459:. High-dimensional mixed frequency time series regressions involve certain data structures that once taken into account should improve the performance of unrestricted estimators in small samples. These structures are represented by groups covering lagged dependent variables and groups of lags for a single (high-frequency) covariate. To that end, the machine learning MIDAS approach exploits the sparse-group LASSO (sg-LASSO) regularization that accommodates conveniently such structures. The attractive feature of the sg-LASSO estimator is that it allows us to combine effectively the approximately sparse and dense signals.
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A MIDAS regression is a direct forecasting tool which can relate future low-frequency data with current and lagged high-frequency indicators, and yield different forecasting models for each forecast horizon. It can flexibly deal with data sampled at different frequencies and provide a direct forecast
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when applied in the context of mixed frequency data. Bai, Ghysels and Wright (2013) examine the relationship between MIDAS regressions and Kalman filter state space models applied to mixed frequency data. In general, the latter involves a system of equations, whereas, in contrast, MIDAS regressions
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with several co-authors. There is now a substantial literature on MIDAS regressions and their applications, including
Ghysels, Santa-Clara and Valkanov (2006), Ghysels, Sinko and Valkanov, Andreou, Ghysels and Kourtellos (2010) and Andreou, Ghysels and Kourtellos (2013).
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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.
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of the low-frequency variable. It incorporates each individual high-frequency data in the regression, which solves the problems of losing potentially useful information and including mis-specification.
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Andreou, Elena & Eric
Ghysels & Andros Kourtellos "Should macroeconomic forecasters use daily financial data and how?", Journal of Business and Economic Statistics 31, 240-251.
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Andreou, Elena & Eric
Ghysels & Andros Kourtellos "Regression Models with Mixed Sampling Frequencies", Journal of Econometrics, 158, 246-261.
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700:"midasml: Estimation and Prediction Methods for High-Dimensional Mixed Frequency Time Series Data maintained by Jonas Striaukas"
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Several software packages feature MIDAS regressions and related econometric methods. These include:
615:"Machine learning panel data regressions with heavy-tailed dependent data: Theory and application"
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179:{\displaystyle y_{t}=\beta _{0}+\beta _{1}B(L^{1/m};\theta )x_{t}^{(m)}+\varepsilon _{t}^{(m)},}
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Econometric models involving data sampled at different frequencies are of general interest.
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Predicting
Volatility: How to Get Most Out of Returns Data Sampled at Different Frequencies
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Babii, Andrii; Ball, Ryan T.; Ghysels, Eric; Striaukas, Jonas (2022-07-26).
566:"Machine Learning Time Series Regressions With an Application to Nowcasting"
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The regression models can be viewed in some cases as substitutes for the
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midasml, R package for High-Dimensional Mixed
Frequency Time Series Data
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424:{\displaystyle B(L^{1/m};\theta )=\sum _{k=0}^{K}B(k;\theta )L^{k/m}}
661:, Journal of Computational and Graphical Statistics, 22(2), 231-245.
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Simon, N., J. Friedman, T. Hastie, and R. Tibshirani (2013):
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Babii, Andrii; Ghysels, Eric; Striaukas, Jonas (2022-07-03).
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Ghysels, Eric, Pedro Santa-Clara and Rossen
Valkanov (2006)
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Ghysels, Eric and Arthur Sinko and Rossen
Valkanov (2006)
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Bai, Jennie and Eric
Ghysels and Jonathan Wright (2013)
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MIDAS Regressions: Further
Results and New Directions
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455:. The machine learning MIDAS regressions involve
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41:A simple regression example has the
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804:. You can help Knowledge (XXG) by
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306:{\displaystyle B(L^{1/m};\theta )}
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193:is the dependent variable,
18:Mixed-data sampling (MIDAS)
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43:independent variable
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688:. 23 February 2021.
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453:nowcasting
205:is yearly
645:0304-4076
600:0735-0015
398:θ
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319:Almon Lag
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480:EViews
189:where
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