Wilson fits a regression model for the Albitania data to use in the simulation. From the results, she determines that the stock market index return for Albitania should be expressed as
R = –0.11 + 0.23 GDP + 3.85 UNEMP + 5.10 BOND – 19.60 INF
where
R = return on the Albitania stock market index
GDP = oneyear change in the GDP
UNEMP = unemployment rate
BOND = return on the 10year government bond
INF = inflation rate
Yang tells Wilson that their analysis is not quite complete because they have failed to address several additional areas:
 They may need to add a decision tree analysis of nonsequential risk.
 The statistical distributions chosen for the inputs may not be stationary.
 The error term in the regression may not have a mean of zero.
With respect to Yang’s final concerns, the most important issue to consider is: statement 2 is the correct answer. justification is "Even when the data fit a statistical distribution in one time period, nonstationarity may cause the parameter of the distribution to change in subsequent periods… 3 is incorrect because regression forces the mean of the error term to zero by definition.
this confused me for 2 reasons

i thought stationarity is a problem associated with time series. what does it mean by “nonstationarity may cause the parameter of the distribution to change in subsequent periods” this isnt something i have come across

“3 is incorrect because regression forces the mean of the error term to zero by definition.” this is true if you are modeling a linear regression but what if the parameters are nonlinear then this wouldn’t hold right? i reread the question on this againam I supposed to interpret that this is not an issue b/c the graphs look linear?