Forecasting turning points in tourism growth

Tourism demand exhibits growth cycles, and it is important to forecast turning points in these growth cycles to minimise risks to destination management. This study estimates logistic models of Hong Kong tourism demand, which are then used to generate both short- and long-term forecasts of tourism growth. The performance of the models is evaluated using the quadratic probability score and hit rates. The results show that the ways in which this information is used are crucial to the models’ predictive power. Further, we investigate whether combining probability forecasts can improve predictive accuracy, and find that combination approaches, especially nonlinear combination approaches, are sensitive to the quality of forecasts in the pool. In addition, model screening can improve forecasting performance.     

Sibling gender and wage differences

Family influences on economic performance are investigated. In particular, sibship sex composition is related to hourly wages using data from the National Longitudinal Survey of Youth 1979. The wages of men are increasing in the proportion of siblings who are brothers, but the wages of women are insensitive to sibling gender. Nonwage outcomes are generally unaffected. Contrasts by age structure and demographic group are also presented. The analysis addresses econometric challenges like the endogeneity of fertility and selection into the workforce. In addition, mechanisms such as labour market interactions, human capital investment and role model effects are documented. A questionnaire on job search indicates a same-gender bias in the use of brothers and sisters in obtaining employment. Developmental and psychological assessments suggest that brothers may be associated with worse childhood home environments and more traditional family attitudes among women. The findings are policy relevant and contribute to an understanding of gender differences and earnings inequality.     

Informality,public employment and employment protection in developing countries

This paper proposes an equilibrium matching model for developing countries’ labor markets where the interaction between public, formal private and informal private sectors are taken into account. Theoretical analysis shows that gains from reforms aiming at liberalizing formal labor markets can be annulled by shifts in the public sector employment and wage policies. Since the public sector accounts for a substantial share of employment in developing countries, this approach is crucial to understand the main labor market outcomes of such economies. Wages offered by the public sector increase the outside option value of the workers during the bargaining processes in the formal and informal sectors. It becomes more profitable for workers to search on-the-job, in order to move to these more attractive and more stable types of jobs. The public sector therefore acts as an additional tax for the formal private firms. Using data on workers’ flows from Egypt, we show empirically and theoretically that the liberalization of labor markets plays against informal employment by increasing the profitability, and hence job creations, of formal jobs. The latter effect is however dampened or even sometimes nullified by the increase of the offered wages in the public sector observed at the same time.     

Why is Price Dispersion Higher Online than Offline? The Impact of Retailer Type and Shopping Risk on Price Dispersion

When physically similar products, of similar quality, are offered by retailers both online and offline, we often observe that the dispersion in prices of these products online is greater than the price dispersion offline. This observation runs counter to early theories that suggested price dispersion online would be smaller than that offline due to the ease of search and information availability online. This paper investigates and provides an explanation for this puzzling phenomenon by examining the impact of two important drivers of price dispersion: retailer type and consumers’ shopping risk. Retailer type refers to whether a retailer is a pure offline, pure online, or dual channel retailer. Shopping risk is defined as the product of consumers’ perceived risk of shopping and the transaction uncertainty related to shopping at different types of retailers.A game-theoretic approach is adopted to model consumers’ price search and product purchase, as well as price competition within and across retailer types in online and offline markets. Equilibrium pricing strategies are derived for different retailer types competing for different consumer segments with different levels of perceived shopping risk. The impact of retailer type and shopping risk on online versus offline price dispersion are quantified, and conditions when price dispersion is greater online than offline are identified.Results indicate that price dispersion is greater online when the number of pure online retailers is sufficiently large and is increasing in the number of pure online retailers. In addition, a reduction in online shopping risk may actually increase online price dispersion. Results further suggest that even without any online sales, dual channel retailers should maintain their online presence for the purpose of information dissemination, which justifies the importance for pure offline retailer to incorporate webrooming strategies, where consumers can search for prices online but purchase offline.     

Forecasting dynamic return distributions based on ordered binary choice

We present a simple approach to the forecasting of conditional probability distributions of asset returns. We work with a parsimonious specification of ordered binary choice regressions that imposes a connection on sign predictability across different quantiles. The model forecasts the future conditional probability distributions of returns quite precisely when using a past indicator and a past volatility proxy as predictors. The direct benefits of the model are revealed in an empirical application to the 29 most liquid U.S. stocks. The forecast probability distribution is translated to significant economic gains in a simple trading strategy. Our approach can also be useful in many other applications in which conditional distribution forecasts are desired.     

Welfare Cost of Fluctuations When Labor Market Search Interacts with Financial Frictions

We study the welfare costs of business cycles in a search and matching model with financial frictions. The model replicates the volatility on labor and financial markets. Business cycle costs are sizable. Indeed, the interactions between labor market and financial frictions magnify the impact of shocks via (i) a credit multiplier effect and (ii) an endogenous wage rigidity inherent to financial frictions. In addition, in a nonlinear framework, large welfare costs of fluctuations are explained by the high average unemployment and the low job finding rates with respect to their deterministic steady‐state values.     

跳频信号的归一化多普勒频差最大似然估计

针对双站定位涉及的跳频信号多普勒频差估计问题，提出了归一化的频差最大似然估计算法，利用两个定位站接收到的跳频脉冲串信号，构建一个关于基准跳频频率多普勒频差的似然函数，通过网格搜索得到使似然函数最大的多普勒频差估计，既解决了跳频信号在不同跳频频率上多普勒频差不一致的问题，又充分利用在不同频率的脉冲串信号提高了多普勒频差估计精度。通过仿真对算法的性能进行了评估，结果表明，与基于子空间的算法比较，在脉冲数达到240个时，所提算法执行效率提升30%以上。     

Ruin probabilities in classical risk models with gamma claims

In this paper, we provide three equivalent expressions for ruin probabilities in a Cramér–Lundberg model with gamma distributed claims. The results are solutions of integro-differential equations, derived by means of (inverse) Laplace transforms. All the three formulas have infinite series forms, two involving Mittag–Leffler functions and the third one involving moments of the claims distribution. This last result applies to any other claim size distributions that exhibits finite moments.