Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

Forecast of telephonometry of Xiqing District using chaos time series

The telecommunication industry has marched into telephonometry competition age. In order to forecast telephonometry development, an effective method by using the chaos time series is proposed. And the best estimate method is presented by contrasted with mature and advanced estimate methods as follows： the stochastic forest, stochastic gradient boosting, the support vector and artificial neuron network.     

“十五”期间我国沥青应用预测

介绍了沥青的生产和需求现状，并根据消费行为的情况对沥青的市场和价格走势进行预测，分析了国内沥青行业的存在问题。     

The over-optimism among experts in assessment and foresight

It is still disputed whether foresight exercises should be based on top-expert assessments or on a broader base of less specialised experts, and whether the self-rating of experts is an acceptable method. Using the German 1993 and the Austrian 1998 Technology Delphis, this study addresses both questions. Self-rating is, in fact, an appropriate method for selecting experts. But the assessment of self-rated top experts tend to suffer from an optimism bias due to the experts' involvement and their underestimation of realisation and diffusion problems. The degree of optimism is positively correlated with the degree of self-rated knowledge, and it is more pronounced for the least pioneering and for organizational innovations. Experts with top self-ratings working in business have a stronger optimism bias than those working in the academia or in the administration: Consistent with the insider hypothesis, they are most optimistic with regard to realisation, innovativeness, and potential leadership in economic exploitation. Given the optimism bias, foresight exercises should base their panels on a fair mixture of experts of different grades, with different types of knowledge and affiliation, and not only on top specialists of the respective field. Delphi-type exercises, therefore, offer an advantage relative to forum groups or small panels of specialists.     

Dynamic Panel Data Evidence on the Trade-Income Relation

This paper revisits the relationship between a country’s openness and its per capita income. Building on Frankel and Romer, it argues that a dynamic econometric specification similar to the ones used in empirical growth studies better fits the theoretical literature and also resolves some otherwise unresolved inconsistencies. The preferred econometric method is Blundell and Bond’s system-GMM estimator, which allows dealing with measurement error, weak instruments, and time-invariant country-specific effects. The findings confirm the existence of a strong effect of trade on income but fail to find evidence for trade as an independent factor of divergence. JEL no. F43, O40     

On Measuring the Value of a Nonmarket Good Using Market Data

We use line integral theory to lay out in a more general theoretical framework the conditions under which it is possible to measure with market data the welfare effects of a change in a nonmarket good. We present in detail a numerical method of measuring the value of nonmarket goods using market data, under either weak neutrality or weak complementarity. Our numerical method is more flexible than the existing analytical method because it can be used with any well-behaved Marshallian demand function, and can be used even when the willig condition does not hold.     

A Pricing and Hedging Comparison of Parametric and Nonparametric Approaches for American Index Options

This article investigates the extent to which options on theAustralian Stock Price Index can be explained by parametricand nonparametric option pricing techniques. In particular,comparisons are made of out-of-sample option pricing performanceand hedging performance. The dataset differs from many of thoseused previously in the empirical options pricing literaturein that it consists of American options. In addition, a broaderspectrum of techniques are considered: a spline-based nonparametrictechnique is considered in addition to the standard kernel techniques,while the performance of a Heston stochastic volatility modelis also considered. Although some evidence is found of superiorperformance by nonparametric techniques for in-sample pricing,the parametric methods exhibit a markedly better ability toexplain future prices and show superior hedging performance.     

More thinking on research methods for the resource‐based perspective

We re‐examine the benefits of using a broader set of research methods to address key questions associated with the resource‐based view (RBV) of the firm. In responding to Levitas and Chi, we consider how research inside organizations can complement and augment research relying on secondary data. Copyright © 2002 John Wiley & Sons, Ltd.     

The Nested PIGLOG Model: An Application to U.S. Food Demand

A new demand system is introduced, the Nested PIGLOG model, nesting thirteen other demand systems including five that are also new. This new model and its nested special cases are applied to models of U.S. food demand that include food-at-home (FAH), food-away-from-home (FAFH), and alcoholic beverages. Although nested tests and out-of-sample forecasting performance favor generalizing models to a certain degree, statistically insignificant improvements to in-sample-fit and even poorer out-of-sample forecast accuracy undermine further generalizations. Based on a subset of preferred models, FAFH is found to be price and income elastic compared to FAH which is price and income inelastic.