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1 – 10 of over 5000Sampling units for the 2013 Methods-of-Payment survey were selected through an approximate stratified two-stage sampling design. To compensate for nonresponse and noncoverage and…
Abstract
Sampling units for the 2013 Methods-of-Payment survey were selected through an approximate stratified two-stage sampling design. To compensate for nonresponse and noncoverage and ensure consistency with external population counts, the observations are weighted through a raking procedure. We apply bootstrap resampling methods to estimate the variance, allowing for randomness from both the sampling design and raking procedure. We find that the variance is smaller when estimated through the bootstrap resampling method than through the naive linearization method, where the latter does not take into account the correlation between the variables used for weighting and the outcome variable of interest.
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Tae-Hwy Lee and Yang Yang
Bagging (bootstrap aggregating) is a smoothing method to improve predictive ability under the presence of parameter estimation uncertainty and model uncertainty. In Lee and Yang…
Abstract
Bagging (bootstrap aggregating) is a smoothing method to improve predictive ability under the presence of parameter estimation uncertainty and model uncertainty. In Lee and Yang (2006), we examined how (equal-weighted and BMA-weighted) bagging works for one-step-ahead binary prediction with an asymmetric cost function for time series, where we considered simple cases with particular choices of a linlin tick loss function and an algorithm to estimate a linear quantile regression model. In the present chapter, we examine how bagging predictors work with different aggregating (averaging) schemes, for multi-step forecast horizons, with a general class of tick loss functions, with different estimation algorithms, for nonlinear quantile regression models, and for different data frequencies. Bagging quantile predictors are constructed via (weighted) averaging over predictors trained on bootstrapped training samples, and bagging binary predictors are conducted via (majority) voting on predictors trained on the bootstrapped training samples. We find that median bagging and trimmed-mean bagging can alleviate the problem of extreme predictors from bootstrap samples and have better performance than equally weighted bagging predictors; that bagging works better at longer forecast horizons; that bagging works well with highly nonlinear quantile regression models (e.g., artificial neural network), and with general tick loss functions. We also find that the performance of bagging may be affected by using different quantile estimation algorithms (in small samples, even if the estimation is consistent) and by using different frequencies of time series data.
This paper aims to test the finite sample properties of the automatic variance ratio (AVR) test and suggest suitable measure to improve its small sample properties under…
Abstract
Purpose
This paper aims to test the finite sample properties of the automatic variance ratio (AVR) test and suggest suitable measure to improve its small sample properties under conditional heteroskedasticity and apply it to test the martingale hypothesis in the stock prices of the Portugal, Ireland, Italy, Greece and Spain (PIIGS economies) markets. This paper also seeks to investigate that “If the time series is not martingale, then what else?”
Design/methodology/approach
Monte Carlo experiments have been undertaken to test the small sample properties of automatic variance ratio (AVR) test. The study uses AVR test on daily and weekly data of the indices to investigate their martingale behaviour. It uses detrended fluctuation analysis (DFA) and BDS test statistics to answer, “If not martingale, then what else?”. The study also applies moving subsample approach to examine the dynamic behavior of stock prices and to obtain inferential findings robust to possible structural changes and presence of influential outliers.
Findings
The author finds that weighted bootstrap procedure significantly improves the small sample properties of AVR tests under conditional heteroskedasticity. The results provide evidence in support of the weak‐form efficiency of Italy and Spain. But Portugal, Ireland and Greece exhibit signs of long memory in the stock prices. All indices also exhibit chaotic characteristics.
Originality/value
This paper has both methodological and empirical originality. On the methodological aspect, the author proposes weighted bootstrap procedure on AVR test to improve its small sample properties. On the empirical side, the study finds that all stocks exhibit dynamic behavioral characteristics which change over time.
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Daniel J. Henderson and Christopher F. Parmeter
Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be…
Abstract
Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the available methods in the literature, discuss the challenges that present themselves when empirically implementing these methods, and extend an existing method to handle general nonlinear constraints. A heuristic discussion on the empirical implementation for methods that use sequential quadratic programming is provided for the reader, and simulated and empirical evidence on the distinction between constrained and unconstrained nonparametric regression surfaces is covered.
The purpose of this study is to investigate empirically the pattern of co-movement between prices and implied volatility in the future markets for crude oil.
Abstract
Purpose
The purpose of this study is to investigate empirically the pattern of co-movement between prices and implied volatility in the future markets for crude oil.
Design/methodology/approach
The tool of non-parametric quantile regression is applied to daily price returns and implied volatility changes from 2007 to 2018.
Findings
For the total sample period, the link between price returns and forward-looking volatility expectations is contemporaneous, negative and asymmetric, and it exhibits an (approximately) inverted U-shaped pattern suggesting that: the pricing of implied volatility is heavier for large (in absolute value terms) changes relative to small ones and it is lighter for large positive changes relative to large negative ones. The pattern of co-movement, therefore, appears to be in line with the theoretical postulates of fear, exuberance and loss aversion. The main characteristics of the relationship are present in some (but not in all) sub-periods, which are also considered in this study.
Originality/value
Less than a handful of works have assessed the link between implied volatility and prices for commodity ETFs. This is the first one relying on flexible non-parametric quantile regressions.
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Dilnaz Muneeb, Syed Zamberi Ahmad, Abdul Rahim Abu Bakar and Shehnaz Tehseen
This study aims to provide insights on the importance of reconfiguring new and existing enterprise resources in a heterogeneous manner. This will lead to improved efficiencies…
Abstract
Purpose
This study aims to provide insights on the importance of reconfiguring new and existing enterprise resources in a heterogeneous manner. This will lead to improved efficiencies, strategies and resource usage as such leading to more synergetic and innovative outcomes. This study highlights the importance of dynamic capabilities (DC) during the process of resources recombination (RR). It suggests that DC can be a source of competitive advantage, but the effect is contingent on the RR capabilities of enterprises.
Design/methodology/approach
Data were obtained from 349 faculty members of higher education institutions (HEIs) from seven states in the United Arab Emirates (UAE). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS was employed as a statistical tool to analyze the structural model.
Findings
The findings confirm the proposed role of DC in the realization of RR, in integrating and reconfiguring internal and external organizational skills and resources for efficiency and performance, since DC helps RR to reconfigure the resource base by extending, creating, and modifying innovative RRs.
Practical implications
The study has important implications for resource managers and policymakers of HEIs. By prioritizing DC, firms can develop novel products and services as a result of a heterogenous mix of new RR. Additionally, since firms have limited resources in ever-changing, complex environmental conditions, this study provides explicit directions on how enterprises can strategically manage their resources in an innovative manner to attain a sustainable competitive advantage.
Originality/value
Insights from the DC and RR perspective in HEI sectors, particularly in the Middle East region, are scarce. This is the first empirical study to delve in this area and exemplify the relationship between these significant constructs.
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Igor Stojanovic, Luisa Andreu and Rafael Curras-Perez
This paper aims to further the knowledge of what effect destination and tourist social media communications have on destination brand equity.
Abstract
Purpose
This paper aims to further the knowledge of what effect destination and tourist social media communications have on destination brand equity.
Design/methodology/approach
The authors performed a quantitative study with 433 international tourists and social media users using an online survey and structural equation modeling.
Findings
The results show that user-generated content (UGC) and destination-generated content (DGC) both positively affect tourist behavior through the mediating role of destination brand equity. Of the two, UGC is more important for building a positive destination image and more valuable for improving perceived destination quality and value. The results also show that affective image is a powerful predictor of tourist behavior.
Practical implications
The findings provide useful insights for destination management organizations (DMOs) and social media marketing strategies. DMOs need to generate content that was highly relatable and evokes emotion, and encourage tourists to share their own experiences to improve destination brand equity and future behavior.
Originality/value
The study was conducted in the passive, pretrip stage before a travel decision is taken, which offers unique insight into how social media communications affect: destination brand equity and users’ decisions to choose certain destinations over others.
研究目的
本文旨在进一步了解目的地和旅游社交媒体传播对目的地品牌资产的影响。
研究设计/方法/途径
本论文使用在线调查和结构方程模型对 433 名国际游客和社交媒体用户进行了定量研究。
研究结果
结果表明, 用户生成的内容(UGC)和目的地生成的内容(DGC)都通过目的地品牌资产的中介作用对游客行为产生积极影响。两者中, UGC对于建立积极的目的地形象更为重要, 对于提高感知的目的地质量和价值更有价值。结果还表明, 情感形象是旅游行为的有力预测因素。
实际意义
研究结果为目的地管理组织 (DMO) 和社交媒体营销策略提供了有用的见解。 DMO 需要生成具有高度相关性和唤起情感的内容, 并鼓励游客分享自己的经验, 以提高目的地品牌资产和未来行为。
原创性/价值
该研究是在做出旅行决定之前的被动旅行前阶段进行的, 它提供了关于社交媒体传播如何影响:(i) 目的地品牌资产, 以及 (ii) 用户的选择决定某些目的地优于其他目的地的独特见解。
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Li Wang, Allison Williams and Peter Kitchen
The purpose of this paper is to investigate the impact of various employment characteristics on the health of Canadian caregiver-employees (CEs), who are working full-time in the…
Abstract
Purpose
The purpose of this paper is to investigate the impact of various employment characteristics on the health of Canadian caregiver-employees (CEs), who are working full-time in the labor market while also providing informal/family care to adults.
Design/methodology/approach
Framed with Pearlin et al.’s (1990) stress model and using data from Statistic Canada’s General Social Survey Cycle 26 (2012), several work-related variables for caregivers were considered, including the availability of various forms of caregiver-friendly workplace policies (CFWPs), and a series of work interferences (WIs) experienced as a result of the caregiving role.
Findings
This study provides evidence for the value of CFWPs in all workplaces. Counter-intuitively, family and other forms of support were found to negatively relate to both physical and mental health.
Originality/value
This suggests that CFWPs will not only have an impact on CEs’ physical health outcomes, but will likely decrease the effect of the WIs experienced.
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Rajesh Kumar Bhaskaran and Sujit Kovilathumpaday Sukumaran
The current study proposes an integrative framework for examination of determinants of stock returns in US market based on the five-factor Fama and French (FF) model…
Abstract
Purpose
The current study proposes an integrative framework for examination of determinants of stock returns in US market based on the five-factor Fama and French (FF) model, macroeconomic variables and investor sentimental factors. The study is based on both value weighted and equally weighted monthly portfolio returns of all CRSP firms which are incorporated in the United States and listed on the NYSE, AMEX or NASDAQ.
Design/methodology/approach
The study applies PLS-SEM methodology to examine the major determinants of portfolio return.
Findings
The study suggests that investor sentiments are the major driving forces which positively influence the portfolio stock returns. The macroeconomic factors, the FF Factors and Momentum factor have negative influences on portfolio stock returns.
Originality/value
The study is the first of its kind which aim to determine the determinants of portfolio returns using the PLS-SEM methodology.
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The authors develop a novel forecast combination approach based on the order statistics of individual predictability from panel data forecasts. To this end, the authors define the…
Abstract
The authors develop a novel forecast combination approach based on the order statistics of individual predictability from panel data forecasts. To this end, the authors define the notion of forecast depth, which provides a ranking among different forecasts based on their normalized forecast errors during the training period. The forecast combination is in the form of a depth-weighted trimmed mean. The authors derive the limiting distribution of the depth-weighted forecast combination, based on which the authors can readily construct prediction intervals. Using this novel forecast combination, the authors predict the national level of new COVID-19 cases in the United States and compare it with other approaches including the ensemble forecast from the Centers for Disease Control and Prevention (CDC). The authors find that the depth-weighted forecast combination yields more accurate and robust predictions compared with other popular forecast combinations and reports much narrower prediction intervals.
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