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Article
Publication date: 1 March 1987

P.L. Kirby and I.D. Pagan

Surface Mounting is shown to be the fourth generation of electronic interconnection technology. It has several facets and is seen differently from various viewpoints in the…

32

Abstract

Surface Mounting is shown to be the fourth generation of electronic interconnection technology. It has several facets and is seen differently from various viewpoints in the assembly industry. A review of published papers shows that the subject grew during the 1970s with no single inventor and as a result of numerous developments which are now combining into a coherent technology with important compatibility with other recent innovations.

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Microelectronics International, vol. 4 no. 3
Type: Research Article
ISSN: 1356-5362

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Article
Publication date: 5 June 2017

Samit Paul and Prateek Sharma

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model…

634

Abstract

Purpose

This study aims to forecast daily value-at-risk (VaR) for international stock indices by using the conditional extreme value theory (EVT) with the Realized GARCH (RGARCH) model. The predictive ability of this Realized GARCH-EVT (RG-EVT) model is compared with those of the standalone GARCH models and the conditional EVT specifications with standard GARCH models.

Design/methodology/approach

The authors use daily data on returns and realized volatilities for 13 international stock indices for the period from 1 January 2003 to 8 October 2014. One-step-ahead VaR forecasts are generated using six forecasting models: GARCH, EGARCH, RGARCH, GARCH-EVT, EGARCH-EVT and RG-EVT. The EVT models are implemented using the two-stage conditional EVT framework of McNeil and Frey (2000). The forecasting performance is evaluated using multiple statistical tests to ensure the robustness of the results.

Findings

The authors find that regardless of the choice of the GARCH model, the two-stage conditional EVT approach provides significantly better out-of-sample performance than the standalone GARCH model. The standalone RGARCH model does not perform better than the GARCH and EGARCH models. However, using the RGARCH model in the first stage of the conditional EVT approach leads to a significant improvement in the VaR forecasting performance. Overall, among the six forecasting models, the RG-EVT model provides the best forecasts of daily VaR.

Originality/value

To the best of the authors’ knowledge, this is the earliest implementation of the RGARCH model within the conditional EVT framework. Additionally, the authors use a data set with a reasonably long sample period (around 11 years) in the context of high-frequency data-based forecasting studies. More significantly, the data set has a cross-sectional dimension that is rarely considered in the existing VaR forecasting literature. Therefore, the findings are likely to be widely applicable and are robust to the data snooping bias.

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Studies in Economics and Finance, vol. 34 no. 2
Type: Research Article
ISSN: 1086-7376

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Book part
Publication date: 23 June 2016

Bao Yong, Fan Yanqin, Su Liangjun and Zinde-Walsh Victoria

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works…

Abstract

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.

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Article
Publication date: 10 August 2020

Rohit Apurv and Shigufta Hena Uzma

The purpose of the paper is to examine the impact of infrastructure investment and development on economic growth in Brazil, Russia, India, China and South Africa (BRICS…

1733

Abstract

Purpose

The purpose of the paper is to examine the impact of infrastructure investment and development on economic growth in Brazil, Russia, India, China and South Africa (BRICS) countries. The effect is examined for each country separately and also collectively by combining each country.

Design/methodology/approach

Ordinary least square regression method is applied to examine the effects of infrastructure investment and development on economic growth for each country. Panel data techniques such as panel least square method, panel least square fixed-effect model and panel least square random effect model are used to examine the collective impact by combining all countries in BRICS. The dynamic panel model is also incorporated for analysis in the study.

Findings

The results of the study are mixed. The association between infrastructure investment and development and economic growth for countries within BRICS is not robust. There is an insignificant relationship between infrastructure investment and development and economic growth in Brazil and South Africa. Energy and transportation infrastructure investment and development lead to economic growth in Russia. Telecommunication infrastructure investment and development and economic growth have a negative relationship in India, whereas there is a negative association between transport infrastructure investment and development and economic growth in China. Panel data results conclude that energy infrastructure investment and development lead to economic growth, whereas telecommunication infrastructure investment and development are significant and negatively linked with economic growth.

Originality/value

The study is novel as time series analysis and panel data analysis are used, taking the time span for 38 years (1980–2017) to investigate the influence of infrastructure investment and development on economic growth in BRICS Countries. Time-series regression analysis is used to test the impact for individual countries separately, whereas panel data regression analysis is used to examine the impact collectively for all countries in BRICS.

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Indian Growth and Development Review, vol. 14 no. 1
Type: Research Article
ISSN: 1753-8254

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Article
Publication date: 1 February 2003

Michael Fung

Most of the past studies have managed to confirm the value‐relevance of R&D, but few of them have formally addressed the issue of knowledge spillovers. In reality, firms improve…

229

Abstract

Most of the past studies have managed to confirm the value‐relevance of R&D, but few of them have formally addressed the issue of knowledge spillovers. In reality, firms improve their know‐how both by producing new knowledge and by learning from others. The objective of this study is to examine the relevance of R&D and knowledge spillovers as an explanation for the observed inconsistency between market and book values. While the level of R&D is measured by R&D expenses and patent counts, the intensity of knowledge spillovers is measured by tracing the linkages between inventions across time as established by patent citations. Specifically, knowledge spillovers are decomposed into intraindustry, internal, and interindustry spillovers. The empirical findings from this study conclude that R&D and knowledge spillovers are value‐relevant. The results also suggest that, among the three components of spillovers, intraindustry spillovers have the strongest impact on market‐to‐book ratios.

Details

Pacific Accounting Review, vol. 15 no. 2
Type: Research Article
ISSN: 0114-0582

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Book part
Publication date: 1 January 1999

Badi H. Baltagi, Young-Jae Chang and Qi Li

Abstract

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Messy Data
Type: Book
ISBN: 978-0-76230-303-8

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Book part
Publication date: 1 September 2023

Ishu Chadda

Abstract

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Social Sector Development and Inclusive Growth in India
Type: Book
ISBN: 978-1-83753-187-5

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Article
Publication date: 14 August 2023

Cong Minh Huynh

This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12…

272

Abstract

Purpose

This study empirically examines the impact of climate change and agricultural research and development (R&D) as well as their interaction on agricultural productivity in 12 selected Asian and Pacific countries over the period of 1990–2018.

Design/methodology/approach

Various estimation methods for panel data, including Fixed Effects (FE), the Feasible Generalized Least Squares (FGLS) and two-step System Generalized Method of Moments (SGMM) were used.

Findings

Results show that both proxies of climate change – temperature and precipitation – have negative impacts on agricultural productivity. Notably, agricultural R&D investments not only increase agricultural productivity but also mitigate the detrimental impact of climate change proxied by temperature on agricultural productivity. Interestingly, climate change proxied by precipitation initially reduces agricultural productivity until a threshold of agricultural R&D beyond which precipitation increases agricultural productivity.

Practical implications

The findings imply useful policies to boost agricultural productivity by using R&D in the context of rising climate change in the vulnerable continent.

Originality/value

This study contributes to the literature in two ways. First, this study examines how climate change affects agricultural productivity in Asian and Pacific countries – those are most vulnerable to climate change. Second, this study assesses the role of R&D in improving agricultural productivity as well as its moderating effect in reducing the harmful impact of climate change on agricultural productivity.

Details

Journal of Economic Studies, vol. 51 no. 3
Type: Research Article
ISSN: 0144-3585

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Book part
Publication date: 18 January 2022

Adrian Pagana and Michael Wickensb

Pesaran and Smith (2011) concluded that Dynamic Stochastic General Equilibrium (DSGE) models were sometimes a straitjacket which hampered the ability to match certain features of…

Abstract

Pesaran and Smith (2011) concluded that Dynamic Stochastic General Equilibrium (DSGE) models were sometimes a straitjacket which hampered the ability to match certain features of the data. In this chapter, the authors look at how one might assess the fit of these models using a variety of measures, rather than what seems to be an increasingly common device – the Marginal Data Density. The authors apply these in the context of models by Christiano, Motto, and Rostagno (2014) and Ireland (2004), finding they fail to make a match by a large margin. Both of these models feature more shocks than observed variables, resulting in the empirical shocks having a singular density, and so making them correlated. When correlated one can neither interpret impulse responses nor perform variance decompositions. Against this, there is a strong argument for having a straitjacket, as it enforces some desirable behavior on models and makes researchers think about how to account for any non-stationarity in the data. The authors illustrate this with examples drawn from the SVAR literature and also more eclectic models such as Holston, Laubach, and Williams (2017) for extracting an estimate of the real natural rate.

Details

Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling
Type: Book
ISBN: 978-1-80262-062-7

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Book part
Publication date: 18 January 2022

CY (Chor-yiu) Sin

In the two seminal papers Anderson and Hsiao (1981, 1982), the linear panel regression model without cross-sectional correlation is thoroughly discussed. This uncorrelatedness…

Abstract

In the two seminal papers Anderson and Hsiao (1981, 1982), the linear panel regression model without cross-sectional correlation is thoroughly discussed. This uncorrelatedness assumption is now often examined in empirical work, using tests such as those by Pesaran, Ullah, and Yamagata (2008), Hsiao, Pesaran, and Pick (2012), or Pesaran (2015). All these tests in turn improve upon the so-called error-components test suggested in Breusch and Pagan (1980). In this chapter, the author revisits this error-components test and derives its asymptotic distribution under various scenarios: (a) both time-series dimension T and cross-sectional dimension N go to ∞ jointly (Phillips & Moon, 1999); (b) T → ∞ while N is fixed, and (c) N → ∞ while T is fixed. To the best of the author’s knowledge, the results under Scenarios (b) and (c) are new. Moreover, while the distributions under (a) and (b) are normal, that under (c) is not and it is even asymmetric. The critical values under (c) can be simulated. A Monte Carlo experiment is performed and it aims to throw light on the choice among the critical values suggested in the three scenarios, given a T and an N.

Details

Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology
Type: Book
ISBN: 978-1-80262-065-8

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