产业结构调整下的广州市技能人才需求预测分析

金融危机对广州的传统制造业产生了较大的冲击,调整产业结构,提升创新能力是当前摆脱危机,实现经济新一轮增长的重要途径。在此过程中,技能人才的供给是广州三次产业战略调整的直接制约因素。尤其随着广州产业结构调整节奏的逐步加快,其对技能人才的市场需求将与三次产业的产值结构相匹配而趋于刚性。因此,合理预测广州三次产业发展中对技能人才的需求,增加技能人才的供给能力,是广州产业结构调整中的重要问题。     

基于灰色理论的海西区域物流需求研究

物流产业的发展在"海西"经济区建设中发挥着重要作用。在当前物流产业统计指标体系尚未健全的情况下,本文运用灰色系统预测模型,对"海西"经济区建设的物流需求量进行预测,以此反映"海西"经济区物流需求的变化情况,为"海西"经济区物流产业的规划和制定相应物流政策提供依据。研究结果表明,GM(1,1)预测模型预测精度较高,效果较好。     

New perspectives on forecasting inflation in emerging market economies: An empirical assessment

We use a broad-range set of inflation models and pseudo out-of-sample forecasts to assess their predictive ability among 14 emerging market economies (EMEs) at different horizons (1–12 quarters ahead) with quarterly data over the period 1980Q1-2016Q4. We find, in general, that a simple arithmetic average of the current and three previous observations (the RW-AO model) consistently outperforms its standard competitors—based on the root mean squared prediction error (RMSPE) and on the accuracy in predicting the direction of change. These include conventional models based on domestic factors, existing open-economy Phillips curve-based specifications, factor-augmented models, and time-varying parameter models. Often, the RMSPE and directional accuracy gains of the RW-AO model are shown to be statistically significant. Our results are robust to forecast combinations, intercept corrections, alternative transformations of the target variable, different lag structures, and additional tests of (conditional) predictability. We argue that the RW-AO model is successful among EMEs because it is a straightforward method to downweight later data, which is a useful strategy when there are unknown structural breaks and model misspecification.     

Pair trading based on quantile forecasting of smooth transition GARCH models

Pair trading is a statistical arbitrage strategy used on similar assets with dissimilar valuations. We utilize smooth transition heteroskedastic models with a second-order logistic function to generate trading entry and exit signals and suggest two pair trading strategies: the first uses the upper and lower threshold values in the proposed model as trading entry and exit signals, while the second strategy instead takes one-step-ahead quantile forecasts obtained from the same model. We employ Bayesian Markov chain Monte Carlo sampling methods for updating the estimates and quantile forecasts. As an illustration, we conduct a simulation study and empirical analysis of the daily stock returns of 36 stocks from U.S. stock markets. We use the minimum square distance method to select ten stock pairs, choose additional five pairs consisting of two companies in the same industrial sector, and then finally consider pair trading profits for two out-of-sample periods in 2014 within a six-month time frame as well as for the entire year. The proposed strategies yield average annualized returns of at least 35.5% without a transaction cost and at least 18.4% with a transaction cost.     

The Macro Forecasting Record of the Indonesian Financial Authorities, 2004–15

A precondition for financial authorities’ macroeconomic effectiveness is a capacity to foresee economic conditions with reasonable accuracy. This article analyses the performance of Bank Indonesia (the central bank) and Indonesia’s Ministry of Finance in forecasting real GDP growth and consumer inflation over the last decade or so. It compares the quality of their forecasts with those of international institutions, aligning the timing of these forecasts to maximise comparability. Overall, the Indonesian authorities—especially the Ministry of Finance, in its revised central government budget (APBN-P)—perform respectably against their international comparators and there is no statistically significant bias in any of the forecasts. Still, there are mild indications of an optimistic bias in forecasts of inflation and growth, and all institutions tend to miss growth slowdowns. The results raise doubts about the value of fiscal policy for near-term macro stabilisation in Indonesia. Likewise, a wide range of uncertainty in forecasting inflation complicates the use of monetary policy for inflation targeting, at least one year ahead.     

Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data

The purpose of this study is to compare the predictive accuracy of various uni- and multivariate models in forecasting international city tourism demand for Paris from its five most important foreign source markets (Germany, Italy, Japan, UK and US). In order to achieve this, seven different forecast models are applied: EC-ADLM, classical and Bayesian VAR, TVP, ARMA, and ETS, as well as the naïve-1 model serving as a benchmark. The accuracy of the forecast models is evaluated in terms of the RMSE and the MAE. The results indicate that for the US and UK source markets, univariate models of ARMA(1,1) and ETS are more accurate, but that multivariate models are better predictors for the German and Italian source markets, in particular (Bayesian) VAR. For the Japanese source market, the results vary according to the forecast horizon. Overall, the naïve-1 benchmark is significantly outperformed across nearly all source markets and forecast horizons.     

经济周期波动转折点的向量自回归门限模型分析--基于12个国家（或地区）1970～2012年数据

经济周期波动转折点的识别与检验问题一直是经济周期理论所关注的重要内容。改进目前使用较为广泛的经济周期转折点CDR指标，扩展其取值范围并消除该指标外生决定的缺点。以12个国家（或地区）1970～2012年的季度数据，分别构建以CDR、修正后的CDR为门限变量的向量自回归门限模型，进行样本外预测能力比较。实证结果显示，以MCDR指标作为门限变量的模型预测能力优于CDR。     

短期负荷预测中气象因素的大数据分析

坚强智能电网的迅速发展,先进数据存储和挖掘技术为气象大数据在负荷预测的应用提供了基础,特别是在短期负荷预测方面,气象大数据的时代已经到来。在考虑气象因素对短期负荷预测的影响下,归纳典型气象因素,对气象大数据的发展及新应用进行阐述,为气象大数据在短期负荷预测中的应用研究做好前期基础工作。     

基于回归模型的西北五省区货运量预测

基于2001~2012年西北五省统计数据,选择货运量为指标,应用SAS软件,对2002~2020年间西北五省货运量和周转量规模进行了预测。研究发现,到2020年,西北五省的货运量可望达到415 321万吨。其中:陕西最高,可望达到201 530万吨;新疆次之,可望达到83 078万吨;宁夏居第三位,可望达到64 924万吨;甘肃居第四位,可望达到46 871万吨;青海居第五位,可望达到18 918万吨。     

Short-term forecasting of air passenger by using hybrid seasonal decomposition and least squares support vector regression approaches

In this study, two hybrid approaches based on seasonal decomposition and least squares support vector regression (LSSVR) model are proposed for short-term forecasting of air passenger. In the formulation of the proposed hybrid approaches, the air passenger time series is first decomposed into three components: trend-cycle component, seasonal factor and irregular component. Then the LSSVR model is used to predict the components independently and these prediction results of the components are combined as an aggregated output. Empirical analysis shows that the proposed hybrid approaches are better than other time series models, indicating that they are promising tools to predict complex time series with high volatility and irregularity.