## How do you carry out cross-correlation analysis in SPSS?

To produce a cross-correlation function for two time series variables in SPSS, start by selecting from the menu: Analyze → Forecasting → Cross-Correlations This is shown in Figure 1. Figure 1: Selecting the Cross-Correlation function from the menu in SPSS. This opens the Cross-Correlations dialog box.

**What is cross-correlation in time series?**

Cross-correlation of stochastic processes In time series analysis and statistics, the cross-correlation of a pair of random process is the correlation between values of the processes at different times, as a function of the two times.

**What does lag mean in cross-correlation?**

The lag refers to how far the series are offset, and its sign determines which series is shifted. Note that as the lag increases, the number of possible matches decreases because the series “hang out” at the ends and do not overlap.

### What is lag in SPSS?

In SPSS, LAG is a function that returns the value of a previous case. It’s mostly used on data with multiple rows of data per respondent. Here it comes in handy for calculating cumulative sums or counts.

**What does cross-correlation do?**

Cross-correlation is a measurement that tracks the movements of two or more sets of time series data relative to one another. It is used to compare multiple time series and objectively determine how well they match up with each other and, in particular, at what point the best match occurs.

**Is cross-correlation associative?**

Correlation is not associative – it is mostly used in matching, where we do not need to combine different filters.

#### How do you calculate lag time?

Time = Distance / Speed Vehicle A would take 10 hours to travel 500 miles, but Vehicle B would take 20 hours. The lag time here is 10 hours. So, the pattern you should note here is “the greater the distance, the longer the lag time.”

**What is a cross lagged panel design?**

a study of the relationships between two or more variables across time in which one variable measured at an earlier point in time is examined with regard to a second variable measured at a later point in time, and vice versa.