scholarly journals Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models

2014 ◽  
Author(s):  
Francisco Blasques ◽  
Siem Jan Koopman ◽  
Max Mallee
Keyword(s):  
Low Frequency ◽  
Factor Models ◽  
Dynamic Factor ◽  
Weighted Likelihood ◽  
Dynamic Factor Models ◽  
Mixed Frequency

scholarly journals Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data

Forecasting ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 56-90
Author(s):  
Monica Defend ◽  
Aleksey Min ◽  
Lorenzo Portelli ◽  
Franz Ramsauer ◽  
Francesco Sandrini ◽  
...  
Keyword(s):  
Panel Data ◽  
Model Selection ◽  
Estimation Method ◽  
Selection Criterion ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Model Selection Criterion ◽  
Mixed Frequency Data

This article considers the estimation of Approximate Dynamic Factor Models with homoscedastic, cross-sectionally correlated errors for incomplete panel data. In contrast to existing estimation approaches, the presented estimation method comprises two expectation-maximization algorithms and uses conditional factor moments in closed form. To determine the unknown factor dimension and autoregressive order, we propose a two-step information-based model selection criterion. The performance of our estimation procedure and the model selection criterion is investigated within a Monte Carlo study. Finally, we apply the Approximate Dynamic Factor Model to real-economy vintage data to support investment decisions and risk management. For this purpose, an autoregressive model with the estimated factor span of the mixed-frequency data as exogenous variables maps the behavior of weekly S&P500 log-returns. We detect the main drivers of the index development and define two dynamic trading strategies resulting from prediction intervals for the subsequent returns.

scholarly journals Identification of Generalized Dynamic Factor Models from mixed-frequency data

2018 ◽  
Vol 51 (15) ◽  
pp. 1008-1013 ◽  
Author(s):  
B.D.O. Anderson ◽  
Alexander Braumann ◽  
Manfred Deistler
Keyword(s):  
Factor Models ◽  
Dynamic Factor ◽  
Frequency Data ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Mixed Frequency Data

NOWCASTING GDP GROWTH IN A SMALL OPEN ECONOMY

2021 ◽  
Vol 256 ◽  
pp. 127-161
Author(s):  
Massimiliano Marcellino ◽  
Vasja Sivec
Keyword(s):  
Open Economy ◽  
Small Open Economy ◽  
Factor Models ◽  
Economic Conditions ◽  
Dynamic Factor ◽  
Gdp Growth ◽  
Learning Models ◽  
Dynamic Factor Models ◽  
Mixed Frequency ◽  
Machine Learning Models

Nowcasting, that is, forecasting the current economic conditions, is a key ingredient for decision making, but it is complex, even more so for a small open economy, due to the higher volatility of its GDP. In this paper, we review the required steps, taking Luxembourg as an example. We consider both standard and alternative indicators, used as inputs in several nowcasting methods, including various factor and machine learning models. Overall, mixed frequency dynamic factor models and neural networks perform well, both in absolute terms and in relative terms with respect to a benchmark autoregressive model. The gains are larger during problematic times, such as the financial crisis and the recent Covid period.

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