Open AccessInstrumental variables estimation of stationary and non‐stationary cointegrating regressions
Author(s) -
Robinson P. M.,
Gerolimetto M.
Publication year - 2006
Publication title -
the econometrics journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.861
H-Index - 36
eISSN - 1368-423X
pISSN - 1368-4221
DOI - 10.1111/j.1368-423x.2006.00186.x
Subject(s) - instrumental variable , econometrics , mathematics , ordinary least squares , estimation , cointegration , uncorrelated , statistics , least squares function approximation , sample (material) , stationary process , economics , chemistry , management , chromatography , estimator
Summary Instrumental variables estimation is classically employed to avoid simultaneous equations bias in a stable environment. Here we use it to improve upon ordinary least‐squares estimation of cointegrating regressions between non‐stationary and/or long memory stationary variables where the integration orders of regressor and disturbance sum to less than 1, as happens always for stationary regressors, and sometimes for mean‐reverting non‐stationary ones. Unlike in the classical situation, instruments can be correlated with disturbances and/or uncorrelated with regressors. The approach can also be used in traditional non‐fractional cointegrating relations. Various choices of instrument are proposed. Finite sample performance is examined.