Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach

DOI10.1002/for.2361zbMath1365.62402OpenAlexW1512403887MaRDI QIDQ5272546

Jörg Breitung, Christoph Roling

Publication date: 30 June 2017

Published in: Journal of Forecasting (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/for.2361

zbMATH Keywords

nonparametric regressionpenalized least-squares estimatorHodrick-Prescott filtering techniquemixed-frequency forecast equations

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (1)

Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach

Cites Work

• Unnamed Item
• MIDAS Regressions: Further Results and New Directions
• A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series
• Predicting volatility: getting the most out of return data sampled at different frequencies
• Regression models with mixed sampling frequencies
• Time-varying linear regression via flexible least squares
• Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion
• Real‐Time Forecasts of Inflation: The Role of Financial Variables
• Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach
• A new look at the statistical model identification

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