# Autocorrelation Error Interpretation

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• pham anh
In summary, The conversation discusses using the Cochrane-Orcutt two-step procedure to fix autocorrelation in an OLS model, resulting in a GLS model. The individual is unsure how to interpret the coefficients in the new model and requests help. There is also a correction made to the equation and a suggestion to use GMM instead of GLS.
pham anh
Hello, I have a question on Autocorrelation of OLS model.
So I encountered a autocorrelation error, and use Cochrane - Orcutt Two-step Procedure(CORC) to fix it. And my OLS became GLS. And I have no idea how to interpret the coefficient on my initial X and Y with the new model. I mean something like, if X rise by 1 unit then Y rise by 3 units. Below are my real output
fyi: Y is outputs per hour ( productivity), X1 is wages/ week, X2 is working hours/week

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Is the left hand side of the GLS autocorrelation fixed model correct? Shouldn't that be ##Y-0.71*Y(-1)##?
(see https://en.wikipedia.org/wiki/Cochrane–Orcutt_estimation )

And a small correction: 248*(1-0.71) = 71.92

With that in mind, the equation only gives you values for the change of ##Y## based on the change of the ##X_i##s. So if you want to apply it to current values of ##Y## and ##X_i##s, you would have to include the data from the prior time ( ##Y(-1)## and ##X_i(-1)##s ).

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## What is autocorrelation error?

Autocorrelation error, also known as serial correlation, is a type of error that occurs when the errors in a statistical model are correlated with each other over time or across observations. This can result in misleading or inaccurate conclusions about the relationship between variables.

## What causes autocorrelation error?

Autocorrelation error can be caused by a variety of factors, such as missing variables in a model, incorrect functional form, or omitted time series effects. It can also be the result of a non-random sample or a violation of the assumptions of the statistical model being used.

## How does autocorrelation error affect the results of a statistical analysis?

Autocorrelation error can lead to biased estimates, inflated standard errors, and incorrect significance levels. This can result in false conclusions about the relationships between variables and can also lead to incorrect predictions and forecasts.

## How can autocorrelation error be detected?

There are several methods for detecting autocorrelation error, including graphical methods such as scatter plots and residual plots, as well as statistical tests like the Durbin-Watson test and the Breusch-Godfrey test. These methods can help identify the presence and magnitude of autocorrelation in a dataset.

## How can autocorrelation error be addressed or corrected?

If autocorrelation is detected, there are several techniques that can be used to address or correct it. These include including additional variables in the model, using different functional forms, and using time series methods such as differencing or autoregressive models. It is important to carefully consider the underlying causes of autocorrelation and select the appropriate method for addressing it in each specific case.

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