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

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15 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)...
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9 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 ...
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7 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 ...
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0answers
61 views

problem with heteroskedasticity

After performing a regression by newey west standard errors ( perform an OLS regression in time series data) when data is not autocorrelated, is there a need for heteroskedasticity test?
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22 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 ...
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1answer
26 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 ...
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1answer
20 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?
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0answers
11 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}...
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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 ...
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26 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 ...
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1answer
69 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 ...
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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 ...
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14 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 ...
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0answers
31 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, ...
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0answers
29 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 ...
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1answer
74 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 ...
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1answer
34 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 ...
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0answers
20 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 ...
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38 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 ...
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2answers
49 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 ...
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32 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 ...
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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....
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1answer
38 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 ...
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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 ...
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0answers
37 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 ...
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1answer
50 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 ...
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1answer
213 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 ...
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0answers
30 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 ...
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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 ...
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0answers
10 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 ...
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2answers
99 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 ...
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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 ...
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14 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,...
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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 ...
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0answers
22 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 ...
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30 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 = \...
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0answers
30 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 ...
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0answers
15 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?
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29 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)...
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1answer
47 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{...
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0answers
26 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 ...
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0answers
24 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 ...
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1answer
86 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 ...
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1answer
57 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 ...
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1answer
29 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 $...
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1answer
26 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 ...
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28 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) ...
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1answer
33 views
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30 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 ...
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1answer
85 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 ...