Index splicing serves to

• A. Creating a new base/clean state to compare things on
• B. Extending the index to more items
• C. Adjusting for seasonality
• D. Removing older data

Answer: (A)

Index splicing creates a new base or clean state to compare things on. When you’re combining or updating index data that may use different base values or scales, you need a common reference point so the movements you observe are meaningful. Splicing sets a chosen base period and aligns the subsequent values so the index remains continuous across the splice. That shared base lets you compare performance over time or across different data sources on a like-for-like footing. It isn’t about expanding the number of items in the index, nor about adjusting for seasonality, nor about discarding older observations.