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.

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 Determining Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2865
Author(s):  
Jiayi Luo ◽  
Cindy Long Yu
Keyword(s):  
Real Time ◽  
Factor Models ◽  
Data Series ◽  
Dynamic Factor ◽  
Model Parameters ◽  
Dynamic Factor Models ◽  
Monthly Data ◽  
Macroeconomic Conditions ◽  
Number Of Factors ◽  
Shrinkage Prior

Real-time nowcasting is a process to assess current-quarter GDP from timely released economic and financial series before the figure is disseminated in order to catch the overall macroeconomic conditions in real time. In economic data nowcasting, dynamic factor models (DFMs) are widely used due to their abilities to bridge information with different frequencies and to achieve dimension reduction. However, most of the research using DFMs assumes a fixed known number of factors contributing to GDP nowcasting. In this paper, we propose a Bayesian approach with the horseshoe shrinkage prior to determine the number of factors that have nowcasting power in GDP and to accurately estimate model parameters and latent factors simultaneously. The horseshoe prior is a powerful shrinkage prior in that it can shrink unimportant signals to 0 while keeping important ones remaining large and practically unshrunk. The validity of the method is demonstrated through simulation studies and an empirical study of nowcasting U.S. quarterly GDP growth rates using monthly data series in the U.S. market.

scholarly journals A forecasting performance comparison of dynamic factor models based on static and dynamic methods

2017 ◽  
Vol 8 (1) ◽  
pp. 43-66 ◽  
Author(s):  
Fabio Della Marra
Keyword(s):  
Financial Time Series ◽  
Principal Component ◽  
Factor Models ◽  
Performance Comparison ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Rolling Window ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Sample Environment

Abstract We present a comparison of the forecasting performances of three Dynamic Factor Models on a large monthly data panel of macroeconomic and financial time series for the UE economy. The first model relies on static principal-component and was introduced by Stock and Watson (2002a, b). The second is based on generalized principal components and it was introduced by Forni, Hallin, Lippi and Reichlin (2000, 2005). The last model has been recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015, 2016). The data panel is split into two parts: the calibration sample, from February 1986 to December 2000, is used to select the most performing specification for each class of models in a in- sample environment, and the proper sample, from January 2001 to November 2015, is used to compare the performances of the selected models in an out-of-sample environment. The metholodogical approach is analogous to Forni, Giovannelli, Lippi and Soccorsi (2016), but also the size of the rolling window is empirically estimated in the calibration process to achieve more robustness. We find that, on the proper sample, the last model is the most performing for the Inflation. However, mixed evidencies appear over the proper sample for the Industrial Production.

scholarly journals Consistent factor estimation in dynamic factor models with structural instability

2013 ◽  
Vol 177 (2) ◽  
pp. 289-304 ◽  
Author(s):  
Brandon J. Bates ◽  
Mikkel Plagborg-Møller ◽  
James H. Stock ◽  
Mark W. Watson
Keyword(s):  
Structural Instability ◽  
Factor Models ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Factor Estimation

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
Sign in / Sign up

Export Citation Format

Share Document