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Refers to the conditions under which a statistics procedure yields valid estimates and/or inference. E.g., many statistical techniques require the assumption that the data are randomly sampled in some way. Theoretical results about estimators usually require assumptions about the data generating mechanism.

7 votes

What should I check for normality: raw data or residuals?

There are no assumptions necessary about the original data. …
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6 votes

How can I check if my time series data is zero mean, stationary and independent identically ...

The errors from the model should have a zero mean or a mean that is not significantly different from zero everywhere. (1) In practice this means no Pulses, no Level/Step shifts , no seasonal pulses an …
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1 vote

What are the consequences of not meeting the assumptions for the residuals of ARIMA model?

To begin with your data is discrete ( fixed # of values that can arise e.g. hour in the day ) while the ADF test requires a large number of assumptions one of which is the data is continuous ( large # … I will leave it others to possibly flesh this out but I would like to subsequentally know the critical assumptions that being made. …
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0 votes

Rationale for fitted vs residuals plot

In my opinion "examining fitted vs residuals" is a graphical alternative to the Box-Cox test When (and why) should you take the log of a distribution (of numbers)? which can help to evaluate the hypot …
IrishStat's user avatar
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11 votes

What are the assumptions of ARIMA/Box-Jenkins modeling for forecasting time series?

seasonal dummies There are no one time anomalies The model parameters are constant over time The error process is homoscedastic (constant) over time Most software solutions proceed to ignore all of these assumptions
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