Search results
1 – 10 of over 3000The objective of this chapter is to study the symmetric and asymmetric impact of macroeconomic variables on the Indian stock prices (SPs) of the Bombay Stock Exchange index. This…
Abstract
The objective of this chapter is to study the symmetric and asymmetric impact of macroeconomic variables on the Indian stock prices (SPs) of the Bombay Stock Exchange index. This chapter further investigates whether the asymmetric impact of macroeconomic variables on SP is due to the impact of any tail events like the global financial recession. An autoregressive distribution lag and non-autoregressive distribution lag approach is used for the full sample covering the period from January 2000 to June 2019 and later this sample is further subdivided into before and after the crisis period to study the variations in result. The findings show that macroeconomic variables and SP have a symmetric relation in the long run whereas an asymmetric relationship in the short run when the whole sample is analyzed. However when data are segregated into “before and after” crisis period this relationship turns to be asymmetric in long run too, meaning that in the long run, the negative and positive changes in a macroeconomic variable do not affect SPs similarly.
Details
Keywords
Shutian Wang, Yan Lin, Yejin Yan and Guoqing Zhu
This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.
Abstract
Purpose
This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.
Design/methodology/approach
The long-run equilibrium relationship and short-run dynamic effects between the valence and volume of UGC, online search traffic and offline car sales are analyzed by applying the autoregressive distribution lag (ARDL) model.
Findings
The study found the following. (1) In the long-run relationship, the valence of online reviews on social media platforms is significantly negatively correlated with the sales of all models. However, in the short-run, the valence of online reviews has a significant positive correlation with all models in different lag periods. (2) The volume of online reviews is significantly positively correlated with the sales of all models in the long run. However, in the short run, the relationship between the volume of online reviews and the sales of lower-sales-volume cars is uncertain. There is a significant positive correlation between the volume of reviews and the sales of higher-sales-volume cars. (3) Online search traffic has a significantly negative correlation with the sales of all models in the long run. However, in the short run, there is no consistent conclusion on the relationship between online search traffic and car sales.
Originality/value
This study provides a reference for managers to use in their efforts to improve offline high-involvement product sales using online information.
Details
Keywords
Bismark Osei, Mark Edem Kunawotor and Evans Kulu
The purpose of this paper is to undertake comparative analysis examining the effect of renewable energy production on employment focusing on European and Asian Countries.
Abstract
Purpose
The purpose of this paper is to undertake comparative analysis examining the effect of renewable energy production on employment focusing on European and Asian Countries.
Design/methodology/approach
The study utilizes panel data from the period 2000 to 2018 and System Generalized Method of Moments (System GMM) for the analysis. This study focuses on 50 European and 40 Asian countries data used for the analysis.
Findings
The result of the study indicates that, renewal energy production has positively affected employment in both European and Asian countries. However, the positive effect result of European countries is stronger than that of Asian countries.
Practical implications
The study recommends that, governments among these countries should continue to show strong commitment towards investing in renewable energy production as stated in Paris Agreement (2015). This will have a strong effect towards increasing further employment creation among these countries.
Originality/value
Numerous empirical studies have been carried out examining the effect of renewable energy production on employment. This study contributes to existing empirical studies by undertaking comparative analysis to examine the subject matter focusing on European and Asian countries.
Details
Keywords
The purpose of this paper is to empirically examine the long run and causal relationship between energy consumption, carbon emissions and economic growth in India over the period…
Abstract
Purpose
The purpose of this paper is to empirically examine the long run and causal relationship between energy consumption, carbon emissions and economic growth in India over the period 1971-2009 within multivariate framework.
Design/methodology/approach
The study uses the Johansen cointegration test to examine the possible long-run equilibrium relationship followed by Granger causality test based on vector error correction model to explore short- and long-run causality between energy consumption, carbon emissions and economic growth in India.
Findings
Cointegration result indicates the long-run equilibrium relationship between economic growth, energy consumption and carbon emissions. Further causality results suggest unidirectional causality running from energy consumption and carbon emissions to economic growth in long run, energy consumption to carbon emissions, carbon emissions to economic growth and economic growth to energy consumption in short run.
Practical implications
There is urgent need of policy development toward boosting energy efficiency, developing alternative carbon-free energy sources like nuclear, renewables and expansion of affordable energy for faster, sustainable and more inclusive growth for India in upcoming years.
Originality/value
India, an energy-dependent economy needs to effectively implement energy efficiency measures, super critical technologies in power plants, and investment in renewable energy resources in order to minimize the dependence on fossil fuels and carbon emissions for faster, more inclusive and sustainable growth.
Details
Keywords
Shi Yin, Zengying Gao and Tahir Mahmood
The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of…
Abstract
Purpose
The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.
Design/methodology/approach
Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.
Findings
Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.
Originality/value
This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.
Details
Keywords
Kirstin Hubrich and Timo Teräsvirta
This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression…
Abstract
This survey focuses on two families of nonlinear vector time series models, the family of vector threshold regression (VTR) models and that of vector smooth transition regression (VSTR) models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the equations. The emphasis is on stationary models, but the considerations also include nonstationary VTR and VSTR models with cointegrated variables. Model specification, estimation and evaluation is considered, and the use of the models illustrated by macroeconomic examples from the literature.
Details
Keywords
Fabio Gobbi and Sabrina Mulinacci
The purpose of this paper is to introduce a generalization of the time-varying correlation elliptical copula models and to analyse its impact on the tail risk of a portfolio of…
Abstract
Purpose
The purpose of this paper is to introduce a generalization of the time-varying correlation elliptical copula models and to analyse its impact on the tail risk of a portfolio of foreign currencies during the Covid-19 pandemic.
Design/methodology/approach
The authors consider a multivariate time series model where marginal dynamics are driven by an autoregressive moving average (ARMA)–Glosten-Jagannathan-Runkle–generalized autoregressive conditional heteroscedastic (GARCH) model, and the dependence structure among the residuals is given by an elliptical copula function. The correlation coefficient follows an autoregressive equation where the autoregressive coefficient is a function of the past values of the correlation. The model is applied to a portfolio of a couple of exchange rates, specifically US dollar–Japanese Yen and US dollar–Euro and compared with two alternative specifications of the correlation coefficient: constant and with autoregressive dynamics.
Findings
The use of the new model results in a more conservative evaluation of the tail risk of the portfolio measured by the value-at-risk and the expected shortfall suggesting a more prudential capital allocation policy.
Originality/value
The main contribution of the paper consists in the introduction of a time-varying correlation model where the past values of the correlation coefficient impact on the autoregressive structure.
Details
Keywords
Chafik Bouhaddioui, Jean-Marie Dufour and Masaya Takano
The authors propose a semiparametric approach for testing independence between two infinite-order cointegrated vector autoregressive series (IVAR(∞)). The procedures considered…
Abstract
The authors propose a semiparametric approach for testing independence between two infinite-order cointegrated vector autoregressive series (IVAR(∞)). The procedures considered can be viewed as extensions of classical methods proposed by Haugh (1976, JASA) and Hong (1996b, Biometrika) for testing independence between stationary univariate time series. The tests are based on the residuals of long autoregressions, hence allowing for computational simplicity, weak assumptions on the form of the underlying process, and a direct interpretation of the results in terms of innovations (or shocks). The test statistics are standardized versions of the sum of weighted squares of residual cross-correlation matrices. The weights depend on a kernel function and a truncation parameter. Multivariate portmanteau statistics can be viewed as a special case of our procedure based on the truncated uniform kernel. The asymptotic distributions of the test statistics under the null hypothesis are derived, and consistency is established against fixed alternatives of serial cross-correlation of unknown form. A simulation study is presented which indicates that the proposed tests have good size and power properties in finite samples.
Details
Keywords
James P. LeSage and R. Kelley Pace
For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with…
Abstract
For this discussion, assume there are n sample observations of the dependent variable y at unique locations. In spatial samples, often each observation is uniquely associated with a particular location or region, so that observations and regions are equivalent. Spatial dependence arises when an observation at one location, say y i is dependent on “neighboring” observations y j, y j∈ϒi. We use ϒi to denote the set of observations that are “neighboring” to observation i, where some metric is used to define the set of observations that are spatially connected to observation i. For general definitions of the sets ϒi,i=1,…,n, typically at least one observation exhibits simultaneous dependence, so that an observation y j, also depends on y i. That is, the set ϒj contains the observation y i, creating simultaneous dependence among observations. This situation constitutes a difference between time series analysis and spatial analysis. In time series, temporal dependence relations could be such that a “one-period-behind relation” exists, ruling out simultaneous dependence among observations. The time series one-observation-behind relation could arise if spatial observations were located along a line and the dependence of each observation were strictly on the observation located to the left. However, this is not in general true of spatial samples, requiring construction of estimation and inference methods that accommodate the more plausible case of simultaneous dependence among observations.