Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 166 - 181 |
| Number of pages | 16 |
| Journal | Journal of Econometrics |
| Volume | 188 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 |
Cite this
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Structural-break models under mis-specification: Implications for forecasting. / Koo, Bonsoo; Seo, Myung Hwan.
In: Journal of Econometrics, Vol. 188, No. 1, 2015, p. 166 - 181.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Structural-break models under mis-specification: Implications for forecasting
AU - Koo, Bonsoo
AU - Seo, Myung Hwan
PY - 2015
Y1 - 2015
N2 - This paper revisits the least squares estimator of the linear regression with a structural break. We view the model as an approximation to the true data generating process whose exact nature is unknown but perhaps changing over time either continuously or with some jumps. This view is widely held in the forecasting literature and under this view, the time series dependence property of all the observed variables is unstable as well. We establish that the rate of convergence of the estimator to a properly defined limit is at most the cube root of TT, where TT is the sample size, which is much slower than the standard super consistent rate. We also provide an asymptotic distribution of the estimator and that of the Gaussian quasi likelihood ratio statistic for a certain class of true data generating processes. We relate our finding to current forecast combination methods and propose a new averaging scheme. Our method compares favourably with various contemporary forecasting methods in forecasting a number of macroeconomic series.
AB - This paper revisits the least squares estimator of the linear regression with a structural break. We view the model as an approximation to the true data generating process whose exact nature is unknown but perhaps changing over time either continuously or with some jumps. This view is widely held in the forecasting literature and under this view, the time series dependence property of all the observed variables is unstable as well. We establish that the rate of convergence of the estimator to a properly defined limit is at most the cube root of TT, where TT is the sample size, which is much slower than the standard super consistent rate. We also provide an asymptotic distribution of the estimator and that of the Gaussian quasi likelihood ratio statistic for a certain class of true data generating processes. We relate our finding to current forecast combination methods and propose a new averaging scheme. Our method compares favourably with various contemporary forecasting methods in forecasting a number of macroeconomic series.
U2 - 10.1016/j.jeconom.2015.03.046
DO - 10.1016/j.jeconom.2015.03.046
M3 - Article
VL - 188
SP - 166
EP - 181
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 1
ER -