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Several lessons learnt from a Bayesian analysis of basic macroeconomic time-series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time-series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical, and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented.

Government R&D subsidies is a major practice to respond to market failures in most countries. The purpose of this study is to examine the effect of the government subsidies on China’s regional innovation output empirically under the regional innovation framework, for the unique regional innovation system and strong national influence of state during the period of transformation.

Based on the construction of regional innovation framework, this study empirically examined the effect of Chinese Government R&D subsidies on regional innovation during the economic transition period using the Bayesian model averaging method and carried out the robustness test under different priori assumptions.

The empirical results showed that R&D capital and human investment has a very significant impact on promoting the regional innovation output of China’s high-tech industries. Meanwhile, the Chinese Government's R&D subsidies failed, thus the goal of improving regional innovation output has not been achieved. In reverse, the effects of regional economic development level and the financial environment on regional innovation are negative but the explanatory power is minimal. Additionally, opening-up has greatly promoted regional innovation output.

The empirical findings provide scientific policy decision-making and management implications for government and firm, respectively, and its experience is a very important reference for other emerging economies. Additionally, China serves as an interesting case to examine whether government R&D subsidy is effective in an immature market.

Using data on 5,491 commercial banks in the USA that were operational between 2001 second quarter and 2016 first quarter, the present study aims to examine the impact of derivative securities and its different constituent categories on bank-specific risks and profitability.

The study uses panel data fixed effects model and Bayesian model averaging techniques.

This study finds aggregate derivatives and both interest-rate and exchange-rate derivatives and their different constituent categories to reduce banks insolvency risks for the entire time period and the pre-crisis era. Moreover, aggregate derivatives increase banks’ risk-adjusted return on assets that are driven by exchange-rate derivatives. Such findings are robust to the size of banks, the degree of derivative use and extent of profitability. However, in the post-crisis period, derivatives reduce bank profits.

While the results largely provide evidence of the beneficial effects of derivatives, the findings for the post-crisis period are rather concerning. It underscores a clear need to improve regulation and supervision across different categories of derivatives to ensure the benefits exceed their costs for banks.

Disaggregate analysis of derivatives can not only unmask important differences in how they affect banks risks, profits, etc. but also help banks mitigate risks arising from specific types of derivative securities banks hold. Furthermore, discerning the impact of derivatives on banks risks and profits in the post-crisis era vis-à-vis the pre-crisis one is extremely important to restore a sounder banking system and foster overall financial stability.

Despite the growing importance of online word-of-mouth (WOM) with regard to television (TV) ratings, it is usually excluded from early prediction models. The purpose of this paper is to investigate the role of online WOM in TV ratings predictions, focussing on whether the incorporation of online WOM could improve predictions of TV ratings, and extracts meaningful rules for decision-making.

The author uses online WOM as a potential predictive variable in the TV ratings prediction model. The author matches a list of programs based on TV ratings for the movie channel with internet user reviews and TV ratings information from Yahoo! Movies (YM) and XYZ Company. The data set includes 71 movies, for which the data were analyzed with a hybrid model.

Grey relational analysis shows that online WOM is a useful ex ante determinant of TV ratings. As a predictive variable, it plays an essential role in enhancing TV ratings predictions. The experimental results also indicate that the proposed model surpasses other listed methods in terms of both accuracy and reduction of variables, while the proposed procedure yields a set of easily understandable decision rules that facilitate the interpretation of TV ratings information.

This paper identifies critical predictors of TV ratings and suggests that online WOM messages are a credible source. A hybrid model is developed to illustrate an intelligent prediction system for TV ratings.

The study demonstrates the effectiveness of online WOM and its impact on TV ratings. It offers an intelligent prediction system for TV ratings with practical implications for managers within the TV industry.

The increased capital requirements and the implementation of new liquidity standards under Basel III sparked various concerns among researchers, academics and other stakeholders. The question is whether Basel III regulation is ideal, that is, adequate to deal with a crisis, such as the 2007–2009 global financial crisis? The purpose of this paper is threefold: First, perform a stress testing exercise on the US banking sector, while examining liquidity and solvency risk indicators jointly under the Basel III regulatory framework. Second, allow the study to cover the post-crisis period, while referring to key Basel III regulatory requirements. And third, focus on the resilience of domestic systemically important banks (D-SIBs), which are supposed to support the US financial system in times of stress and therefore whose failure causes the entire financial system to fail.

The authors used a sample of the 24 largest US banks observed over the period Q1-2015 to Q1-2021 and a scenario-based vector autoregressive conditional forecasting approach.

The authors found that the model successfully produces accurate forecasts and simulates the responses of the solvency and liquidity indicators to different real and historical macroeconomic shocks. The authors also found that the US banking sector is resilient and can withstand both historical and hypothetical macroeconomic shocks because of its compliance with the Basel III capital and liquidity regulations, which consist of encouraging banks to hold high-quality liquid assets and stable funding resources and to strengthen their capital, which absorbs the losses incurred in a crisis.

The authors developed a framework for testing the resilience of the US banking sector under macroeconomic shocks, while examining liquidity and solvency risk indicators jointly under Basel III regulatory framework, a point not yet well studied elsewhere, and most studies on this subject are based on precrisis data. The authors also focused on the resilience of D-SIBs, whose failure causes the failure of the entire financial system, which previous studies have failed to examine.

This is a special issue of Chinese Management Studies and this study aims to engage with debates on innovation in China and to provide new insights for innovation research in the context of China, seeking to develop a greater understanding of the concept of “innovation with Chinese characteristics”.

This study reviews the Chinese innovation management literature in general and the selected papers in this special issue in particular and proposes two new directions for future research.

The nine papers that constitute this special issue present research on important aspects of innovation in China, ranging from the effectiveness of government subsidisation for innovation, the impact of fiscal decentralisation on innovation, the role of management behaviour in promoting (or discouraging) innovation and the effects of differing business models on innovation. These papers shed valuable new light on the theory and practice of innovation in China. The papers are discussed in the context of four primary arguments about innovation management in China identified from the broader literature in the field. These relate to the pattern of China’s innovation performance over time, the reasons for its effectiveness, the role of alliances and influences of indigenous factors. It is also shown that management of the internationalisation of innovation and of efficient internal innovation are two important directions for future research on Chinese innovation in an era of de-globalisation.

The studies presented here provide valuable contributions to theory building in innovation research, as well as some important ideas for directions of future research on innovation in China in the new era of de-globalisation.

Examine the effects of sudden environmental disasters on the advancement of both renewable and conventional energy technologies.

Utilizing panel data from 31 Chinese provinces spanning 2011 to 2022, the SEM (Spatial Error Model) dual fixed model is utilized to examine the impact of sudden environmental disasters on energy technologies.

The findings reveal that: (1) Sudden environmental disasters exert a markedly positive influence on the Innovation of Renewable Energy Technologies (IRET), while their impact on conventional energy technologies is positively non-significant. (2) Sudden environmental disasters not only significantly enhance innovation in local renewable energy technologies but also extend this positive influence to neighboring regions, demonstrating a spatial spillover phenomenon. (3) Research and Development (R&D) funding serves as a partial mediator in the relationship between sudden environmental disasters and renewable ETI. In contrast, Foreign Direct Investment (FDI) exhibits a masking effect.

Consequently, the study advocates for intensified efforts in post-disaster reconstruction following abrupt environmental events, an elevation in the quality of foreign direct investments, and leveraging research funding to catalyze innovation in renewable energy technologies amid unforeseen environmental crises.

We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single-factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state-space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.

Small-scale VARs are widely used in macroeconomics for forecasting US output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time-varying parameters, break dating, Bayesian shrinkage, model averaging, etc. This paper compares the effectiveness of such methods in real-time forecasting. We use forecasts from univariate time series models, the Survey of Professional Forecasters, and the Federal Reserve Board's Greenbook as benchmarks.