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 = one-year change in the GDP
UNEMP = unemployment rate
BOND = return on the 10-year 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 non-sequential 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, non-stationarity 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
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i thought stationarity is a problem associated with time series. what does it mean by “non-stationarity may cause the parameter of the distribution to change in subsequent periods”- this isnt something i have come across
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“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 non-linear then this wouldn’t hold right? i re-read the question on this again-am I supposed to interpret that this is not an issue b/c the graphs look linear?