Training Calendar

Modelling and Forecasting Mixed Frequency Data with EViews

Frankfurt am Main 1 day (23rd September 2019 - 23rd September 2019) EViews Advanced, Intermediate
Finance, Forecasting, Mixed Frequency, Time series


The aim of this training is to introduce the modelling and forecasting with mixed frequency models. The world is indeed mixed-frequent. This not only means that mixed frequency (MF) models constitute a popular and widely studied topic in macroeconomic and financial time series econometrics, it is simply an omnipresent fact that applied and theoretical researchers need to deal with. Indeed, national accounts variables, such as consumption, export or the gross domestic product are available quarterly; inflation, unemployment rate or industrial production indexes are available monthly, whereas most financial time series, such as interest rates or stock prices, are released on a daily basis and are even available at an intraday frequency, minute by minute. When studying the relationship between these series, it has recently become standard to properly account for the mismatch in publication frequencies among variables, instead of aggregating high-frequency observations using predetermined aggregation schemes. Working with genuine variables instead of loosing some important information when aggregating them is very helpful for forecasting or nowcasting macroeconomic indicator as well as for detecting causality. In finance, intraday data are combined to evaluate the uncertainty of daily financial assets (namely their volatility, and hence their risk) and then of portfolios.

The set of mixed frequency models ranges from single-regression models (e.g., the well known MIDAS model) over factor models to vector autoregressive models. While it was relatively tedious to implement those methods with routines written in different programming languages, the new features proposed in EViews11 allow, with some practice, to easily build useful models and to make forecast in a mixed frequency environment.


Modelling and Forecasting Mixed Frequency Data with EViews

The course will cover the following:

Session 1
Introduction to mixed frequency issues, real time data and data availability, nowcasting and forecast evaluation with EViews in single equation and vector autoregressive models, aggregation of stock and flow data. Aggregation and interpolation at different frequencies with EViews.

Session 2
Comparison between observational and data driven methods and mixed frequency methods. Opening a mixed frequency sheet in EViews. Introduction and estimation with (MI)xed (DA)ta (S)ampling (MIDAS) for stationary time series in single equation. Determination of the choice of the weighting function of the high frequency variables and U-MIDAS. Application to macroeconomics and finance.

Session 3
In order to look at the impact of low frequency data to the high frequency ones and to forecast at multiple horizons we have to introduce the mixed frequency vector autoregressive models for stationary time series. We focus on testing for Granger causality between real and financial time series.

Session 4
Additional features: nonstationary mixed frequency model models, and mixed factor models.


Principal texts for pre-course reading:

  • A block book with the slides as well as the data used in the training will be available prior to the course.

Principal texts for post-course reading:

  • A list of survey papers and key articles will be proposed for further reading.


TimeSession / Description
08:45-09:15 Arrival & Registration
09:30-11:00 Session 1
11:00-11:15 Tea/coffee break
11:15-12:45 Session 2
12:45-13:45 Lunch
13:45-15:15 Session 3
15:15-15:30 Tea/coffee break
15:30-17:00 Session 4


Knowledge of time series econometrics, Knowledge of the key features of EViews (e.g. reading data, running regressions).

Terms & Conditions

  • Student registrations: Attendees must provide proof of full time student status at the time of booking to qualify for student registration rate (valid student ID card or authorised letter of enrolment).
  • Additional discounts are available for multiple registrations.
  • Cost includes course materials, lunch and refreshments.
  • Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course. (Alternatively, we can also provide laptops for a small daily charge).
  • If you need assistance in locating hotel accommodation, please notify us at the time of booking.
  • Payment of course fees required prior to the course start date.
  • Registration closes 5-calendar days prior to the start of the course.

    • 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
    • 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
    • No fee returned for cancellations made less than 14-calendar days prior to the start of the course.

The number of delegates is restricted. Please register early to guarantee your place.

  •  CommercialAcademicStudent
    23 September 2019 (23/09/2019 - 23/09/2019)

All prices exclude VAT or local taxes where applicable.

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