Forecasting China's GDP growth using dynamic factors and mixed-frequency data

Forecasting GDP growth is important and necessary for Chinese government to set GDP growth target. To fully and efficiently utilize macroeconomic and financial information, this paper attempts to forecast China's GDP growth using dynamic predictors and mixed-frequency data. The dynamic factor model is first applied to select dynamic predictors among large amount of monthly macroeconomic and daily financial data and then the mixed data sampling regression is applied to forecast quarterly GDP growth based on the selected monthly and daily predictors. Empirical results show that forecasts using dynamic predictors and mixed-frequency data have better accuracy comparing to traditional forecasting methods. Moreover, forecasts with leads and forecast combination can further improve forecast performance.     

Impacts of seasonal patterns of climate on recurrent fluctuations in tourism demand: Evidence from Aruba

This study estimates the effect of seasonal patterns of pull and push climate elements (rainfall, temperature, wind, and cloud coverage) on recurrent fluctuations in tourism demand from the United States (USA) and Venezuela to Aruba. The seasonal patterns were first isolated from the series using the Census X-12 decomposition method, after which the analysis included panel data unit root testing, panel data regression, and Euclidean distance calculation. The results show that both pull and push seasonal factors of climate were relevant in determining the seasonal variations in tourism demand from both countries. The study derives two theoretical propositions: (1) climate is a significant push and pull factor affecting tourism demand; and (2) tourism demand and climate are bounded by intertemporal climate constraints.     

Value for money in energy efficiency retrofits in Ireland: grant provider and grant recipients

The Sustainable Energy Authority of Ireland (SEAI) administers the Better Energy Homes scheme to provide a financial incentive for home owners to engage in energy efficiency retrofits. This study analyses data from the scheme and Building Energy Rating data for participants to the scheme to examine the value for money achieved by households. In addition, this research identifies which retrofit combinations provide greatest value for money, in terms of energy efficiency gains, for the grant provider. We utilize an error-in-variables approach to model the variation in benefits accruing to households of varying characteristics. We find that household and grant provider surplus can be maximized in the short term by retrofitting less energy efficient and larger homes, timber or steel frame homes and houses rather and apartments. The types of retrofits leading to the greatest surplus for both household and grant provider include cavity wall insulation paired with either a boiler with heating controls or heating controls only retrofit.     

Has market concentration fostered on-time performance? A case study of seventy-two U.S. airports

The study compares a multivariate with a quantile regression model to determine whether utilized airport capacity, departure and airborne delays, departure and arrival demand, and market structure explained variations in on-time gate arrivals and arrival delays. In both models, airport departure delays, arrival and departure demand explained variations in the two response variables in prioritized and non-prioritized metroplexes, holding other variables constant. After 2008, the consolidation of the airline industry through mergers coincided with the implementation of NextGen programs, which may have contributed to improvements in on-time performance, especially at prioritized metroplexes where airspace was redesigned.     

Financial stress,sovereign debt and economic activity in industrialized countries: Evidence from dynamic threshold regressions

We analyze how the impact of a change in the sovereign debt-to-GDP ratio on economic growth depends on the level of debt, the stress level on the financial market and the membership in a monetary union. A dynamic growth model is put forward demonstrating that debt affects macroeconomic activity in a non-linear manner due to amplifications from the financial sector. Employing dynamic country-specific and dynamic panel threshold regression methods, we study the non-linear relation between the growth rate and the debt-to-GDP ratio using quarterly data for sixteen industrialized countries for the period 1981Q1-2013Q2. We find that the debt-to-GDP ratio has impaired economic growth primarily during times of high financial stress and only for countries of the European Monetary Union and not for the stand-alone countries in our sample. A high debt-to-GDP ratio by itself does not seem to necessarily negatively affect growth if financial markets are calm.     

中国政府科技投入变化态势及与经济增长的关系

全面剖析了1953—2014年中国政府科技投入的规模和强度的变化态势,系统分析了政府科技投入与经济增长的弹性系数的变化情况。通过回归分析和协整检验,分析了1953—2014年中国政府的科技投入与经济增长的相关性。结果表明:1953—2014年中国政府科技投入的规模和强度不断加大,政府科技投入对经济增长的平均贡献率达到13.2%;政府科技投入与GDP增长之间存在较强的正相关关系和长期动态均衡关系。指出:为实施创新驱动发展战略、实现建设创新型国家的宏伟目标,应稳定增加政府科技投入、优化科技投入结构、完善和创新科技投入管理体制机制。     

Modelling tourism flows through gravity models: a quantile regression approach

Gravity models are widely used to study tourism flows. The peculiarities of the segmented international demand for agritourism in Italy are examined by means of a novel approach: a panel data quantile regression. We characterize the international demand for Italian agritourism with a large data set, by considering data of 33 countries of origin, from 1998 to 2010. Distance and income are the major determinants, but we also found that mutual agreements and high urbanization rates in countries of origin are associated with larger flows of incoming tourists.     

A quantile regression analysis of tourism market growth effect on the hotel industry

This study used the quantile regression method to investigate how inbound tourism market growth proxied by the growth rate of total foreign tourist arrivals (GTA) affects the growth rate of sales (GS) and financial performance of hotel firms in Taiwan. The ordinary least squares estimation results of panel regression test revealed that GTA significantly affects GS, but has no significant effect on financial performance (proxied by hotel equity return). However, quantile regression tests revealed new and interesting results. GTA has a significant effect on GS at the different quantiles of GS. In comparison, although hotel equity return was not significantly related to GTA at the median and high quantiles, the effect of GTA on hotel equity return was statistically significant at the low quantiles. These results suggest that the effect of GTA on hotel equity return is asymmetric and state-dependent, conditional on the distributions of hotel equity return. The study further identified that GTA has a significant influence only on equity returns of hotels with a small size.     

Merger and acquisitions in South African banking: A network DEA model

Banking in South Africa is known for its small number of companies that operate as an oligopoly. This paper presents a strategic fit assessment of mergers and acquisitions (M&A) in South African banks. A network DEA (Data Envelopment Analysis) approach is adopted to compute the impact of contextual variables on several types of efficiency scores of the resulting virtual merged banks: global (merger), technical (learning), harmony (scope), and scale (size) efficiencies. The impact of contextual variables related to the origin of the bank and its type is tested by means of a set of several robust regressions to handle dependent variables bounded in 0 and 1: Tobit, Simplex, and Beta. The results reveal that bank type and origin impact virtual efficiency levels. However, the findings also show that harmony and scale effects are negligible due to the oligopolistic structure of banking in South Africa.     

Assessing impact of regulatory risk on shareholders’ wealth

Research indicates that regulatory risk increases required return on investment by investors and causes underinvestment in industries with high sunk costs. The effects of regulatory changes may be measured by estimating the abnormal returns associated with the event. The results may suggest to regulators what should be encouraged or avoided. This article utilizes a fixed effects regression to examine abnormal returns from changes in Philippine nationalization regulations. The results are consistent with extant literature. Supreme Court decisions, which increased uncertainty and regulatory risk, produced negative abnormal returns. The initial release of draft implementing rules did not produce statistically significant effects, but a succeeding draft favouring liberalization, produced positive abnormal returns.