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

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13 views

Problems when adding a variance structure into a GLMM

I did a GLMM model with proportional data using the lme4 package. This model has three categorical independent variables: Age (2 levels) Sex (2 levels) Status (2 levels) "Year" is the random ...
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1answer
41 views

How to calculate White-Huber standard errors by hand

I can't see how to replicate the calculation of WH standard errors for heteroscedastic data, as produced by the R packages sandwich / ...
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0answers
11 views

ANOVA for data does not fit the assumption of homogeneity of variance, which is better: ANOVA after ln-transformation or Welch's ANOVA

I have data, where many groups have different variance, so the data does not fit the assumption of homogeneity of variance. 1, First I ln-transformed all data, after all most of them had homogenous ...
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0answers
19 views

A comprehension question to conditional heteroscedasticity/GARCH

I have a time series with strong seasonality. At specific time periods/seasons there is also a stronger Variance than in other time periods/seasons. Is that an example of conditional ...
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2answers
41 views

Tests of heteroscedasticity in linear regression models

I am unfamiliar with the implementation used in the R package GVLMA. What are some basic tests of heteroscedasticity in linear regression models and how or where ...
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0answers
12 views

Comparing Variance Ratio tests to Hurst exponents

I have used the Chow Denning test and the Hurst exponent (Peng, Whittle and R/S methods) to examine if a particular time series follows a random walk. My results are conflicting between the 2 tests. ...
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0answers
22 views

Heteroskedasticity of error term in log-level models?

I am running a decomposition of log wages for two time periods and want to explain the variance of the error term. My question is: If there is a wage growth trend, will this automatically increase ...
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1answer
34 views

Is it important to model heteroscedasticity during multiple regression?

Given a multiple linear regression (eg. using a GLS procedure) between a response variable and several predictive variables with different, heteroscedastic relationships with the response variable and ...
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0answers
21 views

Address the problem of heteroscedasticity

I have 15 variables. The aim is to conduct Granger's causality test. I want to see whether I should Use log for all my variables. In order to check for the occurrence of heteroscedasticity, do I ...
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1answer
24 views

White's Test for heteroscedasticity Interpretation

I'm slightly confused as how to interpret the answers Stata is feeding me from the White's test. I am running two regressions: Regression 1 ...
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1answer
163 views

Fit regression model from a fan-shaped relation, in R

I get a fan-shaped scatter plot of the relation between two different quantitative variables: I am trying to fit a linear model for this relation. I think I should apply some kind of transformation ...
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0answers
26 views

Multiple comparison of non-normal, heteroscedastic data. What test should I use?

I have a set of brain pathology data. These were obtained by counting certain parameters in the brain. Due to availability of human brains, the amount of cases vary a lot across the different groups ...
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1answer
55 views

Estimating ARCH model using ML or OLS

ARCH(p) models are defined as: $σ^2 = a_0 + ∑a_ie^2_{t-i}+e_t$, $i>0$ Now, as with any VMA model, estimating this model using OLS/ML is impossible, because the error term is not observable. But ...
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0answers
26 views

Heteroscedastic censored regression

I am dealing with a heteroscedastic censored dataset. I tried to use the survival analysis package in R to estimate a linear model for it. So before doing that, I conducted a simulation study, where I ...
2
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0answers
43 views

Autocorrelation in DOLS: will HAC standard errors work?

I am currently estimating a cointegrating regression (DOLS), where my residuals have autocorrelation. Sometimes it is just in one or two lags, but sometimes it is more. My question is: Can I apply HAC ...
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0answers
30 views

Autocorrelation in squared residuals means heteroskedasticity?

I am wondering whether testing the squared residuals of a regression would provide information on whether there exists heteroskedasticity
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1answer
45 views

Are the statements “normally distributed with same mean and variance” and “identically normally distributed” equivalent?

According to Rohatgi & Saleh [1], random variables X and Y are said to be identically distributed if they have the same distribution function, i.e. $F_X$(x) = $F_Y$(y). Moreover, the ...
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0answers
36 views

Econometrics heteroscedasticity model

Consider the following model for real estate values applied to a cross-section of homes: ${\rm Price} = \beta_0 + \beta_1\cdot SQFT_i + \beta_2 \cdot YARD_i + \beta_3 \cdot POOL_i + \epsilon_i$ ...
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38 views

Non-normally distributed residuals for multivariate linear regression.Still a valid model?

I try to know if the independent variables are affecting the outcome of the dependent variable, but while the Shapiro-Wilk test shows residuals non-normally distributed, the autocorrelation of errors ...
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1answer
228 views

White's test for heteroskedasticity in R

I am trying to estimate heteroskedasticity in R. I had Eviews available in my college's lab but not at home. I have been trying to use "het.test" package and ...
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1answer
39 views

Forecasting a time series with conditional variance (heteroscedasticity) using Arima

I want to forecast a time series and have reason to believe that there are heteroscedastic errors/variance, which could be modelled with GARCH. However, I am not really interested in ...
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1answer
41 views

Can heteroskedastic residuals be justified by variance in dependent variable?

This is a very basic question and I hope it is not a duplicate. Im using a pooled regression model with a log-transformed dependent variable (electricity consumption meter values). The variance of ...
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0answers
15 views

Using Likelihood Ratio Test to deal with heteroscedastic data results in unreliable results

Suppose $Y$ and $x$ are not related. Therefore the linear regression analysis should not reject the null hypothesis ($H_0: b=0$) in $E(Y) = a+bx$. Suppose the variance in $Y$ increase with $x$ (i.e., ...
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1answer
43 views

Heteroskedasticity- is everything over for my model?

So, I've got this exponential model: Which, when tested via Pagan- Breusch, got heteroskedasticity detected. Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant ...
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2answers
151 views

Error term in linear regression

I'm reading about a linear model which is fit to an equation, $Y = \beta_0 + \beta_1X + \varepsilon$, where $B_0$ is the intercept, $B_1$ the slope, and $\varepsilon$ the error term. My question is, ...
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3answers
2k views

Why are there two spellings of “heteroskedastic” or “heteroscedastic”?

I frequently see both the spellings "heteroskedastic" and "heteroscedastic", and similarly for "homoscedastic" and "homoskedastic". There seems to be no difference in meaning between the "c" and the ...
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1answer
71 views

Breusch-Pagan Test for ARIMA Model in R

I am testing my model using the Breusch-Pagan Test, but have not been able to find anything online regarding how to calculate it for an ARIMA Model. My AR1 Model is: ...
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1answer
36 views

Two way ANCOVA with slight heteroscedasticity

I am about to perform a 2-way ANCOVA but I reject the null hypothesis in Levene's Test with a p-value of 0.023. See standard deviation and sample sizes below. I googled up and down and found people ...
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1answer
25 views

Given a regression model with heteroscedasticity, find generalized least squares estimator?

I have $Y_i=\beta_0+U_i, E(U_i)=0, var(U_i)=2log|Z_i|, cov(U_i;U_j)=0$ when $i\neq j$. Suppose there are $n$ observations on $Y_i$ and $Z_i$. How do I use this information to find the GLS estimator ...
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0answers
18 views

Wild bootstrap v Pairs and model-based bootstrap

When calculating the standard errors of coefficient from OLS/Huber/LTS/LMS regression models on a data set showing some levels of heteroskedasticity, Paired and model-based bootstrap give rouhgly ...
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0answers
139 views

Regression Specification and Relation to Conditional Expectation Function

In their book, Mostly Harmless Econometrics, Angrist & Pischke introduce regression as an approximation to the conditional expectation function (CEF). They present (p 46) this equation: ...
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150 views

Unbalanced Panel data using R - Removing outliers and heteroskedastcity

I am new in R and it’s my first time using it so I’ll appreciate the help. I am estimating income elasticity for electricity consumption using budget shares. I have data for 8 regions categorized into ...
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24 views

Why does a robust linear model fitting give a residual standard error?

The way I understood when to use a a robust linear fitting is for example when your variance is not constant (e.g. when you have heteroscedasticity as shown with a Breusch-Pagan test for example) or ...
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1answer
47 views

How can I get a reasonable residual standard error for my linear model which faces heteroscedasticity?

My goal is to get the residual standard error of my model to be as small as possible. I have a linear model lm(y~x). When I plot the standardized residual errors in function of the explanatory ...
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0answers
47 views

Heteroskedasticity and autocorrelation in simple linear regression?

While looking through a simple linear regression, I noted the presence of both heteroskedasticity and autocorrelation, and am looking to understand the consequences of each. On this project, I am not ...
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0answers
56 views

When to use Brown-Forsythe Test?

I have been researching the differences between Welch ANOVA and Brown-Forsythe Test. I know that Welch ANOVA is used for more than two groups comparing whether there is statistically meaningful ...
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0answers
39 views

Should I include weights in LME?

I have two case studies where I am looking at the influence of a trait (trait A) on mortality (m) of trees and seedlings. Following your comments on ...
2
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1answer
66 views

Best way to deal with heteroscedasticity in R?

Originally posted on stackexchange but I was told that it fits better here. I have a plot of residual values of a linear model in function of the fitted values where the heteroscedasticity is very ...
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0answers
29 views

Breusch Pagan LM vs Engel LM

I am performing an event study and have identified 101 distinct events that I am analysing. Therefore, I am running 101 independent OLS regressions (GLS is not recommended in event studies), evaluate ...
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1answer
71 views

Consequences of violating assumptions of nonlinear regression when comparing models and/or datasets

I have a question about the consequences of using non-linear regression when the data violate the assumptions of (1) homoscedasticity and (2) normal distribution. Specifically, I am wondering about ...
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2answers
51 views

Homogeneity of variance is violated for z-scores but not for raw data?

Is this a normal thing to happen or have I done something wrong in SPSS? I am using a Levene's test.
1
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1answer
43 views

Count data and heteroscedasticity

Why are count data characterized by heteroscedasticity? If this a violation of the main linear models' assumptions of homoscedasticity, does it mean that in the relevent models for count data ...
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0answers
113 views

Strucchange R package: correcting heteroskedasticity and autocorrelation [closed]

I’ve got a question concerning the R package strucchange that I use for testing and dating structural breaks in my PhD thesis. To be specific, I use the generalized fluctuation test framework with ...
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0answers
22 views

Wild bootstrap in “bordeline" case t-test

I have to compare the mean levels of a continuos variable y (ranging 1-20) subdividing my sample in two groups according to a dichotomous variables (i.e gender). Sample sizes of the two groups are ...
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0answers
34 views

Is Wild Bootstrap a good strategy in General Linear Model (ANCOVA) with Assumption Violations (both normal residuals and homoscedasticy)?

I need to perform several GLM's (i.e. ANCOVA’s, with a single continuos dependent variable and several predictors, one dichotomous and some other continuos). I was looking for both a significance on ...
3
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1answer
58 views

Little mess with heteroscedasticity in linear model in R

I have a linear regression model: model <- lm(data=df, var1~var2+var3+var4+var5+var6+var7) Hypothesis about absence of heteroscedasticity is rejected as ...
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0answers
17 views

Do I get the HRSEs from my OLS or WLS regression?

I have a multiple regression linear model which I ran a simple OLS test on. I then performed the White test and found that it was heteroskedastic. Then I performed a Weighted Least Squares ...
6
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2answers
152 views

Is OLS Asymptotically Efficient Under Heteroscedasticity

I know that OLS is unbiased but not efficient under heteroscedasticity in a linear regression setting. In Wikipedia http://en.wikipedia.org/wiki/Minimum_mean_square_error The MMSE estimator is ...
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1answer
41 views

Linear regression confidence intervals variance assumption in practice

An assumption for linear regression confidence intervals is that the variance is the same for the dependent variable for whatever of the independent variable. If in practice the variance is ...