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Switching Regression Modeling
A switching regression model can either be used to categorize states that cannot be observed or measure the probability of transition for these unobserved states in time series. This type of regression modeling works like a clustering time series algorithm that provides the measured equation for every cluster and the likelihood of a time series falling into a cluster at a specific point in time. Switching regression is suitable for fields with states in time series that are unobservable.
Weighted Least Squares
A Weighted least-squares method is an improved ordinary least squares. In this technique, the weights, which are non-negative constants, are usually attached to the data points. The following conditions must be true for weighted least squares to be used:
- The data should not take into account the assumption of homoscedasticity. This means that the dependent variable can be classified with similar variances.
- Use weighted least squares when your focus is on a specific area like low input value. Ordinary least squares cannot concentrate on a particular area
- If you are running a process that is part of a non-linear function or logistic regression
- You are caught up in a situation where your points of data should be treated equally
A cointegration test provides information about a correlation between multiple time series in the long term. It strives to relate cases where two on multiple non-stationary time series are combined in a way that they cannot drift from the set equilibrium in the long term. A cointegration test is preferred to other tests in scenarios where an analyst wants to find out the sensitivity degree of two variables to the same equilibrium over a given period.