What are Carry-over models?
Carry-over models are a type of statistical model that capture the effects of time-varying covariates. These models are particularly useful for predicting future values based on past values and can be used to assess the impact of one variable on another. They are also known as autoregressive models or transfer functions.
How do they work?
Carry-over models take into account the effects of past values of a variable on the current value of the same variable. For example, if a company’s sales are affected by the number of customers that visited the store in the past, the carry-over model would take into account the past customer numbers when predicting the current sales. The model works by taking into account the previous values of the variable and incorporating them into the current prediction. This helps to capture the effects of the time-varying covariates, such as customer numbers, on the current prediction.
Examples of Carry-over models
Carry-over models are commonly used in many different fields. Some examples include:
- Weather forecasting – Carry-over models are used to predict future weather based on past weather patterns.
- Stock market analysis – Carry-over models are used to predict future stock prices based on past stock prices.
- Economic forecasting – Carry-over models are used to predict future economic conditions based on past economic conditions.
Conclusion
Carry-over models are a powerful tool for predicting future values based on past values. They are particularly useful for predicting the impact of time-varying covariates on future values. Examples of carry-over models can be found in many different fields, from weather forecasting to stock market analysis to economic forecasting. For more information about carry-over models, please see the following links: