Risk forecasting in (T)GARCH models with uncorrelated dependent innovations

(G)ARCH-type models are frequently used for the dynamic modelling and forecasting of risk attached to speculative asset returns. While the symmetric and conditionally Gaussian GARCH model has been generalized in a manifold of directions, model innovations are mostly presumed to stem from an underlying IID distribution. For a cross section of 18 stock market indices, we notice that (threshold) (T)GARCH-implied model innovations are likely at odds with the commonly held IID assumption. Two complementary strategies are pursued to evaluate the conditional distributions of consecutive TGARCH innovations, a non-parametric approach and a class of standardized copula distributions. Modelling higher order dependence patterns is found to improve standard TGARCH-implied conditional value-at-risk and expected shortfall out-of-sample forecasts that rely on the notion of IID innovations.     

基于马尔可夫链的央行分支机构人力资源的短期预测

本文根据中国人民银行广州分行人力资源变量的现状及其变化趋势,应用马尔可夫链预测其在未来三年期间内某个时间点上人力资源可能出现的状态,从而为中国人民银行广州分行人力资源管理提供可靠的理论依据。     

Equilibrium-correction vs. differencing in macroeconometric forecasting

Recent work by Clements and Hendry elucidate why forecasting systems that are in terms of differences, dVARs, can be more accurate than econometric models that include levels variables, EqCMs. For example, dVAR forecasts are in some cases insulated from parameter non-constancies in the long run mean of the cointegration relationships. In this paper, the practical relevance of these issues are investigated for RIMINI, the quarterly macroeconometric model used in Norges Bank (Central Bank of Norway), an example of an EqCM forecasting model. We develop two dVAR versions of the full RIMINI model and compare EqCM and dVAR forecasts for the period 1992.1–1994.4. We also include forecasts from univariate dVAR type models. The results seem to confirm the relevance of the theoretical results. First, dVAR forecasts appear to provide some immunity against parameter non-constancies that could seriously bias the EqCM forecasts. Second, the misspecification resulting from omitting levels information generates substantial biases in the dVAR forecasts 8 and 12 quarters ahead.     

Influential factors on Chinese airlines’ profitability and forecasting methods

We establish profit models to predict the performance of airlines in the short term using the quarterly profit data collected on the three largest airlines in China together with additional recent historical data on external influencing factors. In particular, we propose the application of the LASSO estimation method to this problem and we compare its performance with a suite of other more modern state-of-the-art approaches including ridge regression, support vector regression, tree regression and neural networks. It is shown that LASSO generally outperforms the other approaches in this study. We concluded a number of findings on the oil price and other influential factors on Chinese airline profitability.     

Analysing differences between scenarios

Comparisons between alternative scenarios are used in many disciplines, from macroeconomics through epidemiology to climate science, to help with planning future responses. Differences between scenario paths are often interpreted as signifying likely differences between outcomes that would materialise in reality. However, even when using correctly specified statistical models of the in-sample data generation process, additional conditions are needed to sustain inferences about differences between scenario paths. We consider two questions in scenario analyses: First, does testing the difference between scenarios yield additional insight beyond simple tests conducted on the model estimated in-sample? Second, when does the estimated scenario difference yield unbiased estimates of the true difference in outcomes? Answering the first question, we show that the calculation of uncertainties around scenario differences raises difficult issues, since the underlying in-sample distributions are identical for both ‘potential’ outcomes when the reported paths are deterministic functions. Under these circumstances, a scenario comparison adds little beyond testing for the significance of the perturbed variable in the estimated model. Resolving the second question, when models include multiple covariates, inferences about scenario differences depend on the relationships between the conditioning variables, especially their invariance to the interventions being implemented. Tests for invariance based on the automatic detection of structural breaks can help identify the in-sample invariance of models to evaluate likely constancy in projected scenarios. Applications of scenario analyses to impacts on the UK’s wage share from unemployment and agricultural growth from climate change illustrate the concepts.     

Manufacturing outsourcing,onshoring, and global equilibrium

Manufacturing is now a national strategy for many countries to combat slow economic growth, and positively viewed with the current trend of onshoring foreign manufacturing operations. We develop a cross-country regression model that predicts manufacturing employment as a function of population growth, foreign direct investment, and purchasing power parity. Results through the year 2100 suggest that manufacturing is trending toward a global equilibrium with higher levels of manufacturing outputs but much lower levels of manufacturing employment. The reason is that countries tend to evolve from having little manufacturing to commodity manufacturing at large scale and low wages. As infrastructure and human capital develop, there is the tendency to pursue advanced manufacturing in support of higher valued goods. The manufacture of commodity products is then outsourced to those countries with lower costs justified by their less-developed infrastructure and human capital, and so the virtuous cycle continues. While this model suggests that current efforts in revitalization of domestic manufacturing would lead to an increase in wealth in the United States, the bad news is that these gains are unlikely to be sustainable in the long term. However, the good news is that manufacturing acts as a rising tide that raises all nations and our global quality of life.     

时序分析在地下洞室变形监测数据处理中的应用

时序分析法是广泛应用于多领域的统计分析方法,在岩土工程数值分析当中也有广泛应用,SAS/ETS(Ti me Series Forecasting)时序分析系统是一种智能化时序分析工具,本文运用SAS数据挖掘工具中时序分析组件对某水电站地下引水发电系统原位监控位移时间序列进行时序预测分析。实践表明,SAS/ETS时序分析方法基本能反映出蕴涵在位移序列中的岩体变形规律,预测结果与实测结果一致,可以运用于工程实践。