The **Ljung-Box test**, named after statisticians Greta M. Ljung and George E.P. Box, is a statistical test that checks if autocorrelation exists in a time series.

The Ljung-Box test is used widely in econometrics and in other fields in which time series data is common.

**The Basics of the Ljung-Box Test**

Here are the basics of the Ljung-Box test:

**Hypotheses**

The Ljung-Box test uses the following hypotheses:

**H _{0}:** The residuals are independently distributed.

**H _{A}:** The residuals are not independently distributed; they exhibit serial correlation.

Ideally, we would like to fail to reject the null hypothesis. That is, we would like to see the p-value of the test be greater than 0.05 because this means the residuals for our time series model are independent, which is often an assumption we make when creating a model.

**Test Statistic**

The test statistic for the Ljung-Box test is as follows:

**Q** = n(n+2) Σp_{k}^{2} / (n-k)

where:

**n** = sample size

Σ = a fancy symbol that means “sum” and is taken as the sum of 1 to *h*, where *h *is the number of lags being tested.

**p _{k}** = sample autocorrelation at lag

*k*

**Rejection Region**

The test statistic *Q* follows a chi-square distribution with *h *degrees of freedom; that is, Q ~ X^{2}(h).

We reject the null hypothesis and say that the residuals of the model are not independently distributed if Q > X^{2}_{1-α, h}

**Example: How to Conduct a Ljung-Box Test in R**

To conduct a Ljung-Box test in R for a given time series, we can use the **Box.test()** function, which uses the following notation:

**Box.test**(x, lag =1, type=c(“Box-Pierce”, “Ljung-Box”), fitdf = 0)

where:

**x:**A numeric vector or univariate time series**lag:**Specified number of lags**type:**Test to be performed; options include Box-Pierce and Ljung-Box**fitdf: bD**egrees of freedom to be subtracted if x is a series of residuals

The following example illustrates how to perform the Ljung-Box test for an arbitrary vector of 100 values that follow a normal distribution with mean = 0 and variance = 1:

#make this example reproducible set.seed(1) #generate a list of 100 normally distributed random variables data <- rnorm(100, 0, 1) #conduct Ljung-Box test Box.test(data, lag = 10, type = "Ljung")

This generates the following output:

Box-Ljung test data: data X-squared = 6.0721, df = 10, p-value = 0.8092

The test statistic of the test is Q = **6.0721** and the p-value of the test is **0.8092**, which is much larger than 0.05. Thus, we fail to reject the null hypothesis of the test and conclude that the data values are independent.

Note that we used a lag value of 10 in this example, but you can choose any value that you would like to use for the lag, depending on your particular situation.