一个Sales的诞生

很久很久以前的一天,牧神潘恩正在尼罗河畔为奥林匹斯众神吹奏美妙的仙乐,却把一个鼻口喷火、拥有上百个龙头的恶魔怪物Typhon引了来,众神一看不妙,纷纷化成动物的形态逃走。这时,爱与美的女神维纳斯和她的儿子恋爱之神丘比特正在河边散步,眼见Typhon就要向她们冲过去。维纳斯怕丘比特被河水冲走,于是用丝带系住丘比特的脚,另一端则绑在自己的身上,同时化身成两条鱼相继跳入水中,于是她们就变成了双鱼星座……漆黑的天花板上,美丽的双鱼星座还在闪闪烁烁,身边的小女儿已在爸爸制作的这个美丽星空下甜甜入睡。“孩子对夜晚漆黑的房间是相当…     

Forecasting Australia’s economic performance during the Asian crisis

During the Asian economic crisis of 1997–98, published forecasts from a Bayesian vector autoregressive (BVAR) model consistently indicated that the crisis would have little or no effect on Australia’s economic performance, despite the deterioration in the trade balance. The worsening trade deficit led many other forecasters to predict a sharp fall in Australia’s GDP growth rate, as the countries most severely affected by the crisis represent over 60 percent of Australia’s export markets. This paper argues that the more pessimistic forecasts attached too much weight to the links between Australia’s external accounts and GDP growth. In particular, I show that forecasts for the period September 1997 to December 1998, conditional on the actual path of the merchandise trade balance, predict higher inflation and interest rates than unconditional forecasts from a model without the trade balance. There does, however, appear to be useful information in the individual components of the trade deficit. Conditioning on the actual paths of both exports and imports generally produces more accurate forecasts than conditioning on net exports. In particular, conditioning on the trade balance results in the least accurate forecasts for inflation and interest rates of any of the models considered here. On the other hand, conditioning on the individual trade flows produces the most accurate forecasts for inflation, and the second-most accurate for interest rates. Taken together, the results presented here lend support to the argument that Australia’s trade flows represent the outcomes of optimizing decisions, rather than defining constraints on economic growth.     

Forecasting Bitcoin with technical analysis: A not-so-random forest?

This paper uses data sampled at hourly and daily frequencies to predict Bitcoin returns. We consider various advanced non-linear models based on a multitude of popular technical indicators that represent market trend, momentum, volume, and sentiment. We run a robust empirical exercise to observe the impact of forecast horizon, model type, time period, and the choice of inputs (predictors) on the forecast performance of the competing models. We find that Bitcoin prices are weakly efficient at the hourly frequency. In contrast, technical analysis combined with non-linear forecasting models becomes statistically significantly dominant relative to the random walk model on a daily horizon. Our comparative analysis identifies the random forest model as the most accurate at predicting Bitcoin. The estimated measures of the relative importance of predictors reveal that the nature of investing in the Bitcoin market evolved from trend-following to excessive momentum and sentiment in the most recent time period.     

Forecasting by factors,by variables,by both or neither?

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation. A forecast-error taxonomy for factor models highlights the impacts of location shifts on forecast-error biases. Forecasting US GDP over 1-, 4- and 8-step horizons using the dataset from Stock and Watson (2009) updated to 2011:2 shows factor models are more useful for nowcasting or short-term forecasting, but their relative performance declines as the forecast horizon increases. Forecasts for GDP levels highlight the need for robust strategies, such as intercept corrections or differencing, when location shifts occur as in the recent financial crisis.     

Forecasting from others’ experience: Bayesian estimation of the generalized Bass model

We propose a Bayesian estimation procedure for the generalized Bass model that is used in product diffusion models. Our method forecasts product sales early based on previous similar markets; that is, we obtain pre-launch forecasts by analogy. We compare our forecasting proposal to traditional estimation approaches, and alternative new product diffusion specifications. We perform several simulation exercises, and use our method to forecast the sales of room air conditioners, BlackBerry handheld devices, and compressed natural gas. The results show that our Bayesian proposal provides better predictive performances than competing alternatives when little or no historical data are available, which is when sales projections are the most useful.     

Forecasting exchange rate volatility using high-frequency data: Is the euro different?

We assess the performances of alternative procedures for forecasting the daily volatility of the euro’s bilateral exchange rates using 15 min data. We use realized volatility and traditional time series volatility models. Our results indicate that using high-frequency data and considering their long memory dimension enhances the performance of volatility forecasts significantly. We find that the intraday FIGARCH model and the ARFIMA model outperform other traditional models for all exchange rate series.     

Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009

In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem into a linear model selection and estimation problem. To this end, we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.     

Forecasting consumer credit card adoption: what can we learn about the utility function?

How to accurately predict customers’ adoption behavior is becoming more important and challenging to many credit card marketers as competition increases. This calls for more knowledge about the consumer utility function and the corresponding decision behavior. In this study, we challenge the commonly used logit model which implies linear utility function and constant marginal rate of substitution (MRS) with a neural network model that can accommodate nonlinear utility function and changing MRS between card attributes. Using the data from a national survey of credit card usage, we find that the neural network model significantly outperforms the logit in predicting consumer card adoption decisions. Our results indicate that consumers do not make linear tradeoffs between card attributes and the MRS between card features does not remain constant even within the same demographic group.     

What Makes a Blockbuster Video Game? An Empirical Analysis of US Sales Data

This study uses a unique data set of individual video game titles to estimate the effect of an exhaustive set of observable characteristics on the likelihood of a video game becoming a blockbuster title. Due to the long‐tailed distribution of the sales data, both ordinary least squares and logistic regression models are estimated. The results consistently show that blockbuster video games are more likely to be released by one of the major publishers for popular hardware platforms. Results also show that games of higher quality are significantly more likely to sell a greater number of units than those of a lower quality. Copyright © 2013 John Wiley & Sons, Ltd.     

知识.知识经济.知识产业

一、前言 今年是马克思、恩格斯的划时代著作《共产党宣言》发表150周年,我们用讨论知识、知识经济、知识产业的方式来纪念这个伟大的日子。自从这本著作发表以后,共产主义运动就以排山倒海之势,雷霆万钧之力,磅礴于全世界。下面我们将指出,高级知识社会就是共产主义社会。     

市场.需求.品牌

一、关于市场 市场的概念,在今天应该是深入人心的.语文意义上的市场常常挂在我们的嘴边:"这里没有你的市场",在这里,市场是作为场所讲的;政治经济学意义的市场,也已编入中学课本,中学生都能理解:市场是商品交换的场所,是商品交换关系的总和.但营销学意义的市场概念,却远远的超出了上述两个方面的界定,现代市场营销学把市场定义为:市场是某种商品所有实际的和潜在的购买者的需求总和,是由人口、购买力和购买动机组成的.     

计量.质量.效益

海尔集团创立于1985年,国家一级企业,是中国家电行业名列前茅的大型企业集团。主导产品有家用电冰箱、空调器、电冰柜、微波炉、热水器等。 海尔集团现有电冰箱股份有限公司、冷柜总公司、空调器总公司、微波电器厂、冷凝器厂、三菱重工海尔(青岛)空调机有限公司和洁具、厨具有限公司等实业单位。为保证集团内各单位的计量工作同步发展,公司于1993年成立直属总裁领导的计量检测委员