Estimation of MIDAS Regressions with Errors-in-the-Variables
Sukhbir Kaur, Sukhbir Singh, Kanchan Jain, Pooja Soni
TLDR
This paper proposes a consistent estimator for Mixed Data Sampling (MIDAS) regressions when variables have measurement errors, addressing inconsistency issues.
Key contributions
- Analyzes MIDAS regressions where both low and high-frequency variables contain measurement errors.
- Demonstrates that the standard profile likelihood estimator is inconsistent in such cases.
- Introduces a consistent estimator using a corrected score and profile likelihood method.
- Evaluates the estimator's small and large sample properties through Monte Carlo simulations.
Why it matters
Measurement error is a common issue in real-world data, making standard MIDAS estimators unreliable. This paper offers a consistent estimation method, crucial for accurate modeling and forecasting in fields like economics and finance.
Original Abstract
In this paper, a Mixed Data Sampling (MIDAS) model is studied when both low and high frequency variables are contaminated with measurement error. It is shown that the profile likelihood estimator becomes inconsistent in the presence of measurement error. Using the corrected score approach along with profile likelihood approach, a consistent estimator for parameters of MIDAS Measurement Error model is proposed. Small and large sample properties of the estimator are examined by performing a monte carlo simulation study and considering the effect of sample size, number of lags and profiling parameter.
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