Famidas: A Mixed Frequency Factor Model with MIDAS Structure

Frale, Cecilia; Monteforte, Libero

doi:10.2139/ssrn.1664951

Cited by 5 publications

(5 citation statements)

References 98 publications

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“…Secondly, by taking the common trend across a large number of series through the use of factor analysis, it is able to include information from a broad range of measures whilst reducing the impact of noise in the data that would otherwise come along with this. High‐level details of the method are set out below with a more in‐depth discussion on the method available in Frale and Monteforte (2011).…”

Section: Nowcasts Of Key Australian Macroeconomic Variablesmentioning

confidence: 99%

Nowcasting Key Australian Macroeconomic Variables

Anthonisz

2023

Australian Economic Review

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Forecasts are relied upon as a guide to what future outcomes for the economy might be. However, it is also important to estimate what is happening in the economy now or has taken place in the recent past. This is where ‘nowcasts’ come in. In this article, I describe what nowcasting is, why it can be a useful tool for macroeconomists as well as present daily nowcasts of key Australian macroeconomic variables, including GDP growth, inflation and the unemployment rate.

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Section: Nowcasts Of Key Australian Macroeconomic Variablesmentioning

confidence: 99%

Nowcasting Key Australian Macroeconomic Variables

Anthonisz

2023

Australian Economic Review

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“…This differs from the MIDAS factor model of Marcellino and Schumacher (2010) which extract factors from x H t and treat the high frequency factor as regressors in the standard MIDAS regression. It is also different from the MIDAS dynamic factor model of Frale and Monteforte (2011), which is basically a bivariate Gaussian model with observation vector (y…”

Section: A Midas-gas Factor Modelmentioning

confidence: 99%

“…Besides our 4 MIDAS-GAS models, we include several competing models in the comparison. We include MIDAS regression models, autoregressive models, standard GAS models and the MIDAS factor model of Frale and Monteforte (2011). For these models, we consider Student-t error distributions and conditional heteroscedasticity.…”

Section: Out-of-sample Exercisementioning

confidence: 99%

Forecasting economic time series using score-driven dynamic models with mixed-data sampling

Gorgi

Koopman

2019

International Journal of Forecasting

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We introduce a mixed-frequency score-driven dynamic model for multiple time series where the score contributions from high-frequency variables are transformed by means of a mixed-data sampling weighting scheme. The resulting dynamic model delivers a flexible and easy-to-implement framework for the forecasting of a low-frequency time series variable through the use of timely information from high-frequency variables. We aim to verify in-sample and out-of-sample performances of the model in an empirical study on the forecasting of U.S. headline inflation. In particular, we forecast monthly inflation using daily oil prices and quarterly inflation using effective federal funds rates. The forecasting results and other findings are promising. Our proposed score-driven dynamic model with mixed-data sampling weighting outperforms competing models in terms of point and density forecasts.

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“…For example, Clements et al [12,13] used monthly macroeconomic and financial indicators to forecast quarterly GDP growth rate. Frale et al [14] used Factor-Mixed Data regression model (FaMIDAS) to predict Italian quarterly GDP based on monthly macroeconomic indicators, such as PMI, M2, and foreign trade indices. Andreou et al [15] used the FaMIDAS model to predict the growth rate of US quarterly GDP and examined the feasibility and practicality of using the daily financial data to predict low-frequency GDP, such as quarterly GDP, in the MIDAS model.…”

Section: Literature Reviewmentioning

confidence: 99%

The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data

et al. 2018

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Presently, the total supply of crude oil is sufficient, but short-term supply and demand imbalances and regional imbalances still exist. The effect of crude oil supply security and price impact cannot be ignored. As the world's largest oil importer, China is highly dependent on foreign oil. Therefore, the fluctuation of international oil prices may impact the development of China's various industries in a significant and differential way. However, because the available data have different frequencies, much of the recent research that addresses the effect of oil prices on industry development need to replace, split, or merge the original data, resulting in loss of the information from the original data. Using the mixed data sampling model (MIDAS(m,K,h)-AR(1)) with the first-order lag autoregressive terms of the interpreted variables, this study builds a mixed data model to investigate the effect of oil price volatility on the output of China's industries. This study expands the extant research by financial market fluctuations and macroeconomic analysis, and at the same time makes short-term predictions on the output of China's seven main industries. The analysis results show that the mixed data regression model brings the original information contained in different frequency data into the model analysis, and utilizes the latest high frequency data of the explanatory variables to perform real-time short-term prediction of low-frequency interpreted variables. This method improves the timeliness of forecasting macroeconomic indicators and the accuracy of short-term forecasts. The empirical results show that the spot price of international crude oil has a significant and differential impact on the outputs of the seven industries in China. Among them, oil price fluctuation has the greatest impact on the output of China's financial industry.

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Famidas: A Mixed Frequency Factor Model with MIDAS Structure

Cited by 5 publications

References 98 publications

Nowcasting Key Australian Macroeconomic Variables

Nowcasting Key Australian Macroeconomic Variables

Forecasting economic time series using score-driven dynamic models with mixed-data sampling

The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data

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