on the response, just the predictor or both should be used to achieve linearity and homoscedasticity.
they are plots of response(gestation period in weeks) ...
0
votes
1answer
30 views
Homoskedasticity: Plotting residuals against fitted values
Does the below graph show homoscedasticity?
1
vote
0answers
28 views
Heteroscedasticity and weighted least square estimator
"In presence of heteroscedasticity, OLS estimators are unbiased but inefficient"
Showing the unbiased part is relatively easy. Some authors have explained the inefficiency with the help of new ...
1
vote
1answer
62 views
How to handle bounded [0,1] dependent variable that causes one to fail heteroscedasticity
In my particular situation, our outcome variable is recall (bounded between 0 and 1 inclusive), and we are building a linear mixed effects model in R. We end up with a qq plot like the one below:
Is ...
0
votes
0answers
19 views
Transformation for non-normally distributed homoscedastic data?
I have continuous data (latency to perform a behaviour) that is heteroscedastic and also the data and the residuals are not normally dsitributed.
I've tried
square root
$\frac{1}{log}$ and
log10 ...
1
vote
1answer
33 views
Paper for the rule of thumb for homogenity of variances
I often read about a rule of thumb, one can apply if the test of equal variances returns a significant result. Depending on the source, the proposed maximal $F$ ratio varies between 1.5 and 4, which ...
1
vote
0answers
20 views
Factorization of product distribution
I would like to obtain a factorization of the probability distribution of a random variable $Z$ which is the product of two independent random variables $\delta=\varepsilon X$ where $\varepsilon \sim ...
0
votes
1answer
31 views
The quadratic risk of beta estimate in heteroscedastic regression
I am trying to answer the following question:
Given $\vec{\epsilon} \sim N(0,\Sigma)$ vector where $\epsilon_i$ are NOT iid. Find the estimator $\hat{\beta}$ that minimises $(Y − X\beta)^T\Sigma^{−...
2
votes
0answers
31 views
Should I test for heteroskedasticity when I run unit root tests?
"The Phillips-Perron (PP) unit root tests differ from the ADF tests mainly in how they deal with serial correlation and heteroskedasticity in the errors." Zivot (2005) Modelling Financial Time Series ...
1
vote
2answers
87 views
Theil-Sen estimator assumptions
I found by accident the nonparametric Theil-Sen Estimator as a replacement for standard OLS linear Regression. How well does it perform with autocorrelated data, non-normal residuals and ...
3
votes
2answers
55 views
Is this seasonal heteroskedasticity?
Quick and possibly quite silly question:
So I have this time series, there is obviously large seasonality, with observations in winter much higher than in summer. There is also an overall inverse-U-...
1
vote
1answer
28 views
Is there heteroskedasticity in binomial GLMs?
We know a linear probability model (LPM) will produce heteroskedastic errors by definition because of how the variance of a bernoulli r.v. is defined. My question is whether the same is true for logit/...
1
vote
1answer
22 views
How to estimate cluster homoscedastic model?
This is a follow-up to "How to estimate variance for identical & fraternal twins with a mixed model" for more clarification of my understanding.
I believe there are some theoretical analogues to ...
1
vote
0answers
105 views
Closest approximation of a Poisson GLM using weighted least squares analysis to take into account mean/variance relationship
I have an application where I would like to approximate a Poisson GLM with identity link, i.e. a glm of the form
...
3
votes
0answers
29 views
Optimal block size for spatial bootstrapping
Take a regression model:
$$
y_s = X_s\beta + \epsilon
$$
Where $E[\epsilon|X] = 0$, but $cor(\epsilon_s, \epsilon_{near}) > cor(\epsilon_s, \epsilon_{far})> 0$.
In other words, $X$ is ...
0
votes
0answers
32 views
Ljung-Box test for squared residuals - first lag not statisticaly significant
I applied Ljung-Box test for squared residuals for different model specifications (interecept only; ar(1); ma(1); arma(1,1)...).
I always get that the first lag is statistically not significant with ...
0
votes
0answers
112 views
How to make forecast with confidence intervals with arma-garch model in python?
I have financial time series with non constant variance. I suppose that using ARMA- GARCH i will create more accurate confident intervals for predictions than using ARMA model. This is how i fit model
...
