A dynamic factor model of the coincident indicators for the US transportation sector

Lahiri, Kajal; Yao, Wenxiong

doi:10.1080/1350485042000271125

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…Kajal Lahiri has contributed to the DFMs literature with several empirical works. For example, Lahiri and Yao (2004) implement a DFM to analyze the business cycle features of the transportation sector and Lahiri and Sheng (2010) to measure the forecast uncertainty by disagreement. Lahiri et al (2015) also implement a DFM to a real-time jagged-edge data set of over 160 explanatory variables to reexamine the role of consumer confidence surveys in forecasting personal consumption expenditure.…”

Section: Introductionmentioning

confidence: 99%

Determining the number of factors after stationary univariate transformations

2016

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A very common practice when extracting factors from non-stationary multivariate time series is to differentiate each variable in the system. As a consequence, the ratio between variances and the dynamic dependence of the common and idiosyncratic differentiated components may change with respect to the original components. In this paper, we analyze the effects of these changes on the finite sample properties of several procedures to determine the number of factors. In particular, we consider the information criteria of Bai and Ng (Econometrica 70 (1) idiosyncratic noises. Furthermore, we also analyze the role of the number of factors in the original non-stationary system as well as of its temporal and cross-sectional dimensions. Finally, we implement the different procedures to determine the number of common factors in a system of inflation rates in 15 euro area countries.

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Section: Introductionmentioning

confidence: 99%

Determining the number of factors after stationary univariate transformations

2016

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“…There is a vast literature with similar observations. For example, Young et al [60] as well as Lahiri and Yao [61] have observed that the transportation and activity system follows a lead-lag phase pattern and environment system is inversely related to the other two. The following axioms can be postulated to assess the sustainability or the unsustainability of our SOS.…”

Section: Resultsmentioning

confidence: 99%

Estimation of Performance Indices for the Planning of Sustainable Transportation Systems

Paz

Maheshwari

Kachroo

et al. 2013

Advances in Fuzzy Systems

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In the context of sustainable transportation systems, previous studies have either focused only on the transportation system or have not used a methodology that enables the treatment of incomplete, vague, and qualitative information associated with the available data. This study proposes a system of systems (SOS) and a fuzzy logic modeling approach. The SOS includes the Transportation, Activity, and Environment systems. The fuzzy logic modeling approach enables the treatment of the vagueness associated with some of the relevant data. Performance Indices (PIs) are computed for each system using a number of performance measures. The PIs illustrate the aggregated performance of each system as well as the interactions among them. The proposed methodology also enables the estimation of a Composite Sustainability Index to summarize the aggregated performance of the overall SOS. Existing data was used to analyze sustainability in the entire United States. The results showed that the Transportation and Activity systems follow a positive trend, with similar periods of growth and contractions; in contrast, the environmental system follows a reverse pattern. The results are intuitive and are associated with a series of historic events, such as depressions in the economy as well as policy changes and regulations.

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“…Inspiration of this work is, as its structure two-fold: Firstly the stream of work on the development and utilization of the U.S Transportation Services Index in Lahiri et al (2003aLahiri et al ( , 2003bLahiri et al ( , 2012, Lahiri and Yao (2004a, 2004b, Lahiri (2010a, Brady et al (2006) and the reports of Young et al (2007Young et al ( , 2009, and the major motivation being the feasibility of applying and adjusting this concepts to ports and trade. This endeavor led, eventually, to the prospect of factor-based modeling of port and trade related variables, which in turn led to contemporary approximate and generalized dynamic factor methodologies of Watson (2002a, 2002b) and Forni et al (2000Forni et al ( , 2005 respectively.…”

Section: Methodological Inspirations Motivations and Intentionsmentioning

confidence: 99%

Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade

Angelopoulos

2016

Marit Econ Logist

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A dynamic factor model of the coincident indicators for the US transportation sector

Cited by 12 publications

References 10 publications

Determining the number of factors after stationary univariate transformations

Determining the number of factors after stationary univariate transformations

Estimation of Performance Indices for the Planning of Sustainable Transportation Systems

Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade

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