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    <title>topic Non-parametric robust t-test alternative in SAS Forecasting and Econometrics</title>
    <link>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642529#M3819</link>
    <description>&lt;DIV class="lia-message-body lia-component-message-view-widget-body lia-component-body-signature-highlight-escalation lia-component-message-view-widget-body-signature-highlight-escalation"&gt;&lt;DIV class="lia-message-body-content"&gt;&lt;P&gt;It was suggested that I also post my question in this subforum.. so:&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I want to compare two &lt;STRONG&gt;paired&lt;/STRONG&gt; short time series (say of length 10) by comparing their means.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;However, two significant issues currently prevent me from doing it:&lt;/P&gt;&lt;P&gt;(1) The data is not normally distributed and the sample sizes are really small, hence relying on the CLT appears to be quite a strong assumption.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;(2) The data is autocorrelated.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Both prevent me from doing, e.g. a paired-t test, since the assumptions of normality and i.i.d. observations are violated. I know that I can, for example, perform a non-parametric test such as the Wilcoxon-Rank-Sum test to deal with the first issue. I also know that I can deal with the second issue by, for example, calculating the paired t-test with robust standard erros. However, the Wilcoxon-Rank-Sum test still requires independence, and calculating robust standard errors still requires normality.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Put differently, I do not know how to deal with both issues at once. I would be grateful if anyone could point me towards a procedure that deals with both issues.&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;</description>
    <pubDate>Fri, 24 Apr 2020 11:39:12 GMT</pubDate>
    <dc:creator>shenflow</dc:creator>
    <dc:date>2020-04-24T11:39:12Z</dc:date>
    <item>
      <title>Non-parametric robust t-test alternative</title>
      <link>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642529#M3819</link>
      <description>&lt;DIV class="lia-message-body lia-component-message-view-widget-body lia-component-body-signature-highlight-escalation lia-component-message-view-widget-body-signature-highlight-escalation"&gt;&lt;DIV class="lia-message-body-content"&gt;&lt;P&gt;It was suggested that I also post my question in this subforum.. so:&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I want to compare two &lt;STRONG&gt;paired&lt;/STRONG&gt; short time series (say of length 10) by comparing their means.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;However, two significant issues currently prevent me from doing it:&lt;/P&gt;&lt;P&gt;(1) The data is not normally distributed and the sample sizes are really small, hence relying on the CLT appears to be quite a strong assumption.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;(2) The data is autocorrelated.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Both prevent me from doing, e.g. a paired-t test, since the assumptions of normality and i.i.d. observations are violated. I know that I can, for example, perform a non-parametric test such as the Wilcoxon-Rank-Sum test to deal with the first issue. I also know that I can deal with the second issue by, for example, calculating the paired t-test with robust standard erros. However, the Wilcoxon-Rank-Sum test still requires independence, and calculating robust standard errors still requires normality.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Put differently, I do not know how to deal with both issues at once. I would be grateful if anyone could point me towards a procedure that deals with both issues.&lt;/P&gt;&lt;/DIV&gt;&lt;/DIV&gt;</description>
      <pubDate>Fri, 24 Apr 2020 11:39:12 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642529#M3819</guid>
      <dc:creator>shenflow</dc:creator>
      <dc:date>2020-04-24T11:39:12Z</dc:date>
    </item>
    <item>
      <title>Re: Non-parametric robust t-test alternative</title>
      <link>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642617#M3820</link>
      <description>&lt;P&gt;Note that if you are contemplating a paired test, you only need the paired differences to be normally distributed.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;To account for autocorrelation, you could specify a model such as&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;MODEL myPairedDiff = / nlag=1;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;i.e. fit an intercept only model to the paired differences, in &lt;STRONG&gt;proc autoreg&lt;/STRONG&gt; (part of SAS/ETS).&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;hth&lt;/P&gt;</description>
      <pubDate>Fri, 24 Apr 2020 14:46:45 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642617#M3820</guid>
      <dc:creator>PGStats</dc:creator>
      <dc:date>2020-04-24T14:46:45Z</dc:date>
    </item>
    <item>
      <title>Re: Non-parametric robust t-test alternative</title>
      <link>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642636#M3821</link>
      <description>&lt;P&gt;Thanks for your reply. I am aware of the statement regarding the normality.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;What exactly does the model you suggested lead to?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I was thinking to estimate an intercept only model of the differences, and then computing a t-test with robust standard errors of the estimated coefficient. Since it is an intercept only model, this is basically just a paired t-test with robust standard errors. Is that what you are suggesting?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;However, the differences are NOT normally distributed and I am unsure of how to account for that.&lt;/P&gt;</description>
      <pubDate>Fri, 24 Apr 2020 15:17:13 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642636#M3821</guid>
      <dc:creator>shenflow</dc:creator>
      <dc:date>2020-04-24T15:17:13Z</dc:date>
    </item>
    <item>
      <title>Re: Non-parametric robust t-test alternative</title>
      <link>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642837#M3823</link>
      <description>&lt;P&gt;I would really appreciate a reply. Thank you.&lt;/P&gt;</description>
      <pubDate>Sat, 25 Apr 2020 06:45:53 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642837#M3823</guid>
      <dc:creator>shenflow</dc:creator>
      <dc:date>2020-04-25T06:45:53Z</dc:date>
    </item>
    <item>
      <title>Re: Non-parametric robust t-test alternative</title>
      <link>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642929#M3829</link>
      <description>&lt;P&gt;I am sure you already considered transforming your data. Beyond that, I just don't know what else to try, sorry.&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Sat, 25 Apr 2020 18:37:34 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Forecasting-and-Econometrics/Non-parametric-robust-t-test-alternative/m-p/642929#M3829</guid>
      <dc:creator>PGStats</dc:creator>
      <dc:date>2020-04-25T18:37:34Z</dc:date>
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