scholarly journals Bivariate models for time series of counts: A comparison study between PBINAR models and dynamic factor models

2019 ◽  
pp. 1-15
Author(s):  
Magda Monteiro ◽  
Isabel Pereira ◽  
Manuel G. Scotto
Keyword(s):  
Time Series ◽  
Factor Models ◽  
Dynamic Factor ◽  
Comparison Study ◽  
Dynamic Factor Models ◽  
Time Series Of Counts ◽  
Bivariate Models

scholarly journals Fitting dynamic factor models to non-stationary time series

2011 ◽  
Vol 163 (1) ◽  
pp. 51-70 ◽  
Author(s):  
Michael Eichler ◽  
Giovanni Motta ◽  
Rainer von Sachs
Keyword(s):  
Time Series ◽  
Factor Models ◽  
Stationary Time Series ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Stationary Time

An empirical testing of comparative efficiency of static and dynamic factor models towards stock returns’ predictability in capital market of Pakistan

2020 ◽  
Vol 4 (2) ◽  
pp. 141-162
Author(s):  
Laila Taskeen Qazi ◽  
Atta ur Rahman ◽  
Shahid Ali ◽  
Sohail Alam
Keyword(s):  
Time Series ◽  
Stock Returns ◽  
Time Series Data ◽  
Factor Model ◽  
Efficient Market Hypothesis ◽  
Factor Models ◽  
Series Data ◽  
Dynamic Factor ◽  
Dynamic Factor Models ◽  
Returns Predictability

Efficient Market Hypothesis has its supporters and critics as it has invited significant attention of research scholarship in recent years. The taxonomy and existence of this hypothesis is widely debated in terms of making economic decisions in the capital markets. Stock returns predictability has galvanized researchers to use forecasting models. Literature shows that forecasting is possible yet it debates problems associated with the techniques used for forecasting from the time series data. The study relies on stock returns for 67 randomly selected companies listed on the Pakistan Stock Exchange. The static and the dynamic factor models are compared in terms of forecast efficiency. The study also uses eight macroeconomic variables to forecast stock returns by including gold prices, crude oil prices, market capitalization, PSX- 100 index, PSX-100 index turnover, KIBOR 1-month rates, KIBOR 3 years rates and Rupee to Dollar rates. The results of the hit rates and out-of-sample forecasting technique suggest that dynamic factor model is the best multivariate time series forecasting model in the Pakistani context.

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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