Forecasting with Approximate Dynamic Factor Models: The Role of Non-Pervasive Shocks

2011 ◽  
Author(s):  
Matteo Luciani
Keyword(s):  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models

Using dynamic factor models to forecast Canadian inflation: the role of US variables

2008 ◽  
Vol 17 (1) ◽  
pp. 15-18 ◽  
Author(s):  
Marc-André Gosselin ◽  
Greg Tkacz
Keyword(s):  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models

Bayesian Dynamic Factor Models and Portfolio Allocation

2000 ◽  
Vol 18 (3) ◽  
pp. 338 ◽  
Author(s):  
Omar Aguilar ◽  
Mike West
Keyword(s):  
Factor Models ◽  
Portfolio Allocation ◽  
Dynamic Factor ◽  
Dynamic Factor Models

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 Dynamic Factor Models: A Review of the Literature

2013 ◽  
Author(s):  
Karim Barhoumi ◽  
Olivier Darné ◽  
Laurent Ferrara
Keyword(s):  
Factor Models ◽  
Dynamic Factor ◽  
Review Of The Literature ◽  
Dynamic Factor Models

scholarly journals Empirical Bayes Methods for Dynamic Factor Models

2014 ◽  
Author(s):  
Siem Jan Koopman ◽  
Geert Mesters
Keyword(s):  
Empirical Bayes ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Bayes Methods ◽  
Empirical Bayes Methods

scholarly journals Markov-Switching Dynamic Factor Models in Real Time

2012 ◽  
Author(s):  
Maximo Camacho ◽  
Gabriel Perez-Quiros ◽  
Pilar Poncela
Keyword(s):  
Real Time ◽  
Markov Switching ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Switching Dynamic

scholarly journals Dynamic factor models: Does the specification matter?

SERIEs ◽  
2021 ◽  
Author(s):  
Karen Miranda ◽  
Pilar Poncela ◽  
Esther Ruiz
Keyword(s):  
Factor Models ◽  
Model Specification ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Number Of Factors ◽  
Time Varying Parameters ◽  
Out Of Sample ◽  
Mixed Frequency Data ◽  
Natural Way ◽  
Factor Estimation

AbstractDynamic factor models (DFMs), which assume the existence of a small number of unobserved underlying factors common to a large number of variables, are very popular among empirical macroeconomists. Factors can be extracted using either nonparametric principal components or parametric Kalman filter and smoothing procedures, with the former being computationally simpler and robust against misspecification and the latter coping in a natural way with missing and mixed-frequency data, time-varying parameters, nonlinearities and non-stationarity, among many other stylized facts often observed in real systems of economic variables. This paper analyses the empirical consequences on factor estimation, in-sample predictions and out-of-sample forecasting of using alternative estimators of the DFM under various sources of potential misspecification. In particular, we consider factor extraction when assuming different number of factors and different factor dynamics. The factors are extracted from a popular data base of US macroeconomic variables, widely analyzed in the literature without consensus about the most appropriate model specification. We show that this lack of consensus is only marginally crucial when it comes to factor extraction, but it matters when the objective is out-of-sample forecasting.

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