Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory

This paper employs a multi-equation model approach to consider three statistic problems (heteroskedasticity, endogeneity and persistency), which are sources of bias and inefficiency in the predictive regression models. This paper applied the residual income valuation model (RIM) proposed by Ohlson (1995) to forecast stock prices for Taiwan three sectors. We compare relative forecasting accuracy of vector error correction model (VECM) with the vector autoregressive model (VAR) as well as OLS and RW models used in the prior studies. We conduct out-of-sample forecasting and employ two instruments to assess forecasting performance. Our empirical results suggest that the VECM statistically outperforms other three models in forecasting stock prices. When forecasting horizons extend longer, VECM produces smaller forecasting errors and performs substantially better than VAR, suggesting that the ability of VECM to improve VAR forecast accuracy is stronger with longer horizons. These findings imply that an error correction term (ECT) of the VECM contributes to improving forecast accuracy of stock prices. Our economic significance analyses and robustness tests for different data frequency are in favor of the superiority of VECM estimator.     

Forecasting stock returns: Do less powerful predictors help?

This paper proposes a simple but efficient way to improve the predictability of stock returns. Instead of torturously constructing new powerful predictors, we readily select existing predictors that have low correlations and thus provide complementary information. Our forecasting strategy is to use the selected predictors based on a multivariate regression model. In our forecasting strategy, less powerful predictors are also useful for forecasting stock returns if they could provide complementary information. The empirical results show that our forecasting strategy outperforms not only the univariate regression models that use each predictor's information separately but also combination approaches that use all predictors jointly. We also document that our strategy extracts significantly more useful information from the complementary predictors than the competing models. In addition, from an asset allocation perspective, a mean-variance investor realizes substantial economic gains. Furthermore, the evidence based on Monte Carlo simulations supports the feasibility of our forecasting strategy.     

Uncertainty and crude oil market volatility: new evidence

ABSTRACT

The main goal of this paper is to investigate the predictability of five economic uncertainty indices for oil price volatility in a changing world. We employ the standard predictive regression framework, several model combination approaches, as well as two prevailing model shrinkage methods to evaluate the performances of the uncertainty indices. The empirical results based on simple autoregression models including only one index suggest that global economic policy uncertainty (GEPU) and US equity market volatility (EMV) indices have significant predictive power for crude oil market volatility. In addition, the model combination approaches adopted in this paper can improve slightly the performances of individual autoregressive models. Lastly, the two model shrinkage methods, namely Elastin net and Lasso, outperform other individual AR-type model and combination models in most forecasting cases. Other empirical results based on alternative forecasting methods, estimation window sizes, high/low volatility and economic expansion/recession time periods further make sure the robustness of our major conclusions. The findings in this paper also have several important economic implications for oil investors.     

经济增长的技术效率及其影响因素研究——以四川省为例

运用2000—2010年数据,基于对数型柯布—道格拉斯生产函数的随机前沿模型,对四川省区域经济增长效率及其影响因素进行研究。结果表明,四川省经济增长主要靠劳动力驱动,平均技术效率水平较低,经济处于规模报酬递减状态,且其区域差异有扩大的趋势。而基础设施建设和科研投入对经济增长效率具有促进作用,金融机构的贷款业务和政府财政支出对效率的提高具有抑制作用。     

Time-series model forecasts and structural breaks: evidence from Spanish pre-EMU interest rates

Analysis of the future behaviour of economic variables can be biased if structural breaks are not considered. When these structural breaks are present, the in-sample fit of a model gives us a poor guide to ex ante forecast performance. This problem is true for both univariate and multivariate analysis and can be extremely important when co-integration relationships are analysed. The main goal of this article is to analyse the impact of structural breaks on forecast accuracy evaluation. We focus on forecasting several interest rates from the Spanish interbank money market. In order to carry out the analysis, we perform two forecasting exercises: (a) without structural breaks and (b) when structural breaks are explicitly considered. We use new sequential methods in order to estimate change-points in an endogenous way. This method allows us to detect structural breaks in all four rates in May 1993. However, the effects of these breaks are not very strong, since we found scarce gains in forecasting accuracy when the structural breaks are included in the models.     

农村金融发展与经济增长关系的实证研究——以四川为例

以农村金融与经济增长相关理论和Pagano模型为基础,运用计量经济方法对四川1989～2008年农村金融发展与经济增长之间的关系进行了实证分析。研究表明:四川农村金融相关率、资本边际产出率与农村经济增长存在显著的正相关关系,而储蓄率与农村经济增长存在显著的负相关关系。最后结合研究结论提出了一系列符合四川实际情况的农村金融改革的相关政策和建议。     

Forecasting the oil futures price volatility: A new approach

This study provides a new perspective of modelling and forecasting realized range-based volatility (RRV) for crude oil futures. We are the first to improve the Heterogeneous Autoregressive model of Realized Range-based Volatility (HAR-RRV) model by considering the significant jump components, signed returns and volatility of realized range-based volatility. The empirical results show that the volatility of volatility significantly exists in the oil futures market. Moreover, our new proposed models with significant jump components, signed returns and volatility of volatility can gain higher forecast accuracy than HAR-RRV-type models. The results are robust to different forecasting windows and forecasting horizons. Our new findings are strategically important for investors making better decisions.     

地级行政区划调整对区域经济发展的影响--以四川省为例

行政区划调整对区域经济发展有一定的影响,文章以四川地级行政区划调整为例分析这种影响.从1993年到1998年,先后对原南充地区、达县地区、乐山市和内江市进行了拆分,在保留原有四个地级行政区建制的同时,新设了广安地区、巴中地区、眉山地区和资阳地区.通过实证分析,发现自区域拆分后,新设地区和保留地区经济均取得了高于周边同类地区的发展速度,而这种拆分对新设地区的中心城市发展有更大的推动.     

Forecasting and analyzing the competitive diffusion of mobile cellular broadband and fixed broadband in Taiwan with limited historical data

Taiwan experienced the rapid growth of mobile cellular broadband from 2005 by introducing 3G operations and had higher penetration than the average of the developing countries, the world, and even the developed countries. There are many forecasting models which were developed and successfully predicted the diffusion of long lifecycle product, but there are very few forecasting models which were developed for predicting new products with short lifecycle. Assumption of these models is always the growth of products follows an S-shaped curve. As for the products which were just introduced to the market, it is very difficult to identify if they follow an S-shaped curve with their limited historical data. This research aims to apply Grey system theory to predict the diffusion of mobile cellular broadband and fixed broadband in Taiwan since Grey system theory has a characteristic which requires very limited primitive data (the least 4 data) to build a differential forecasting model. We use penetration as an indicator to describe the diffusion of new products. The numerical data show that the Grey forecasting models GM(1,1) built in this paper have higher prediction accuracy than logistic models and grey Verhulst models. Moreover, we apply Lotka–Volterra model to analyze the competitive relationship between mobile cellular broadband and fixed broadband. The empirical data show that the relationship is commensalism rather than predator–prey. These results can be extended to contribute to other researches.     

坚持科学发展观振兴四川老工业基地

振兴和发展老工业基地,是我们目前亟待解决的重大战略难题。产生并发展于传统发展现指导下的四川老工业基她很难适应市场经济的发展,暴露出一系列深层次问题,阻碍着四川经济的发展。要实现四川省老工业基地的振兴,必须树立和落实科学发展现,走出一条以人为本,全面、协调、可持续发展的振兴之路。     

Semiconductor industry cycles: Explanatory factors and forecasting

This paper aims to suggest the best forecasting model for the semiconductor market. A wide range of alternative modern econometric modeling approaches have been implemented, and a large variety of criteria and tests have been employed to assess the out-of-sample forecasting accuracy at various horizons. The results suggest that if a VECM can be an interesting source of information, the Bayesian models are superior forecasting tools compared to univariate and unrestricted VAR models. However, for decision makers a spectral method could be a useful tool, which can be easily implemented. In addition, MS-AR models make it possible to obtain valuable forecasts on turning-points in order to adjust the programming of heavy capital and research investments.     

Synthesis of qualitative and quantitative approaches to long-range forecasting

Integration of qualitative and quantitative forecasting approaches is usually attempted and appraised in the context of improving forecast accuracy. This paper examines the feasibility and potential merits of integrating these two approaches to produce a useful range, rather than to improve the accuracy, of long-range forecasting. Two approaches of synthesis, one beginning with qualitative future scenarios and the other beginning with quantitative econometric models, are described, illustrated, and compared.     

Kuznets’ swings and intangible investments in forecast: the case of Greece

This article offers an analysis of the relationship between intangible investments and the pattern of local economic productivity in Greece. There are two main objectives in the article: (i) to explore the pattern of economic development in the country; (ii) to find evidence whether this pattern – and its trend – can be better predicted through a forecasting model including intangible investments (next to other relevant factors). To operationalize our study, we use the World Bank Development Indicators database which offers a time-series for Greece for the period from 1981 till date. This data set has two alternative relevant measures for intangible investments: knowledge- and health-related investments. In our analysis we employ first, a centred moving average with a stochastic estimation of the trend, and second a double exponential smoothing, as two alternative (‘deductive’ and next ‘inductive’) approaches to identifying a trend in our data for Greece. We find evidence for a Kuznets swing type of cyclical pattern for Greece – confirmed by triangulation. Most significantly, we also find a relationship between local economic development and intangible investments. These results prompt an evidence-based query about underlying Myrdalian and Tieboutian foundations of wave theory for understanding local economic crises.     

Alternative econometric approaches for studying the link between economic systems and economic outcomes

Econometric studies on the link between economic systems and economic outcomes have relied on two approaches. In the forecasting approach an estimated relationship for Western countries is used to generate hypothetical outcomes for Eastern countries, which are then compared to actual outcomes. In the dummy-variable approach a single relationship is estimated for Eastern and Western countries, using dummy variables to test for differences in Eastern and Western performance. The forecasting approach is shown as flawed; a more complete version of the dummy-variable approach is shown as far superior. A study of energy consumption in some Eastern and Western countries illustrates these approaches.     

Nowcasting the French index of industrial production: A comparison from bridge and factor models

Governments and central banks need to have an accurate and timely assessment of indicators for the current month, as this is essential for providing a reliable and early analysis of the current economic situation. The index of industrial production (IIP) is probably the most important and widely analyzed monthly indicator, given the relevance of the manufacturing activity as a driver of the whole business cycle. This paper presents a series of models conceived to forecast the current French monthly IIP, based on regression models and dynamic factor models. The combination of these two approaches allows selecting economically relevant explanatory variables among a large data set. In addition, a rolling forecast study is carried out to assess the forecasting performance of the estimated models, using predictive ability and model confidence set tests. This latter allows getting several models displaying equivalent forecasting performance and therefore gives robustness to the forecasting exercise rather than to base the forecasting analysis only on one model.     

Economic forecast quality: information timeliness and data vintage effects

This paper investigates the impact of the timeliness of information releases and data vintage variation on economic forecast quality. Specifically, using a set of 63 key US economic series, we provide a concise measure of the forecast accuracy associated with use of economic activity indices with different publication lags. A forecasting model based on an economic activity index that is subject to a short publication lag (viz. the Aruoba-Diebold-Scotti index) is more efficient than competing models. Moreover, if this publication lag advantage is removed (by artificially imposing a publication lag restriction comparable to that of a competing indicator) this efficiency largely disappears. The final part of the analysis employs a novel (simulation-based) method of assessing the impact of data vintage variation on forecast accuracy, and finds that the results are somewhat sensitive to such variation.     

经济预测模型在碳减排压力中的应用探讨

文章论述低碳经济与碳排放的内涵及意义,阐明影响碳排放的主要因素,进而以徐州市为例,在分析全市碳排放主要影响因素的基础上,着重利用时间序列曲线趋势外推和灰色关联分析两个经济预测模型探讨徐州市碳减排压力.预测结果表明:两个经济模型预测结论基本相符.从长期来看,至2020年,徐州市碳减排压力较小,但由于工业结构偏重、工业能耗弹性系数波动性大、能源结构单一等原因,徐州市面临短期及中期碳排放压力.     

组合预测模型在区域物流需求预测中的应用

针对单一预测方法用于区域物流需求量预测存在的不足,文章提出了基于预测有效度的组合预测模型,即通过组合多个单一模型的预测结果,发挥各自的优点,提高预测的精确度。以广东省江门市为例,分别采用线性回归模型、灰色GM(1,1)模型和组合预测模型对其物流需求量进行了预测,实证结果表明区域物流需求组合预测模型能够取得更高的预测精度。     

Predicting the unpredictable: The impact of meterological satellites on weather forecasting

The practice of weather forecasting was revolutionized with the launching of the first meterological satellite in April, 1960. This paper examines the impact of this new form of weather observation on the accuracy of weather forecasts. In studying this question, the author also discusses the influence of numerical weather prediction on forecasting accuracy. Estimates of the economic saving associated with improved weather forecasts are provided. The author concludes that weather satellites have greatly improved the accuracy of storm warning forecasts. It is unlikely, however, that the contribution of technological advances, such as meteorological satellites and numerical weather prediction, will be fully exploited, until systems for disseminating and using weather information are substantially improved.     

四川省经济增长质量分析:2000—2011

在建立经济增长质量评价体系的基础上,本文对新世纪以来四川省经济增长质量的变动情况做出测度,通过对四川省经济增长质量基本状态的分析与描述,找出影响四川省经济增长质量改进的因素,总结四川经济发展方式转型道路上的优势与障碍,机遇与挑战,为制定相关战略与发展政策提出建议。