词条 | Mixed-data sampling |
释义 |
A simple regression example has the independent variable appearing at a higher frequency than the dependent variable: where y is the dependent variable, x is the regressor, m denotes the frequency – for instance if y is yearly is quarterly – is the disturbance and is a lag distribution, for instance the Beta function or the Almon Lag. The regression models can be viewed in some cases as substitutes for the Kalman filter when applied in the context of mixed frequency data. Bai, Ghysels and Wright (2010) examine the relationship between MIDAS regressions and Kalman filter state space models applied to mixed frequency data. In general, the latter involve a system of equations, whereas in contrast MIDAS regressions involve a (reduced form) single equation. As a consequence, MIDAS regressions might be less efficient, but also less prone to specification errors. In cases where the MIDAS regression is only an approximation, the approximation errors tend to be small. See also
References1. ^Andreou, Elena & Eric Ghysels & Andros Kourtellos "Regression Models with Mixed Sampling Frequencies", Journal of Econometrics, 158, 246-261. 2. ^Andreou, Elena & Eric Ghysels & Andros Kourtellos "Should macroeconomic forecasters use daily financial data and how?", Journal of Business and Economic Statistics 31, 240-251.
External links
3 : Econometric modeling|Time series models|Statistical forecasting |
随便看 |
|
开放百科全书收录14589846条英语、德语、日语等多语种百科知识,基本涵盖了大多数领域的百科知识,是一部内容自由、开放的电子版国际百科全书。