Is it possible to do time series with multiple independent variables?

Is it possible to do time series with multiple independent variables?

Time Series with multiple independent variables is it possible to do a time series analysis with more than 1 explanatory variable? I know in the simplest (using the word simple very loosely) terms time series involves modeling Y = time, where time is essentially the only information used to try and predict Y (based on trends, seasonality….).

When do you use time series for regression?

Regression modelling goal is complicated when the researcher uses time series data since an explanatory variable may influence a dependent variable with a time lag. This often necessitates the inclusion of lags of the explanatory variable in the regression.

When is there an interaction between two independent variables?

There is one main effect for each independent variable. There is an interaction between two independent variables when the effect of one depends on the level of the other. Some of the most interesting research questions and results in psychology are specifically about interactions.

What causes strong correlation between two time series variables?

This strong correlation may be purely caused by the fact that the two time series variables have non-constant mean. This phenomenon is called spurious relationship. However, under a special circumstance, we can model time series data y using time series data x, when x and y are both I (1) process and cointegrated.

How to analyse data with multiple dependent and independent variables?

From doing individual simple linear regression I have found significance for summer rainfall and winter temperature as factors influencing my dependent variables, but I know that this isn’t very statistically viable! Is principle component analysis a suitable way of analysing this data?

When to use VARs in time series regression?

Regardings VARS; i never performed one, but you use VAR-modelling if your dependent variable is a function of your independent variable and vice versa (your independent variable values depend on your dependent variables value). I don’t think that’s the question here.

Which is more complex time series or univariate time series?

As noetsi said, it is much more complex than univariate time series. You won’t be able to jump in without reading up on the assumptions and tests necessary to perform before and after estimation. In many cases, neither variable will be clearly exogenous.