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In this chapter we approach the estimation of dynamic stochastic general equilibrium models through a moments-based estimator, the empirical likelihood. We attempt to show that this inference process can be a valid alternative to maximum likelihood, which has been one of the preferred choices of the related literature to estimate these models. The empirical likelihood estimator is characterized by a simple setup and only requires knowledge about the moments of the data generating process of the model. In this context, we exploit the fact that these economies can be formulated as a set of moment conditions to infer on their parameters through this technique. For illustrational purposes, we consider a standard real business cycle model with a constant relative risk averse utility function and indivisible labor, driven by a normal technology shock.

In this paper, the authors applied the empirical likelihood method, which was originally proposed by Owen, to the copula moment based estimation methods to take advantage of its properties, effectiveness, flexibility and reliability of the nonparametric methods, which have limiting chi-square distributions and may be used to obtain tests or confidence intervals. The authors derive an asymptotically normal estimator of the empirical likelihood based on copula moment estimation methods (ELCM). Finally numerical performance with a simulation experiment of ELCM estimator is studied and compared to the CM estimator, with a good result.

In this paper we applied the empirical likelihood method which originally proposed by Owen, to the copula moment based estimation methods.

We derive an asymptotically normal estimator of the empirical likelihood based on copula moment estimation methods (ELCM). Finally numerical performance with a simulation experiment of ELCM estimator is studied and compared to the CM estimator, with a good result.

In this paper we applied the empirical likelihood method which originally proposed by Owen 1988, to the copula moment based estimation methods given by Brahimi and Necir 2012. We derive an new estimator of copula parameters and the asymptotic normality of the empirical likelihood based on copula moment estimation methods.

This study aims to propose a model-free statistic that tests asymmetric correlations of stock returns, in which stocks move more often with the market when the market goes down than when it goes up, and then empirically analyze the asymmetric correlations of the China stock market and international stock markets, respectively.

Using empirical likelihood method, this study designs and conducts a model-free test, which converges to χ2 distribution under regulated conditions and performs well in the finite sample using bootstrap critical value method.

By analyzing the authors' model-free test, the authors find that compared with Hong et al.'s test that closely relates to the authors, both of the tests are under rejected using asymptotic critical value. However, using the bootstrap critical value method can greatly improve the performance of the two tests. Second, investigating the power of the two tests, the authors find that the proportion of rejections of the authors' test is roughly 10-20 percent larger than Hong et al.'s test in mixed copula model setting. The last finding is the authors find evidence of asymmetric for small-cap size portfolios, but no evidence for middle-cap and large-cap size portfolios in the China stock market. Besides, the authors test asymmetric correlations between the USA and Japan, France and the UK; the asymmetric phenomenon exists in international stock markets, which is similar to Longin and Solnik's findings, but they are not significant according to both the authors' test and Hong et al.'s test.

The findings in this study suggest that both the authors' test and Hong et al.'s test are under rejected using asymptotic critical value. When applying these statistics to test asymmetric correlations, the authors should take care with the choice of critical value.

The empirical analysis has a significant practical implication for asset allocation, asset pricing and risk management fields.

This study constructs a model-free statistic to test asymmetric correlations using empirical likelihood method for the first time and corrects the size performance by bootstrap method, which improves the performance of Hong et al.'s test. To the authors' knowledge, this is the first study to test the asymmetric correlations in the China stock market.

We show how to use a smoothed empirical likelihood approach to conduct efficient semiparametric inference in models characterized as conditional moment equalities when data are collected by variable probability sampling. Results from a simulation experiment suggest that the smoothed empirical likelihood based estimator can estimate the model parameters very well in small to moderately sized stratified samples.

The purpose of this study is to investigate the impact of corporate governance structures on the likelihood of financial distress in UK listed companies. The paper examines the impact of borrowing and corporate governance structures on financial distress likelihood in UK companies.

The study uses a quantitative approach with financial, governance and borrowing measures and data from 270 firm-observations between 2010 and 2018. The study analyses the impact of borrowing and corporate governance structures to indicate financial distress likelihood in British companies. Corporate governance variables such as ownership concentration, independence indicators, chief executive officer duality, director remuneration and corporate loans are considered, as well as the UK Corporate Governance Code.

The results indicate that companies with low ownership concentration and a low degree of independence are more likely to incur financial distress. Larger boards and better director remuneration can reduce financial distress likelihood and the existence of corporate loans can increase this likelihood. Empirical consideration of corporate borrowing is a new contribution to the literature.

Variables are highlighted and aggregated that have not otherwise been studied together; the UK Corporate Governance Code’s main ideas are empirically supported; the study is useful for defining corporate governance structure strategies.

This chapter analyzes the empirical relationship between the pricesetting/consumption behavior and the sources of persistence in inflation and output. First, a small-scale New-Keynesian model (NKM) is examined using the method of moment and maximum likelihood estimators with US data from 1960 to 2007. Then a formal test is used to compare the fit of two competing specifications in the New-Keynesian Phillips Curve (NKPC) and the IS equation, that is, backward- and forward-looking behavior. Accordingly, the inclusion of a lagged term in the NKPC and the IS equation improves the fit of the model while offsetting the influence of inherited and extrinsic persistence; it is shown that intrinsic persistence plays a major role in approximating inflation and output dynamics for the Great Inflation period. However, the null hypothesis cannot be rejected at the 5% level for the Great Moderation period, that is, the NKM with purely forward-looking behavior and its hybrid variant are equivalent. Monte Carlo experiments investigate the validity of chosen moment conditions and the finite sample properties of the chosen estimation methods. Finally, the empirical performance of the formal test is discussed along the lines of the Akaike's and the Bayesian information criterion.

Despite service downgrades' undisputed practical relevance, service downgrades (e.g. customers shifting the price tier downward) have received surprisingly little attention from scholars. Previous studies have focussed on either the public policy issue of tiered pricing or optimal pricing by the service provider. Only a few studies have examined why customers shift across different price tiers and how such activities indicate their future behaviour.

Based on customer data collected from a major telecommunications company, the authors use a logistic regression model to investigate how two service modification levers (i.e. transaction- and relationship-level factors) influence the likelihood of service downgrade. The authors apply a survival model to study how service downgrades affect customer churn.

Transaction-level factors such as service usage (e.g. the frequency and recency of underuse experiences) are positively associated with the likelihood of a downgrade. However, relationship-level factors (e.g. relationship duration and customer status) are negatively associated with the likelihood of downgrades. Customers engaging in downgrades are more likely to churn in the future.

The authors focus on downgrade behaviour, which can be perceived as customers' choice to move down the price tier, which likely ruins the service provider's performance. The authors conceptualise two fundamental driving forces behind a service downgrade: the misfits between the actual usage and the service plan chosen and the deteriorating relationships. The authors' empirical findings on the factors influencing downgrades provide insights for service providers seeking to prevent such behaviour.

Social media platforms are changing the way consumers and celebrities engage. This research aims to better understand how and why consumers use social media to engage with celebrities, and identify the potential antecedents and outcomes, which may result from these online connections.

Both qualitative (two focus groups) and quantitative (survey) methods were used to explore consumer engagement with celebrities on social media. A structural model from the survey data was developed and analyzed.

Textual analysis of the focus groups revealed that consumers follow celebrities on social media to obtain career and personal information about the celebrity. Further, authenticity and emotional attachment were identified as favorable aspects of following celebrities on social media. An empirical study confirmed that the constructs of authenticity and emotional attachment positively influence the outcomes of word-of-mouth and purchase likelihood.

The study was limited by the self-identification of a favorite celebrity and social media site. Future research should include empirical testing of specific celebrities featured on a specific social media site and the development of the constructs identified in the focus groups.

This research sheds light on the antecedents and outcomes associated with consumer–celebrity engagement on social media. The implications for marketers and advertisers include a better understanding of how celebrities transform themselves and engage with consumers on social media.

This paper fulfills an identified need to study authenticity and emotional attachment as they relate to celebrities and consumers’ engagements on social media.

The authors discuss the econometric underpinnings of Barro (2006)'s defense of the rare disaster model as a way to bring back an asset pricing model “into the right ballpark for explaining the equity-premium and related asset-market puzzles.” Arbitrarily low-probability economic disasters can restore the validity of model-implied moment conditions only if the amplitude of disasters may be arbitrary large in due proportion. The authors prove an impossibility theorem that in case of potentially unbounded disasters, there is no such thing as a population empirical likelihood (EL)-based model-implied probability distribution. That is, one cannot identify some belief distortions for which the EL-based implied probabilities in sample, as computed by Julliard and Ghosh (2012), could be a consistent estimator. This may lead to consider alternative statistical discrepancy measures to avoid the problem with EL. Indeed, the authors prove that, under sufficient integrability conditions, power divergence Cressie-Read measures with positive power coefficients properly define a unique population model-implied probability measure. However, when this computation is useful because the reference asset pricing model is misspecified, each power divergence will deliver different model-implied beliefs distortion. One way to provide economic underpinnings to the choice of a particular belief distortion is to see it as the endogenous result of investor's choice when optimizing a recursive multiple-priors utility a la Chen and Epstein (2002). Jeong et al. (2015)'s econometric study confirms that this way of accommodating ambiguity aversion may help to address the Equity Premium puzzle.