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

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1answer
35 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
126 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
1k 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
50 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
27 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
24 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
13 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
84 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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0answers
49 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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0answers
21 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
32 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
28 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
20 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
24 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 ...
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1answer
59 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
14 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
46 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
44 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.
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1answer
36 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
63 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
18 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
24 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 ...
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1answer
56 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
10 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 ...
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2answers
127 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
31 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 ...
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1answer
47 views

When testing autoregressive conditional heteroskedasticity with GARCH do you need to include the ind. variables?

I have seen GARCH specified both ways... including the independent variables and excluding them. In the latter, only the ARCH and GARCH term remain in the specified regression equation. For testing ...
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0answers
37 views

homogeneity of variance two way model sas

How do I test for homogeneity of variance of a two way model in SAS? I am trying to run a two-way anova model.
0
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1answer
109 views

DCC GARCH - specifying ARCH and GARCH parameter matrices in Stata

The command in Stata to estimate the DCC model of two variables is: mgarch dcc ( x1 x2=, noconstant) , arch(1) garch(1) distribution(t) $$ \begin{bmatrix} ...
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1answer
31 views

How to Estimate the Error Term in a Heteroscedastic Model with Regression Through the Origin

Suppose we have a NO INTERCEPT model, $$y_i=\beta x_i+e_i$$ where $e_{i}$ follows a N(0,$\sigma^2 x_i^h$), so $e_i$ is equal in distribution to $e_{0i} x_i^{\frac{h}{2}}$, where $e_{0i}$ follows a ...
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0answers
12 views

About S/N and averaging

It is well known that Thus, in the ideal case S/N increases with the square root of the number of measurements that are averaged. (Wikipedia) The assumptions are that (1) signal and noise are ...
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1answer
65 views

Understanding Bartlett's test from SAS

I am trying interpret the results of Bartlett's test run in SAS. This is the output I get: ...
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0answers
34 views

bootstrapping a regression with autocorrelated error

I have to verify that on two variables, $X_t$ and $Y_t$ hold the followings: $Y_t=\beta \times X_t+\varepsilon_t$ and that $var(Y_t)=\gamma \times X_t^2$. In order to give evidence / support to these ...
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0answers
22 views

regression with autocorrelated errors and specific error structure

I have to fit a linear regression model that takes into account both a specific - conditional variance relationship and a regression form $y_i=a+\beta \times x+\sqrt{\gamma \times x^2}\times ...
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0answers
54 views

Normality and homogeneity

I have performed certain statistical tests (ANOVA, DMRT, t-test, etc.) assuming my data is normal as well as with homogeneous variance. Now my paper is almost accepted in a reputed journal, reviewer ...
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0answers
24 views

Distribution of test statistic in ARCH LM test

To test for ARCH effects in a time series, there is the ARCH LM test. Its test statistic is $\chi^2(n)$-distributed, where $n$ is the number of lags in the test regression. But if you have run ...
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1answer
187 views

Robust OLS standard errors (Newey-West)

I am running a simple OLS regression with HAC adjustment (i.e. Heteroschedasticity and Autocorrelation adjustment) using the following function in hac() in matlab. ...
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0answers
15 views

Hetoroskedasticity changes with similar dependent variables

I have two regressions with the same "independent variables" . The "dependent" variables is a ratio where what changes is the denominator. In other words, the two dependent variables have the same ...
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1answer
42 views

Is LIML consistent under heteroskedastic errors?

Please let the answer be yes. Suppose we have a model \begin{eqnarray} y= X \beta + \epsilon \\ X = Z \Pi + V \end{eqnarray} and we compute the LIML estimator \begin{eqnarray} \hat{\beta}_{LIML} = ...
2
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2answers
212 views

Newey-West standard errors in OLS

I am trying to compute robust coefficient estimates for OLS, using the hac() function in MATLAB (see description of function in MathWorks). In my case, I am ...
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2answers
46 views

White Test , testing for heteroscedasticity

i used a White Test for testing the homoscedasticity assumption of my linear regression I am working on. I have a problem whit the interpretation as I have a result from the test in which the p-value ...
1
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2answers
104 views

Statistical test for difference in mean of means non-normally distributed, different number of cases

I have this data generated from simulations. For each "prob" I observe the mean value of cases with "rule" = 1,2,3 or 4. So in the example below, for "prob=.05" all cases with "rule=2" have a mean ...
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1answer
45 views

Eliminating observations with big residuals in regression

I busy with a regression model that seems to have heteroscedasticity. The model has 6 independent variables and one dependent variable. I did the regression and noticed heteroscedasticity. I then ...
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1answer
120 views

Transforming data for homoscedasticity for Linear Mixed Effects models

I have a model based on a dataset that respects all linear model assumptions except for homoscedasticity. When I just ignore the problem of heteroscedasticity, the p-value, for the interaction with ...
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2answers
83 views

What are the various forms of heteroscedasticity?

Is this picture below indicating a form of heteroscedasticity? According to the definition of heteroscedasticity, heteroscedasticity exists when the variance is not the same. But here the variance is ...
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0answers
122 views

Performing a test on heteroskedasticity and the interpretation of the results

I ran a test for heteroskedasticity in the panel data I am using and got the following output (performed in Stata): ...
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0answers
83 views

Heteroscedasticity test for random effects model in Stata

I have a panel data and according to Hausman, I have to use a random effects model. I know that in Stata I can use a modified Wald test, but only with a fixed effects model. I want to know a test for ...
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0answers
61 views

(Pretty) large sample size, unequal variance, unequal sample sizes, non-normal distribution. T-test alternative?

So, I've never had all these problems at the same time before, but I have: - Likert data, from a 9 point scale - n = 317 v 177 respectively - SD = 2.08 v. .274; 1.9 v. 2.6; 2.5 v. 2.9 etc. (you get ...
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1answer
67 views

Robust option in Stata: why are the p values computed using a Student distribution?

The commonly used "robust" option in the regress command of Stata gives standard errors using the Huber-White sandwich estimators. The t statistic also uses these standard errors. However I have ...