scholarly journals Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement

2015 ◽  
Vol 2015 (031) ◽  
Author(s):  
Laura E. Jackson ◽  
M. Ayhan Kose ◽  
Christopher Otrok ◽  
Michael T. Owyang
Keyword(s):  
Monte Carlo ◽  
House Price ◽  
Monte Carlo Analysis ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models

Can we use seasonally adjusted variables in dynamic factor models?

2015 ◽  
Vol 19 (3) ◽  
Author(s):  
Maximo Camacho ◽  
Yuliya Lovcha ◽  
Gabriel Perez Quiros
Keyword(s):  
Monte Carlo Analysis ◽  
Factor Models ◽  
Dynamic Factor ◽  
Seasonal Adjustment ◽  
Dynamic Factor Models ◽  
Alternative Approaches ◽  
Individual Variables ◽  
The Individual ◽  
Term Performance ◽  
Better Than

AbstractWe examine the short-term performance of two alternative approaches of forecasting from dynamic factor models. The first approach extracts the seasonal component of the individual variables before estimating the model, while the alternative uses the non seasonally adjusted data in a model that endogenously accounts for seasonal adjustment. Our Monte Carlo analysis reveals that the performance of the former is always comparable to or even better than that of the latter in all the simulated scenarios. Our results have important implications for the factor models literature because they show the that the common practice of using seasonally adjusted data in this type of models is very accurate in terms of forecasting ability. Using five coincident indicators, we illustrate this result for US data.

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.

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