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1 – 10 of over 24000Fabio Canova and Matteo Ciccarelli
This article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous…
Abstract
This article provides an overview of the panel vector autoregressive models (VAR) used in macroeconomics and finance to study the dynamic relationships between heterogeneous assets, households, firms, sectors, and countries. We discuss what their distinctive features are, what they are used for, and how they can be derived from economic theory. We also describe how they are estimated and how shock identification is performed. We compare panel VAR models to other approaches used in the literature to estimate dynamic models involving heterogeneous units. Finally, we show how structural time variation can be dealt with.
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Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive…
Abstract
Purpose
Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.
Design/methodology/approach
It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.
Findings
This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.
Research limitations/implications
This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.
Practical implications
The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.
Originality/value
This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.
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This paper provides a selective survey of the panel macroeconometric techniques that focus on controlling the impact of “unobserved heterogeneity” across individuals and over time…
Abstract
This paper provides a selective survey of the panel macroeconometric techniques that focus on controlling the impact of “unobserved heterogeneity” across individuals and over time to obtain valid inference for “structures” that are common across individuals and over time. We consider issues of (i) estimating vector autoregressive models; (ii) testing of unit root or cointegration; (iii) statistical inference for dynamic simultaneous equations models; (iv) policy evaluation; and (v) aggregation and prediction.
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There are structural elements on the aircraft that may be exposed to high-intensity sound levels. One of them is an air inlet duct of the jet engine. To prepare data for the air…
Abstract
Purpose
There are structural elements on the aircraft that may be exposed to high-intensity sound levels. One of them is an air inlet duct of the jet engine. To prepare data for the air duct damage tolerance analysis, flat panels were tested under acoustic loading. The paper aims to discuss this issue.
Design/methodology/approach
The acoustic fatigue test equipment for grazing wave’s incidence was designed based on the FE analyses. Flat composite panels were designed and manufactured using the Hexply 8552/AGP193-PW prepreg with the simulation of production imperfections or operational damage. The dynamic behaviour of panels has been tested using three regimes of acoustic loading: white noise spectrum, engine noise spectrum and discrete harmonic frequencies. The panel deflection was monitored along its longitudinal axis, and the ultrasonic NDT instruments were used for the monitoring of relevant delamination increments. The FE model of the panel was created in Abaqus to study panel dynamic characteristics.
Findings
No delamination progress was observed by NDT testing even if dynamic characteristics, especially modal frequency, of the panel changed during the fatigue test. Rayleigh damping coefficients were evaluated for their use in FE models. Significant differences were found between the measured and computed panel deflection curves near the edge of the panel.
Originality/value
The research results underscored the signification of the FE model boundary conditions and the element type selections when the panel works like a membrane rather than a plate because of their low bending stiffness.
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The paper aims to propose a clustering model for panel data. More specifically, the paper aims to construct a gray incidence model for panel data to solve the classification with…
Abstract
Purpose
The paper aims to propose a clustering model for panel data. More specifically, the paper aims to construct a gray incidence model for panel data to solve the classification with multi-factors and multi-attributes.
Design/methodology/approach
The paper opted for a clustering theory study using gray incidence theory based on dynamic weighted function. The paper presents an example to verify the rationality of the new model, which suggests that the new model can reflect the incidence degree of panel data.
Findings
The paper provides a new gray incidence model based on a dynamic weighted function that can amplify the characteristics of the sample to some extent. The properties of the new incidence model, such as normalization, symmetry and nearness, are all satisfied. The paper also shows that the new incidence model performs very well on cluster discrimination.
Originality/value
The new model in this paper has supplemented and improved the gray incidence analysis theory for panel data.
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Mourad Mroua and Lotfi Trabelsi
This paper aims to investigate simultaneously the causality and the dynamic links between exchange rates and stock market indices. It attempts to identify the short- and long-term…
Abstract
Purpose
This paper aims to investigate simultaneously the causality and the dynamic links between exchange rates and stock market indices. It attempts to identify the short- and long-term effect of the US dollar on major stock market indices of Brazil, Russia, India, China and South-Africa (BRICS) nations.
Design/methodology/approach
This paper applies a new methodology combining the panel generalized method of moments model and the panel auto-regressive distributed lag (ARDL) method to investigate the existence of a causal short-/long-run relationships and dynamic dependence among all stock market returns and exchanges rates changes of BRICS countries.
Findings
Results show that exchange rate changes have a significant effect on the past and the current volatility of the BRICS stock indices. Besides, ARDL estimations reveal that exchange rate movements have a significant effect on short- and long-term stocks market indices of all BRICS countries
Originality/value
The findings have implications for policymakers and market participants who try to manage the exchange rate will have a different dose of intervention if they know that the effects of currency depreciation are different than appreciation. These results have important implications that investors should take into account in frequency-varying exchange rates and stock returns and regulators should consider developing sound policy measures to prevent financial risk.
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Yonghui Zhang and Qiankun Zhou
It is shown in the literature that the Arellano–Bond type generalized method of moments (GMM) of dynamic panel models is asymptotically biased (e.g., Hsiao & Zhang, 2015; Hsiao &…
Abstract
It is shown in the literature that the Arellano–Bond type generalized method of moments (GMM) of dynamic panel models is asymptotically biased (e.g., Hsiao & Zhang, 2015; Hsiao & Zhou, 2017). To correct the asymptotical bias of Arellano–Bond GMM, the authors suggest to use the jackknife instrumental variables estimation (JIVE) and also show that the JIVE of Arellano–Bond GMM is indeed asymptotically unbiased. Monte Carlo studies are conducted to compare the performance of the JIVE as well as Arellano–Bond GMM for linear dynamic panels. The authors demonstrate that the reliability of statistical inference depends critically on whether an estimator is asymptotically unbiased or not.
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Abdullahi D. Ahmed and Abu N.M. Wahid
This paper aims to use the newly developed panel data cointegration analysis and the dynamic time series modeling approach to examine the linkages between financial structure…
Abstract
Purpose
This paper aims to use the newly developed panel data cointegration analysis and the dynamic time series modeling approach to examine the linkages between financial structure (market‐based vs bank‐based) and economic growth in African economies.
Design/methodology/approach
The research investigates the dynamic relationship between financial structure and economic growth in a panel of a group of seven African developing countries over the period of 1986‐2007. The paper uses various indicators/measures of financial structure and financial system, and employs the traditional time‐series analysis for causality as well as the newly developed panel unit root and cointegration techniques and estimated finance‐growth relationship using FMOLS for heterogeneous panel.
Findings
From the dynamic heterogeneous panel approach, the paper firstly finds that market‐based financial system is important for explaining output growth through enhancing efficiency and productivity. Second, the authors' empirical evidence supports the view that higher levels of banking system development are positively associated with capital accumulation growth and lead to faster rates of economic growth.
Originality/value
Panel cointegration, group mean panel FMOLS and country‐by‐country time series investigations indicate that the market‐based financial system is important for explaining output growth through enhancing efficiency and productivity, whereas the development of banking system is significantly associated with capital accumulation growth. Further results from the time‐series approach show evidence of unidirectional causality running from market‐oriented as well as bank‐oriented financial systems to economic growth.
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