Basic Statistics and Data Analysis

Lecture notes, MCQS of Statistics

The Correlogram

A correlogram is a graph used to interpret a set of autocorrelation coefficients in which $r_k$ is plotted against the $log k$. A correlogram is often very helpful for visual inspection. Some general advice to interpret the correlogram are:

  • A Random Series: If a time series is completely random, then for large $N$, $r_k \cong 0$ for all non-zero value of $k$. A random time series $r_k$ is approximately $N\left(0, \frac{1}{N}\right)$. If a time series is random, let 19 out of 20 of the values of $r_k$ can be expected to lie between $\pm \frac{2}{\sqrt{N}}$. However, plotting the first 20 values of $r_k$, one can expect to find one significant value on average even when time series is really random.
  • Short-term Correlation: Stationary series often exhibit short term correlation characterized by a fairly large value of $r_1$ followed by 2 or 3 more coefficients (significantly greater than zero) tend to get successively smaller value of $r_k$ for larger lags tends to get be approximately zero. A time series which give rise to such a correlogram is one for which an observation above the mean tends to be followed by one or more further observations above the mean and similarly for observation below the mean. A model called an autoregressive model, may be appropriate for series of this type.
  • Alternating Series: If a time series has a tendency to alternate with successive observations on different sides of the overall mean, then the correlogram also tends to alternate. The value of $r_1$ will be negative, however, the value of $r_2$ will be positive as observation at lag 2 will tend to be on the same side of the mean.
  • Non-Stationary Series: If a time series contains a trend, then the value of $r_k$ will not come down to zero except for very large values of the lags. This is because by a large number of further observations on the same side of the mean because of the trend. The sample autocorrelation function $\{ r_k \}$ should only be calculated for stationary time series and no any tend should be removed before calculating $\{ r_k\}$.
  • Seasonal Fluctuations: If a time series contains a seasonal fluctuation then the correlogram will also exhibit an oscillation at the same frequency. If $x_t$ follows a sinusoidal patterns then so does $r_k$.
    $x_t=a\, cos\, t\, w, $ where $a$ is constant, $w$ is frequency such that $0 < w < \pi$. Therefore $r_k \cong cos\, k\, w$ for large $N$.
    If the seasonal variation is removed from seasonal data then the correlogram may provide useful information.
  • Outliers: If a time series contains one or more outliers the correlogram may be seriously affected. If there is one outlier in the time series and it is not adjusted, then the plot of $x_y$ vs $x_{t+k}$ will contain two extreme points, which will tend to depress the sample correlation coefficients towards zero. If there are two outliers, this effect is more noticeable.
  • General Remarks: Experience is required to interpret autocorrelation coefficients. We need to study the probability theory of stationary series and the classes of model too. We also need to know the sampling properties of $x_t$.
Updated: Jun 4, 2016 — 12:56 am

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Muhammad Imdadullah

Student and Instructor of Statistics and business mathematics. Currently Ph.D. Scholar (Statistics), Bahauddin Zakariya University Multan. Like Applied Statistics and Mathematics and Statistical Computing. Statistical and Mathematical software used are: SAS, STATA, GRETL, EVIEWS, R, SPSS, VBA in MS-Excel. Like to use type-setting LaTeX for composing Articles, thesis etc.

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  1. Dr i ran an analysis were some data are stationary while other are not but somebody said i cant use both stationary and no stationary data what can i do, but at levels some data were stationary. what is your advice

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