Permanent-Transitory decomposition of cointegrated time series via Dynamic Factor Models, with an application to commodity prices

Abstract We propose a cointegration-based Permanent-Transitory decomposition for non-stationary Dynamic Factor Models. Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a common and an idiosyncratic component. The common component is further split into a long-term non-stationary and a short-term stationary part. A Monte Carlo experiment shows that incorporating the cointegration structure into the DFM leads to a better reconstruction of the space spanned by the factors, compared to the most standard technique of applying a factor model in differenced systems. We apply our procedure to a set of commodity prices to analyse the comovement among different markets and find that commodity prices move together mostly due to long-term common forces; while the trend for the prices of most primary goods is declining, metals and energy exhibit an upward or at least stable pattern since the 2000s.

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

Communications in Statistics - Simulation and Computation ◽

10.1080/03610918.2019.1599015 ◽

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

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Generalized Dynamic Factor Models for Mixed-Measurement Time Series

Journal of Computational and Graphical Statistics ◽

10.1080/10618600.2012.729986 ◽

2014 ◽

Vol 23 (1) ◽

pp. 169-191 ◽

Cited By ~ 3

Author(s):

Kai Cui ◽

David B. Dunson

Keyword(s):

Time Series ◽

Factor Models ◽

Measurement Time ◽

Dynamic Factor ◽

Dynamic Factor Models

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

Journal of Applied Economics and Business Studies ◽

10.34260/jaebs.427 ◽

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.

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