• A Survey of Econometric Methods for Mixed-Frequency Data 

      Foroni, Claudia; Marcellino, Massimiliano (Working Papers;6/2013, Working paper, 2013)
      The development of models for variables sampled at different frequencies has attracted substantial interest in the recent econometric literature. In this paper we provide an overview of the most common techniques, including ...
    • Density Forecasts with Midas Models 

      Aastveit, Knut Are; Foroni, Claudia; Ravazzolo, Francesco (Working Papers;10/2014, Working paper, 2014)
      In this paper we derive a general parametric bootstrapping approach to compute density forecasts for various types of mixed-data sampling (MIDAS) regressions. We consider both classical and unrestricted MIDAS regressions ...
    • Nowcasting GDP in Real-Time: A Density Combination Approach 

      Aastveit, Knut Are; Gerdrup, Karsten R.; Jore, Anne Sofie; Thorsrud, Leif Anders (Working Papers;11/2011, Working paper, 2011)
      In this paper we use U.S. real-time vintage data and produce combined density nowcasts for quarterly GDP growth from a system of three commonly used model classes. The density nowcasts are combined in two steps. First, a ...
    • Nowcasting Norwegian household consumption with debit card transaction data 

      Aastveit, Knut Are; Fastbø, Tuva Marie; Granziera, Eleonora; Paulsen, Kenneth Sæterhagen; Torstensen, Kjersti Næss (Working Paper;17/2020, Working paper, 2020)
      We use a novel data set covering all domestic debit card transactions in physical terminals by Norwegian households, to nowcast quarterly Norwegian household consumption. These card payments data are free of sampling errors ...
    • Nowcasting Using News Topics. Big Data Versus Big Bank 

      Thorsrud, Leif Anders (Working Papers;20/2016, Working paper, 2016)
      The agents in the economy use a plethora of high frequency information, including news media, to guide their actions and thereby shape aggregate economic fluctuations. Traditional nowcasting approches have to a relatively ...