I have data that explores the relationship between Port activity (TEU) and industrial real estate vacancy/availability (avail). Availability is not a stationary variable, so I have taken the first difference to find it as stationary. My data are quarterly.
My model has the difference in log-availability as a function of the first lag in log-teu.
I've specified the following regression and with the following results
The coefficient on l1.ln_availability is b = -0.143
How do I interpret the coefficient in l1.ln_teu ? Is this a standard elasticity? ("A one percent increase in TEU is associated with a 0.143% decrease in availability in a subsequent quarter")?
Or is there a different interpretation given that the Y variable is a first difference?
Thanks!
My model has the difference in log-availability as a function of the first lag in log-teu.
I've specified the following regression and with the following results
Code:
arima d.ln_avail l.ln_teu l.ln_avail if tin(2002q1,), ar(1)
How do I interpret the coefficient in l1.ln_teu ? Is this a standard elasticity? ("A one percent increase in TEU is associated with a 0.143% decrease in availability in a subsequent quarter")?
Or is there a different interpretation given that the Y variable is a first difference?
Thanks!
Code:
clear **SET YOUR WORKING DIRECTORY cd "C:\yourdirectory" use "baltportsindustrial.dta", clear **set TS** tsset qdate, quarterly **check ac in Y variable*** ac avail ac ln_avail ac d.ln_avail **regression** arima d.ln_avail l.ln_teu l.ln_avail if tin(2002q1,), ar(1) **tests for staionarity in residuals** predict resid dfuller resid, nocons lags(2) dfuller resid, nocons
