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Consumer Confidence and Stock Markets: The Panel Causality Evidence  [cached]
Chih-Chiang Hsu,Hung-Yu Lin,Jyun-Yi Wu
International Journal of Economics and Finance , 2011, DOI: 10.5539/ijef.v3n6p91
Abstract: This paper uses a panel of country-level data to investigate the causal relationship between the consumer confidence index (CCI) and the stock market index (SMI). We apply the common correlated effects mean group (CCEMG) estimation of Pesaran (2006) to capture the cross-sectional dependence of our variables before examining this causal relationship. In the panel data analysis, we discover the two-way causality between the CCI and SMI. One of the ways is where stock returns Granger-cause the changes in the CCI. According to the information view of the CCI, this result is due to consumers regarding the stock returns as being the leading indicators of the future situation, regardless of whether they own the stocks or not. On the other hand, the changes in the CCI also Granger-cause the stock returns, the reason for this being attributable to the animal spirits view of consumers. When consumers believe in their own opinions, they will at the same time have strong confidence in and an optimistic attitude toward the future economic situation. Based on these conditions, consumers will invest more in the stock market.
Assessing the Distribution Consistency of Sequential Data  [PDF]
Mahendra Mariadassou,Avner Bar-Hen
Mathematics , 2009,
Abstract: Given n observations, we study the consistency of a batch of k new observations, in terms of their distribution function. We propose a non-parametric, non-likelihood test based on Edgeworth expansion of the distribution function. The keypoint is to approximate the distribution of the n+k observations by the distribution of n-k among the n observations. Edgeworth expansion gives the correcting term and the rate of convergence. We also study the discrete distribution case, for which Cram\`er's condition of smoothness is not satisfied. The rate of convergence for the various cases are compared.
On the Consistency and Confidence of Distributed Dynamic State Estimation in Wireless Sensor Networks  [PDF]
Shaocheng Wang,Wei Ren
Mathematics , 2015,
Abstract: The problem of distributed dynamic state estimation in wireless sensor networks is studied. Two important properties of local estimates, namely, the consistency and confidence, are emphasized. On one hand, the consistency, which means that the approximated error covariance is lower bounded by the true unknown one, has to be guaranteed so that the estimate is not over-confident. On the other hand, since the confidence indicates the accuracy of the estimate, the estimate should be as confident as possible. We first analyze two different information fusion strategies used in the case of information sources with, respectively, uncorrelated errors and unknown but correlated errors. Then a distributed hybrid information fusion algorithm is proposed, where each agent uses the information obtained not only by itself, but also from its neighbors through communication. The proposed algorithm not only guarantees the consistency of the estimates, but also utilizes the available information sources in a more efficient manner and hence improves the confidence. Besides, the proposed algorithm is fully distributed and guarantees convergence with the sufficient condition formulated. The comparisons with existing algorithms are shown.
The link between government spending, consumer confidence and consumption expenditures in emerging market countries
?zerkek Yasemin,?elik Sadullah
Panoeconomicus , 2010, DOI: 10.2298/pan1004471o
Abstract: The impact of government spending on private consumption is extensively studied in the literature. However, the main theme of these studies is the possible crowding-in or crowding-out impact of government spending on consumer spending. This paper attempts to introduce a new variable to this well-known literature by investigating the existence of a relationship between government expenditure, consumer spending and consumer confidence for a group of emerging market countries. We examine whether a change in consumer confidence causes any change in government spending. Moreover, we analyze whether there is a feedback from government spending and private consumption to consumer confidence. Our empirical findings demonstrate the important role of consumer confidence on government spending and private consumption expenditures.
Confidence Intervals for Assessing Sizes of Social Network Centralities  [PDF]
Dawn Iacobucci, Rebecca McBride, Deidre L. Popovich, Maria Rouziou
Social Networking (SN) , 2018, DOI: 10.4236/sn.2018.74017
Abstract:
This research uses random networks as benchmarks for inferential tests of network structures. Specifically, we develop formulas for expected values and confidence intervals for four frequently employed social network centrality indices. The first study begins with analyses of stylized networks, which are then perturbed with increasing levels of random noise. When the indices achieve their values for fully random networks, the indices reveal systematic relationships that generalize across network forms. The second study then delves into the relationships between numbers of actors in a network and the density of a network for each of the centrality indices. In doing so, expected values are easily calculated, which in turn enable chi-square tests of network structure. Furthermore, confidence intervals are developed to facilitate a network analyst’s understanding as to which patterns in the data are merely random, versus which are structurally significantly distinct.
Assessing phenotypic correlation through the multivariate phylogenetic latent liability model  [PDF]
Gabriela B. Cybis,Janet S. Sinsheimer,Trevor Bedford,Alison E. Mather,Philippe Lemey,Marc A. Suchard
Quantitative Biology , 2014, DOI: 10.1214/15-AOAS821
Abstract: Understanding which phenotypic traits are consistently correlated throughout evolution is a highly pertinent problem in modern evolutionary biology. Here, we propose a multivariate phylogenetic latent liability model for assessing the correlation between multiple types of data, while simultaneously controlling for their unknown shared evolutionary history informed through molecular sequences. The latent formulation enables us to consider in a single model combinations of continuous traits, discrete binary traits and discrete traits with multiple ordered and unordered states. Previous approaches have entertained a single data type generally along a fixed history, precluding estimation of correlation between traits and ignoring uncertainty in the history. We implement our model in a Bayesian phylogenetic framework, and discuss inference techniques for hypothesis testing. Finally, we showcase the method through applications to columbine flower morphology, antibiotic resistance in Salmonella and epitope evolution in influenza.
Assessing the consistency of community structure in complex networks  [PDF]
Matthew Steen,Satoru Hayasaka,Karen Joyce,Paul Laurienti
Physics , 2011, DOI: 10.1103/PhysRevE.84.016111
Abstract: In recent years, community structure has emerged as a key component of complex network analysis. As more data has been collected, researchers have begun investigating changing community structure across multiple networks. Several methods exist to analyze changing communities, but most of these are limited to evolution of a single network over time. In addition, most of the existing methods are more concerned with change at the community level than at the level of the individual node. In this paper, we introduce scaled inclusivity, which is a method to quantify the change in community structure across networks. Scaled inclusivity evaluates the consistency of the classiffication of every node in a network independently. In addition, the method can be applied cross-sectionally as well as longitudinally. In this paper, we calculate the scaled inclusivity for a set of simulated networks of United States cities and a set of real networks consisting of teams that play in the top division of American college football. We found that scaled inclusivity yields reasonable results for the consistency of individual nodes in both sets of networks. We propose that scaled inclusivity may provide a useful way to quantify the change in a network's community structure.
A study on effects of packaging characteristics on consumer's purchasing confidence  [PDF]
Naser Azad,Leila Hamdavipour
Management Science Letters , 2012,
Abstract: Packaging plays an important role on marketing products and services in many competitive environments. A good packaging can increase sales of products, reduces the level of inventory, which yields to higher profitability. In this paper, we study the relationship between a good packaging program and customer's confidence as well as customer's attraction on purchasing goods and services. The paper uses a questionnaire based on Likert scale and distributes among the target population of this survey and the information of packaging are divided into two groups of visibility and informative. The results indicate that a good and label with detailed and precise information on product could significantly impact customer's confidence while other visible information do not have much impact on customer's confidence.
Consistency of plug-in confidence sets for classification in semi-supervised learning  [PDF]
Christophe Denis,Mohamed Hebiri
Statistics , 2015,
Abstract: Confident prediction is highly relevant in machine learning; for example, in applications such as medical diagnoses, wrong prediction can be fatal. For classification, there already exist procedures that allow to not classify data when the confidence in their prediction is weak. This approach is known as classification with reject option. In the present paper, we provide new methodology for this approach. Predicting a new instance via a confidence set, we ensure an exact control of the probability of classification. Moreover, we show that this methodology is easily implementable and entails attractive theoretical and numerical properties.
Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics  [PDF]
Xianlei Dong,Johan Bollen
Computer Science , 2014, DOI: 10.1371/journal.pone.0120039
Abstract: Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.
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