Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models

Götz, Thomas; Hecq, Alain; Urbain, Jean‐Pierre

doi:10.1002/for.2286

Cited by 25 publications

(31 citation statements)

References 37 publications

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“…As far as the long-run term in the ECM-MIDAS model is concerned, Götz et al (2014) discuss the choice of which high-frequency observation to include in z v t−1 . It is shown that it does not make any significant difference which observation enters the long-run term, in terms of forecasting performances, as long as the structures of the short-run dynamics terms are adapted accordingly.…”

Section: Resultsmentioning

confidence: 99%

“…Although we aim to forecast GNP growth rates, it is quite possible that GNP is cointegrated with one or more of the regressors. As was shown by Götz et al (2014), disregarding (including) a long-run relationship in the presence (absence) of cointegration worsens the forecasting performance considerably. Hence, all of the models, except for (4), i.e., the autoregressive one, are in an error-correction format, in which a long-run term (z v t−1 below) is either included (labeled 'a') or excluded ('b').…”

Section: Appendix Modelsmentioning

confidence: 89%

See 1 more Smart Citation

Combining forecasts from successive data vintages: An application to U.S. growth

Götz

Hecq

Urbain

2016

International Journal of Forecasting

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Section: Resultsmentioning

confidence: 99%

Section: Appendix Modelsmentioning

confidence: 89%

Combining forecasts from successive data vintages: An application to U.S. growth

Götz

Hecq

Urbain

2016

International Journal of Forecasting

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“…It is expected to have better estimating and forecasting ability than many other conventional models (Ghysels et al, 2004). Götz, Hecq, and Urbain (2014) report that the unrestricted MIDAS model suffers from parameter proliferation for samples of relatively small size, whereas MIDAS forecasts are robust to over-parametrization. Foroni, Marcellino, and Schumacher (2015) compare unrestricted-MIDAS (U-MIDAS) with MIDAS with distributed lag functions, and find that U-MIDAS performs better than MIDAS for small differences in sampling frequencies, but not with large differing sampling frequencies.…”

Section: Introductionmentioning

confidence: 99%

Macroeconomic forecasting with mixed data sampling frequencies: Evidence from a small open economy

Tsui

Zhang

2018

Journal of Forecasting

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The aim of this study was to forecast the Singapore gross domestic product (GDP) growth rate by employing the mixed‐data sampling (MIDAS) approach using mixed and high‐frequency financial market data from Singapore, and to examine whether the high‐frequency financial variables could better predict the macroeconomic variables. We adopt different time‐aggregating methods to handle the high‐frequency data in order to match the sampling rate of lower‐frequency data in our regression models. Our results showed that MIDAS regression using high‐frequency stock return data produced a better forecast of GDP growth rate than the other models, and the best forecasting performance was achieved by using weekly stock returns. The forecasting result was further improved by performing intra‐period forecasting.

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“…These notational conventions have become standard in the mixed-frequency literature and are similar to the ones in Götz, Hecq, and Urbain (2014), Clements and Galvão (2008, 2009) or Miller (2012.…”

Section: Notationmentioning

confidence: 99%

Testing for Granger causality in large mixed-frequency VARs

Götz

Hecq

Smeekes

2016

Journal of Econometrics

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Reproduction permitted only if source is stated.ISBN 978-3-95729-217-9 (Printversion) Non-technical summary Research QuestionWhen dealing with time series sampled at various frequencies it has become common practice to directly incorporate high-frequency information into the econom(etr)ic model at hand. These specifications were first restricted to the single regression case; with the development of the (stacked) mixed-frequency vector autoregressive (MF-VAR) system (Ghysels, 2015) it is now possible to treat all series similarly and investigate causal effects between them. However, if the difference in frequencies between the series involved is large (as, e.g., in a month/working day scenario), estimation accuracy of the system coefficients is exacerbated, implying the detection of causal effects to be potentially inaccurate. To overcome this issue various parameter reduction techniques are introduced and analyzed. These methods are then evaluated in terms of their ability to detect causality patterns between the series under consideration in the resulting restricted model. ContributionTwo parameter reduction techniques are discussed in detail: three reduced rank regression (RRR) model variants and a Bayesian MF-VAR. Using a Monte Carlo experiment both approaches are compared in terms of their Granger causality testing behavior with an unrestricted VAR, a (time-aggregated) low-frequency VAR and the max-test (Ghysels, Motegi, and Hill, 2015a). To further enhance their finite sample properties we develop and evaluate (whenever possible) two bootstrap variants of these tests. Finally, the methods are applied to U.S. data by investigating channels of causality between the monthly growth rate of the industrial production index (IPI) and daily bipower variation (BV) of the S&P500 index. ResultsWe find that, depending on the direction of causality under consideration, a different set of tests results in the best Granger non-causality testing behavior. For the direction from the high-to the low-frequency series, standard testing within the Bayesian MF-VAR, the max-test and the restricted bootstrap version of the Wald test in two RRR model versions performs best. For the reverse direction, the unrestricted bootstrap variants of the Bonferroni-corrected Wald tests within the unrestricted VAR and the RRR models dominate. As far as our application is concerned, Granger causality from BV to IPIgrowth is clearly supported by the data; evidence for causality in the reverse direction, however, only comes from a subset of tests. Maastricht UniversityAbstract We analyze Granger causality testing in a mixed-frequency VAR, where the difference in sampling frequencies of the variables is large. Given a realistic sample size, the number of high-frequency observations per low-frequency period leads to parameter proliferation problems in case we attempt to estimate the model unrestrictedly. We propose several tests based on reduced rank restrictions, and implement bootstrap versions to account for the uncertainty when estimating fac...

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Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models

Cited by 25 publications

References 37 publications

Combining forecasts from successive data vintages: An application to U.S. growth

Combining forecasts from successive data vintages: An application to U.S. growth

Macroeconomic forecasting with mixed data sampling frequencies: Evidence from a small open economy

Testing for Granger causality in large mixed-frequency VARs

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