Moment Approximation for Least-Squares Estimators in Dynamic Regression Models With A Unit Root  

Oleh:

Kiviet, Jan F. ; Phillips, Garry D.A.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The Econometrics Journal vol. 8 no. 2 (2005), page 115-142.
Topik: REGRESSION; asymptotic expansions; dynamic regression; finite sample bias; moment approximation; small - drift asymptotics; unit root
Fulltext: 115.pdf (219.34KB)

Ketersediaan

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

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Isi artikel

To find approximations for bias, variance and mean - squared error of least - squares estimators for all coefficients in a linear dynamic regression model with a unit root, we derive asymptotic expansions and examine their accuracy by simulation. It is found that in this particular context useful expansions exist only when the autoregressive model contains at least one non-redundant exogenous explanatory variable. Surprisingly, the large - sample and small - disturbance asymptotic techniques give closely related results, which is not the case in stable dynamic regression models. We specialize our general expressions for moment approximations to the case of the random walk with drift model and find that they are unsatisfactory when the drift is small. Therefore, we develop what we call small - drift asymptotics which proves to be very accurate, especially when the sample size is very small.

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