Labor productivity forecasts based on a Beveridge–Nelson filter: Is there statistical evidence for a slowdown?

In this paper, I assess the evidence for a structural break in labor productivity growth in the years before the Great Recession with the use of out-of-sample forecasting exercises for the years 2010 to 2019 and the recently developed Beveridge–Nelson filter. Models based on a Beveridge–Nelson filter with no structural breaks outperform those allowing for a structural break, and there is statistically significant evidence that they outperform the random walk, though all models were too optimistic about labor productivity growth. Recently developed statistical tests do point to the presence of a structural break before the Great Recession, but uncertainty about the data-generating process for labor productivity growth or the timing and magnitude of the break may be too great to be helpful in forecast preparation.     

Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

Managing organizational entry in China: the roles of newcomer–supervisor exchange,incumbent support,and perceived insider status

The socialization literature has long recognized the important role of perceived insider status in facilitating newcomer adjustment, but knowledge on how to cultivate this desirable psychological state is limited. This study examines newcomer–supervisor exchange as an antecedent of perceived insider status of newcomers, which in turn facilitates their social adjustment and task mastery during organizational entry. We also explore incumbent support as a boundary condition for these effects. We conducted a survey study on 320 new employees and 92 supervisors in China. This study revealed that newcomer–supervisor exchange was related positively to newcomers’ perceived insider status, and perceived insider status mediated the positive relationships of newcomer–supervisor exchange with social adjustment and task mastery. In addition, incumbent support moderated the positive relationship between newcomer–supervisor exchange and perceived insider status, as well as the positive mediated relationships of newcomer–supervisor exchange with social adjustment and task mastery through perceived insider status. Specifically, these relationships were weaker when incumbent support was higher. Theoretical and practical implications were discussed.     

The effect of external supply knowledge acquisition,development activities and organizational status on the supply performance of SMEs

Given the increasingly strategic role of external resources, acquiring knowledge about current suppliers and the broader supply market is an important and demanding task for the purchasing and supply management (PSM) function of a firm. Performance-improvement-oriented application of external supply knowledge present further challenges for the function. To examine this, we draw on the knowledge-based view and develop a hypothesized model in which supply knowledge acquisition drives PSM exploration and exploitation orientations which in turn mediate the organizational status of PSM function in terms of supply performance. We test the model on an SME-focused and survey-based dataset, using structural equation modelling. Our results indicate that an exploitative orientation is associated with knowledge gained from the supply base, whereas an explorative orientation is predominantly associated with supply market knowledge and less with supply base knowledge, suggesting natural pairings. The findings also show how an exploitative development orientation mediates the positive association of the PSM function's organizational status with supply performance. Driven by supply base knowledge, a status-empowered exploitative PSM orientation may suppress supply market based explorative orientation in resource-scarce SMEs, thus appearing to serve as the sole path to supply performance. Our research contributes by pointing out the significance of the knowledge-resource, and the knowledge-based view, in understanding performance in PSM.     

Semistatic hedging and pricing American floating strike lookback options

We price an American floating strike lookback option under the Black–Scholes model with a hypothetic static hedging portfolio (HSHP) composed of nontradable European options. Our approach is more efficient than the tree methods because recalculating the option prices is much quicker. Applying put–call duality to an HSHP yields a tradable semistatic hedging portfolio (SSHP). Numerical results indicate that an SSHP has better hedging performance than a delta-hedged portfolio. Finally, we investigate the model risk for SSHP under a stochastic volatility assumption and find that the model risk is related to the correlation between asset price and volatility.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.     

New development: China renews reform of central–local fiscal relations and provides for basic national public services

China’s reform on central–local fiscal reform has slowed down in recent years. The appointment of a new finance minister experienced in local government affairs is expected to renew the reform affirmed by the Chinese Communist Party (CCP) congress in late 2017. China has unprecedentedly identified a comprehensive list of 81 national basic public services as entitlements. Eighteen of them are subject to national and local standards, and co-financed by central and local governments. A new cost-sharing method for 10 of these services classifies sub-national jurisdictions into five tiers, in which the central government’s share declines from 80% to 10%. These measures, effective in 2019, aim at creating a ‘harmonious and moderately prosperous society’.     

Non-linear Gaussian sovereign CDS pricing models

Prior literature indicates that quadratic models and the Black–Karasinski model are very promising for CDS pricing. This paper extends these models and the Black [J. Finance 1995, 50, 1371–1376] model for pricing sovereign CDS’s. For all 10 sovereigns in the sample quadratic models best fit CDS spreads in-sample, and a four factor quadratic model can account for the joint effects on CDS spreads of default risk, default loss risk and liquidity risk with no restriction to factors correlation. Liquidity risk appears to affect sovereign CDS spreads. However, quadratic models tend to over-fit some CDS maturities at the expense of other maturities, while the BK model is particularly immune from this tendency. The Black model seems preferable because its out-of-sample performance in the time series dimension is the best.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.