Backtesting significance of interaction variable

[Matthias Keer]

Hi all,

Currently in the works of writing my thesis where one of the regressions is the following:


rhp = Real House Prices
shock = Start of unconventional monetary policy
hsr = housing supply
hhdi = household income
mr = mortgage rate
unem = unemployment
hhd = household debt

Basically I am testing whether UMP had an effect on house prices in EZ when accounting for the housing supply (which seems to be the case). However, for the period Q1 2010 - Q1 2021 (shock = Q1 2015) I want to research in which quarter the variable became significant sort of in a backtesting manner. I used the following code, but it drops all variables due to collinearity (I assume the collinearity between quarters):

gen significance = .

forval i = 1/44 {
local quarter = 200 + `i'

xtreg rhp hsr shock hsrxshock hhdi mr unem hhd if quarter == `quarter', fe

// Check the significance of the interaction term
local t_statistic_of_interaction = _b[hsrxshock]
if abs(`t_statistic_of_interaction') > 1.96 {
replace significance = `i' if missing(significance)
}
}

Anyone has any ideas on how to determine how I can test in which quarter the interaction variable became significant?

Many thanks!

Matthias

[Carlo Lazzaro]

Matthias:
why not cnsidering an interaction instead?:

xtreg rhp hsr shock hsrxshock hhdi mr i.unem##i.quarter hhd, fe

Te recommendation about using -fvvarlist- notation holds for -hsrxshock-, too.
As an aside, please note that the -fe- estimator wipes out time-invariant variables.