Derivation of the Variance of Autocorrelation

In summary, the conversation discusses the calculation of autocorrelation and its variance at a specific lag. The formula for calculating the variance is shown, but the poster is looking for a proof of this formula. They have searched for it online but have not found it.
  • #1
mertcan
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Hi everyone in this link (https://stats.stackexchange.com/questions/226334/ljung-box-finite-sample-adjustments) I see the variance of autocorrelation related to specific lag is demonstrated in the following: $$ Var(r_k) = \frac {\sum_{t=k+1}^n a_t*a_{t-k}} {\sum_{t=1}^n a_t^2}$$ where ##r_k## is autocorrelation at relevant lag, ##n## is the number of data set and ##a_t## is error. Could help me prove the formula I mentioned above?
 
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  • #2
mertcan said:
Hi everyone in this link (https://stats.stackexchange.com/questions/226334/ljung-box-finite-sample-adjustments) I see the variance of autocorrelation related to specific lag is demonstrated in the following: $$ Var(r_k) = \frac {\sum_{t=k+1}^n a_t*a_{t-k}} {\sum_{t=1}^n a_t^2}$$ where ##r_k## is autocorrelation at relevant lag, ##n## is the number of data set and ##a_t## is error. Could help me prove the formula I mentioned above?

Guys sorry for wrong question. Please let me rectify it.
I have seen the following formula whereas $$ r_k = \frac {\sum_{t=k+1}^n a_t*a_{t-k}} {\sum_{t=1}^n a_t^2}$$ $$Var(r_k) = \frac {n-k}{n*(n+2)}$$ where $r_k$ is the autocorrelation at relevant lag, $n$ is the number of points in the data set, and $a_t$ is the error.

I have searched the internet for the proof for variance equation, but I haven't found it. Could anyone help me prove the formula I mentioned above?
 

FAQ: Derivation of the Variance of Autocorrelation

What is the purpose of deriving the variance of autocorrelation?

The variance of autocorrelation is a statistical measure used to determine the strength and direction of a relationship between a variable and its past values. By deriving this measure, scientists can better understand the patterns and trends in their data and make more accurate predictions.

How is the variance of autocorrelation calculated?

The variance of autocorrelation is calculated by taking the squared difference between each data point and its lagged value, and then dividing by the total number of observations. This calculation is repeated for each lag until all possible lags have been considered.

What factors can affect the variance of autocorrelation?

There are several factors that can affect the variance of autocorrelation, including the strength and direction of the relationship between the variable and its past values, the number of observations, and the distribution of the data. Additionally, the presence of outliers or missing data can also impact the variance of autocorrelation.

How can the variance of autocorrelation be interpreted?

The variance of autocorrelation is typically interpreted as a measure of the degree of dependence between a variable and its past values. A high variance of autocorrelation indicates a strong relationship between the variable and its past values, while a low variance indicates a weak or non-existent relationship.

What are some potential applications of the variance of autocorrelation?

The variance of autocorrelation has many applications in various fields, including finance, economics, and meteorology. It can be used to analyze time series data, make predictions, and identify patterns and trends. Additionally, it is often used in quality control and process improvement to detect any systematic changes in a process over time.

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