Professional forecasters and real-time forecasting with a DSGE model

This paper analyses the real-time forecasting performance of the New Keynesian DSGE model of Galí, Smets and Wouters (2012), estimated on euro area data. It investigates the extent to which the inclusion of forecasts of inflation, GDP growth and unemployment by professional forecasters improve the forecasting performance. We consider two approaches for conditioning on such information. Under the “noise” approach, the mean professional forecasts are assumed to be noisy indicators of the rational expectations forecasts implied by the DSGE model. Under the “news” approach, it is assumed that the forecasts reveal the presence of expected future structural shocks in line with those estimated in the past. The forecasts of the DSGE model are compared with those from a Bayesian VAR model, an AR(1) model, a sample mean and a random walk.     

A new methodology for the logistic analysis of evolutionary S-shaped processes: Application to historical time series and forecasting

A new multi-logistic methodology to analyze long range time series of evolutionary S-shaped processes is presented. It conceptually innovates over the traditional logistic approach. The ansatz includes computing the residuals to an optimized multi-logistic trend curve least squares fitted to the time-series data. The elements of the residuals series are checked for autocorrelations and once detected the residuals series is further analyzed to search for eventual presence of underlying periodic structures using a truncated Fourier sine series. The method foundations ensures both a universal applicability and a capacity to disclose the existence of active clocks that can be possibly traced to the driving motors of the evolutionary character of the time series, due to the responsiveness of corresponding process to the development of economic cycles. On associating these two views, it is found that the methodology has a strong potential to improve the quality of short-term forecasts. These findings have been put to test through applications of the methodology to studying the time evolution of two commodities of strong economic and social importance (corn and steel) and good results were consistently obtained for both the analytical and forecasting aspects.     

Application of ANFIS to exchange rate forecasting

The need of exchange rate forecasting in order to preventing its disruptive movements has engrossed many policy-makers and economists for many years. The determinants of exchange rate have grown manifold making its behavior complex, nonlinear and volatile so that nonlinear models have better performance for its forecasting. In this study the accuracy of ANFIS as the nonlinear model and ARIMA as the linear models for forecasting 2, 4 and 8 days ahead of daily Iran Rial/∈ and Rial/US$ was compared. Using forecast evaluation criteria we found that nonlinear model outperforms linear model in all three horizons.     

Forecasting house prices using dynamic model averaging approach: Evidence from China

Forecasting house price has been of great interests for macroeconomists, policy makers and investors in recent years. To improve the forecasting accuracy, this paper introduces a dynamic model averaging (DMA) method to forecast the growth rate of house prices in 30 major Chinese cities. The advantage of DMA is that this method allows both the sets of predictors (forecasting models) as well as their coefficients to change over time. Both recursive and rolling forecasting modes are applied to compare the performance of DMA with other traditional forecasting models. Furthermore, a model confidence set (MCS) test is used to statistically evaluate the forecasting efficiency of different models. The empirical results reveal that DMA generally outperforms other models, such as Bayesian model averaging (BMA), information-theoretic model averaging (ITMA) and equal-weighted averaging (EW), in both recursive and rolling forecasting modes. In addition, in recent years it is found that the Google search index, instead of fundamental macroeconomic or monetary indicators, has developed greater predictive power for house price in China.     

Oil price forecasting under asymmetric loss

Based on the approach developed by Elliott et al. (2005), we found that the loss function of a sample of oil price forecasters is asymmetric in the forecast error. Our findings indicate that the loss oil price forecasters incurred when their forecasts exceeded the price of oil tended to be larger than the loss they incurred when their forecast fell short of the price of oil. Accounting for the asymmetry of the loss function does not necessarily make forecasts look rational.     

Skill transferability,regret and mobility

After graduation many students start working in sectors not related to their field of study or participate in training targeted at work in other sectors. In this article, we look at mobility immediately after graduation from the perspective that educational choices have been made when these pupils had little experience of the actual working life in these professions. We develop a model where students accumulate partially transferable human capital but also learn about their professional preferences at the university and during the first years in the labour market. As a consequence of this newly acquired insight, these young workers might realize that working in another occupational field would better fit their preferences, although they are better equipped to work in their own field. The empirical analysis reveals that if wages are 1% lower due to lower skill transferability, the probability that a graduate who regrets his choice actually switches decreases by 1.4 percentage points, while those who switch on average take 0.3 months additional education.     

论合成氨厂含氨尾气达标排放新方法

文章阐述了几种常见的含氨放空气处理方法,从实际角度出发,分析了采用氨加工系统处理含氨放空气在运行中容易出现的问题,并提出了采用填料塔来解决含氨放空气的方法。     

Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models

We extend the recently introduced latent threshold dynamic models to include dependencies among the dynamic latent factors which underlie multivariate volatility. With an ability to induce time-varying sparsity in factor loadings, these models now also allow time-varying correlations among factors, which may be exploited in order to improve volatility forecasts. We couple multi-period, out-of-sample forecasting with portfolio analysis using standard and novel benchmark neutral portfolios. Detailed studies of stock index and FX time series include: multi-period, out-of-sample forecasting, statistical model comparisons, and portfolio performance testing using raw returns, risk-adjusted returns and portfolio volatility. We find uniform improvements on all measures relative to standard dynamic factor models. This is due to the parsimony of latent threshold models and their ability to exploit between-factor correlations so as to improve the characterization and prediction of volatility. These advances will be of interest to financial analysts, investors and practitioners, as well as to modeling researchers.     

模拟环境件的定位和检测设计

针对内外饰零件的结构匹配特点以及目前零件在检具上的尺寸检测结果与实车装配效果存在差异的现象,本文通过实例简要介绍了带卡扣、卡钩、筋条的这几类零件模拟环境件进行定位设计和检测设计的方法,并提出了一种新式的、组合环境件进行定位和检测的设计方案。     

A review of Delphi forecasting research in tourism

The Delphi technique is the most popular judgemental forecasting method in tourism studies, but theoretical and empirical developments in this area (especially for forecasting purposes) have been slow. This paper analyses published research on Delphi forecasting in tourism and hospitality, explores how the Delphi forecasting method has progressed over the past four decades in terms of topical areas, empirical applications, and issues of reliability and validity, and is thus expected to advance understanding of the Delphi technique, providing topical and methodological recommendations for researchers and industry practitioners for producing accurate forecasts. The literature concerning the qualitative and quantitative applications of Delphi forecasting in tourism is mainly divided into three research themes: event forecasting, forecasting tourism demand, and forecasting future trends/market conditions (the most popular application). Issues of accuracy, reliability, and validity, as well as a group of Delphi-specific characteristics, such as panel size, panel selection, consensus measures, and analysis of results, are summarized and discussed. This study also examines the accuracy of Delphi forecasts as well as exploring the role of the Delphi approach in integrating human judgement into quantitative forecasts.