Co-Movement of State’s Mortgage Default Rates: A Dynamic Factor Analysis Approach

2011 ◽  
Author(s):  
Yaw Owusu-Ansah
Keyword(s):  
Factor Analysis ◽  
Analysis Approach ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Mortgage Default ◽  
Default Rates

scholarly journals Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

Technometrics ◽  
2011 ◽  
Vol 53 (2) ◽  
pp. 137-151 ◽  
Author(s):  
Andrés M. Alonso ◽  
Carolina García-Martos ◽  
Julio Rodríguez ◽  
María Jesús Sánchez
Keyword(s):  
Factor Analysis ◽  
Electricity Market ◽  
Seasonal Dynamic ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Bootstrap Inference

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

Analysis of Power Output Time Series in Response to Supramaximal Exercise: An Approach Through Dynamic Factor Analysis

2011 ◽  
Vol 23 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Paula Marta Bruno ◽  
Fernando Duarte Pereira ◽  
Renato Fernandes ◽  
Gonçalo Vilhena de Mendonça
Keyword(s):  
Factor Analysis ◽  
Power Output ◽  
Soccer Players ◽  
Upper Body ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Lower Body ◽  
Supramaximal Exercise ◽  
Output Time ◽  
Pattern Of Variation

The responses to supramaximal exercise testing have been traditionally analyzed by means of standard parametric and nonparametric statistics. Unfortunately, these statistical approaches do not allow insight into the pattern of variation of a given parameter over time. The purpose of this study was to determine if the application of dynamic factor analysis (DFA) allowed discriminating different patterns of power output (PO), during supramaximal exercise, in two groups of children engaged in competitive sports: swimmers and soccer players. Data derived from Wingate testing were used in this study. Analyses were performed on epochs (30 s) of upper and lower body PO obtained from twenty two healthy boys (11 swimmers and 11 soccer players) age 11–12 years old. DFA revealed two distinct patterns of PO during Wingate. Swimmers tended to attain their peak PO (upper and lower body) earlier than soccer players. As importantly, DFA showed that children with a given pattern of upper body PO tend to perform similarly during lower body exercise.

scholarly journals Estimating common trends in multivariate time series using dynamic factor analysis

2003 ◽  
Vol 14 (7) ◽  
pp. 665-685 ◽  
Author(s):  
A. F. Zuur ◽  
R. J. Fryer ◽  
I. T. Jolliffe ◽  
R. Dekker ◽  
J. J. Beukema
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Multivariate Time Series ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor ◽  
Common Trends

Dynamic factor analysis of nonstationary multivariate time series

Psychometrika ◽  
1992 ◽  
Vol 57 (3) ◽  
pp. 333-349 ◽  
Author(s):  
Peter C. M. Molenaar ◽  
Jan G. De Gooijer ◽  
Bernhard Schmitz
Keyword(s):  
Time Series ◽  
Factor Analysis ◽  
Multivariate Time Series ◽  
Dynamic Factor Analysis ◽  
Dynamic Factor
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