Lag-Augmented Two- and Three-Stage Least Squares Estimators for Integrated Structural Dynamic Models  

Oleh:

Cheng, Hsiao ; Siyan, Wang

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The Econometrics Journal vol. 10 no. 1 (2007), page 49-81.
Topik: Dynamic Model; structural vector autoregressions; non stationary time series; cointegration; hypothesis testing; two - and three - stage least squares
Fulltext: 49.pdf (407.49KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: EE39.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We consider a lag - augmented two - or three - stage least - squares estimator for a structural dynamic model of non-stationary and possibly cointegrated variables without the prior knowledge of unit roots or rank of cointegration. We show that the conventional two - and three - stage least - squares estimators are consistent but contain non - standard distributions without the strict exogeneity assumption ; hence the conventional Wald type test statistics may not be chi - square distributed. We propose a lag order augmented two - or three - stage least - squares estimator that is consistent and asymptotically normally distributed. Limited Monte Carlo studies are conducted to shed light on the finite sample properties of various estimators.

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