In the presence of heteroskedasticity, is quantile regression more appropiate than OLS? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)When is quantile regression worse than OLS?Quantile regression and heteroscedasticity/autocorrelationHelp clarify the implication of linearity in an Ordinary Least Squares (OLS) RegressionHeteroskedasticity in my regression model?Terminology for regression with more than 1 independent variable and more than 1 dependent variable?Non-normality in OLS regressionWhat are the assumptions for applying a quantile regression model?Predicting values using quantile regressionheteroskedasticity and quantile regressionWhat are the advantages of linear regression over quantile regression?
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In the presence of heteroskedasticity, is quantile regression more appropiate than OLS?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)When is quantile regression worse than OLS?Quantile regression and heteroscedasticity/autocorrelationHelp clarify the implication of linearity in an Ordinary Least Squares (OLS) RegressionHeteroskedasticity in my regression model?Terminology for regression with more than 1 independent variable and more than 1 dependent variable?Non-normality in OLS regressionWhat are the assumptions for applying a quantile regression model?Predicting values using quantile regressionheteroskedasticity and quantile regressionWhat are the advantages of linear regression over quantile regression?
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..for understanding the relationship between a dependent and independent variables, given that quantile regression makes no assumptions about the distribution of the residual.
multiple-regression least-squares inference heteroscedasticity
asked 6 hours ago
StatsScaredStatsScared
323516
$endgroup$
add a comment |
$begingroup$
..for understanding the relationship between a dependent and independent variables, given that quantile regression makes no assumptions about the distribution of the residual.
multiple-regression least-squares inference heteroscedasticity
asked 6 hours ago
StatsScaredStatsScared
323516
$endgroup$
add a comment |
$begingroup$
..for understanding the relationship between a dependent and independent variables, given that quantile regression makes no assumptions about the distribution of the residual.
multiple-regression least-squares inference heteroscedasticity
asked 6 hours ago
StatsScaredStatsScared
323516
$endgroup$
..for understanding the relationship between a dependent and independent variables, given that quantile regression makes no assumptions about the distribution of the residual.
multiple-regression least-squares inference heteroscedasticity
multiple-regression least-squares inference heteroscedasticity
asked 6 hours ago
StatsScaredStatsScared
323516
asked 6 hours ago
StatsScaredStatsScared
323516
asked 6 hours ago
StatsScaredStatsScared
323516
asked 6 hours ago
StatsScaredStatsScared
323516
asked 6 hours ago
StatsScaredStatsScared
323516
323516
add a comment |
add a comment |
1 Answer
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If you are really interested in determining how the conditional mean value of the dependent variable varies with the independent variables, then you would address this question by using:
- Ordinary least squares regression in the absence of heteroskedasticity;
- Generalized least squares regression or weighted least squares regression in the presence of heteroskedasticity.
On the other hand, if you are interested in determining how the quantiles of the conditional distribution of the dependent variable vary with the independent variables, then you would address that via quantile regression.
All of these regression techniques target some aspect(s) of the conditional distribution of the dependent variable given the independent variables. Usually, you would choose the aspect relevant to your study based on subject matter considerations.
In some cases, focusing on a single aspect of that distribution (e.g., conditional mean or conditional median) is sufficient given the study purposes.
In other cases, a more comprehensive look at the entire conditional distribution is necessary, which can be obtained by focusing on an appropriately selected set of quantiles of that distribution.
So what is appropriate depends primarily on the study question, though it also has to take into account features present in the data used to elucidate this question, such as presence/absence of heteroscedasticity when the study question involves the conditional mean of the dependent variable. Note that, if the study question concerns quantiles of the conditional distribution of the dependent variable, then quantile regression is appropriate whether or not heteroskedasticity is present.
answered 5 hours ago
Isabella GhementIsabella Ghement
8,0581422
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1 Answer
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1 Answer
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$begingroup$
If you are really interested in determining how the conditional mean value of the dependent variable varies with the independent variables, then you would address this question by using:
- Ordinary least squares regression in the absence of heteroskedasticity;
- Generalized least squares regression or weighted least squares regression in the presence of heteroskedasticity.
On the other hand, if you are interested in determining how the quantiles of the conditional distribution of the dependent variable vary with the independent variables, then you would address that via quantile regression.
All of these regression techniques target some aspect(s) of the conditional distribution of the dependent variable given the independent variables. Usually, you would choose the aspect relevant to your study based on subject matter considerations.
In some cases, focusing on a single aspect of that distribution (e.g., conditional mean or conditional median) is sufficient given the study purposes.
In other cases, a more comprehensive look at the entire conditional distribution is necessary, which can be obtained by focusing on an appropriately selected set of quantiles of that distribution.
So what is appropriate depends primarily on the study question, though it also has to take into account features present in the data used to elucidate this question, such as presence/absence of heteroscedasticity when the study question involves the conditional mean of the dependent variable. Note that, if the study question concerns quantiles of the conditional distribution of the dependent variable, then quantile regression is appropriate whether or not heteroskedasticity is present.
answered 5 hours ago
Isabella GhementIsabella Ghement
8,0581422
$endgroup$
add a comment |
$begingroup$
If you are really interested in determining how the conditional mean value of the dependent variable varies with the independent variables, then you would address this question by using:
- Ordinary least squares regression in the absence of heteroskedasticity;
- Generalized least squares regression or weighted least squares regression in the presence of heteroskedasticity.
On the other hand, if you are interested in determining how the quantiles of the conditional distribution of the dependent variable vary with the independent variables, then you would address that via quantile regression.
All of these regression techniques target some aspect(s) of the conditional distribution of the dependent variable given the independent variables. Usually, you would choose the aspect relevant to your study based on subject matter considerations.
In some cases, focusing on a single aspect of that distribution (e.g., conditional mean or conditional median) is sufficient given the study purposes.
In other cases, a more comprehensive look at the entire conditional distribution is necessary, which can be obtained by focusing on an appropriately selected set of quantiles of that distribution.
So what is appropriate depends primarily on the study question, though it also has to take into account features present in the data used to elucidate this question, such as presence/absence of heteroscedasticity when the study question involves the conditional mean of the dependent variable. Note that, if the study question concerns quantiles of the conditional distribution of the dependent variable, then quantile regression is appropriate whether or not heteroskedasticity is present.
answered 5 hours ago
Isabella GhementIsabella Ghement
8,0581422
$endgroup$
add a comment |
$begingroup$
If you are really interested in determining how the conditional mean value of the dependent variable varies with the independent variables, then you would address this question by using:
- Ordinary least squares regression in the absence of heteroskedasticity;
- Generalized least squares regression or weighted least squares regression in the presence of heteroskedasticity.
On the other hand, if you are interested in determining how the quantiles of the conditional distribution of the dependent variable vary with the independent variables, then you would address that via quantile regression.
All of these regression techniques target some aspect(s) of the conditional distribution of the dependent variable given the independent variables. Usually, you would choose the aspect relevant to your study based on subject matter considerations.
In some cases, focusing on a single aspect of that distribution (e.g., conditional mean or conditional median) is sufficient given the study purposes.
In other cases, a more comprehensive look at the entire conditional distribution is necessary, which can be obtained by focusing on an appropriately selected set of quantiles of that distribution.
So what is appropriate depends primarily on the study question, though it also has to take into account features present in the data used to elucidate this question, such as presence/absence of heteroscedasticity when the study question involves the conditional mean of the dependent variable. Note that, if the study question concerns quantiles of the conditional distribution of the dependent variable, then quantile regression is appropriate whether or not heteroskedasticity is present.
answered 5 hours ago
Isabella GhementIsabella Ghement
8,0581422
$endgroup$
If you are really interested in determining how the conditional mean value of the dependent variable varies with the independent variables, then you would address this question by using:
- Ordinary least squares regression in the absence of heteroskedasticity;
- Generalized least squares regression or weighted least squares regression in the presence of heteroskedasticity.
On the other hand, if you are interested in determining how the quantiles of the conditional distribution of the dependent variable vary with the independent variables, then you would address that via quantile regression.
All of these regression techniques target some aspect(s) of the conditional distribution of the dependent variable given the independent variables. Usually, you would choose the aspect relevant to your study based on subject matter considerations.
In some cases, focusing on a single aspect of that distribution (e.g., conditional mean or conditional median) is sufficient given the study purposes.
In other cases, a more comprehensive look at the entire conditional distribution is necessary, which can be obtained by focusing on an appropriately selected set of quantiles of that distribution.
So what is appropriate depends primarily on the study question, though it also has to take into account features present in the data used to elucidate this question, such as presence/absence of heteroscedasticity when the study question involves the conditional mean of the dependent variable. Note that, if the study question concerns quantiles of the conditional distribution of the dependent variable, then quantile regression is appropriate whether or not heteroskedasticity is present.
answered 5 hours ago
Isabella GhementIsabella Ghement
8,0581422
answered 5 hours ago
Isabella GhementIsabella Ghement
8,0581422
answered 5 hours ago
Isabella GhementIsabella Ghement
8,0581422
answered 5 hours ago
Isabella GhementIsabella Ghement
8,0581422
8,0581422
add a comment |
add a comment |
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