Tentative business confidence indicators for the Italian economy

This paper concerns the cyclical evolution of the Italian economy and, in particular, the role of confidence indicators. In the first part of the paper various confidence indicators, computed according to the European Commission (EC) methodology, are investigated. In particular, the analysis concentrates on the dynamics of some business climate indicators referred to the supply side of the economy (i.e. manufacturing, retail and construction industries). In the second part of the analysis, new confidence indicators exploring the wide informative set characterizing the ISAE business surveys are computed. The ability of these indicators in predicting the short‐term evolution of GDP, here considered as a reference series, is compared with that of the previous EC confidence indicators. Finally, in the third part, some estimates of the relationship between the chosen business confidence indicator and some driving variables are presented. Copyright © 2003 John Wiley & Sons, Ltd.     

产业结构变动对我国直辖市宏观经济的影响研究

在测度四个直辖市产业结构合理化和产业结构高级化的基础上，研究了产业结构对经济增长、物价稳定、就业充分、对外贸易均衡的影响。研究结果表明：产业结构合理化和产业结构高级化对宏观经济指标均具正向作用。其中，对前三个指标，产业结构合理化比高级化时宏观经济的影响更加显著，并表现出较好的稳定性，高级化则表现出较大的不确定性。而在对外贸易均衡指标的影响上，产业结构高级化比合理化更加显著。     

An evaluation of forecasting using leading indicators

We consider the use of indices of leading indicators in forecasting and macro-economic modelling. The procedures used to select the components and construct the indices are examined, noting that the composition of indicator systems gets altered frequently. Cointegration within the indices, and between their components and macro-economic variables are considered as well as the role of co-breaking to mitigate regime shifts. Issues of model choice and data-based restrictions are investigated. A framework is proposed for index analysis and selecting indices, and applied to the UK longer-leading indicator. The effects of adding leading indicators to macro models are considered theoretically and for UK data.     

Finding good predictors for inflation: a Bayesian model averaging approach

We consider a Bayesian model averaging approach for the purpose of forecasting Swedish consumer price index inflation using a large set of potential indicators, comprising some 80 quarterly time series covering a wide spectrum of Swedish economic activity. The paper demonstrates how to efficiently and systematically evaluate (almost) all possible models that these indicators in combination can give rise to. The results, in terms of out‐of‐sample performance, suggest that Bayesian model averaging is a useful alternative to other forecasting procedures, in particular recognizing the flexibility by which new information can be incorporated. Copyright © 2004 John Wiley & Sons, Ltd.     

In search of leading indicators of economic activity in Germany

In this paper we present two new composite leading indicators of economic activity in Germany estimated using a dynamic factor model with and without regime switching. The obtained optimal inferences of business cycle turning points indicate that the two‐state regime switching procedure leads to a successful representation of the sample data and provides an appropriate tool for forecasting business conditions. Copyright © 2003 John Wiley & Sons, Ltd.     

A modified sequential Monte Carlo procedure for the efficient recursive estimation of extreme quantiles

Many applications in science involve finding estimates of unobserved variables from observed data, by combining model predictions with observations. The sequential Monte Carlo (SMC) is a well‐established technique for estimating the distribution of unobserved variables that are conditional on current observations. While the SMC is very successful at estimating the first central moments, estimating the extreme quantiles of a distribution via the current SMC methods is computationally very expensive. The purpose of this paper is to develop a new framework using probability distortion. We use an SMC with distorted weights in order to make computationally efficient inferences about tail probabilities of future interest rates using the Cox–Ingersoll–Ross (CIR) model, as well as with an observed yield curve. We show that the proposed method yields acceptable estimates about tail quantiles at a fraction of the computational cost of the full Monte Carlo.     

Household projection methods

"The role of household projections as a basis for forecasts of households at [the] national and sub-national level is discussed and a number of criteria for such projections are outlined. The projection method used by the Department of the Environment [in the United Kingdom] is examined in the context of these criteria and it is concluded that it is both practical and robust. However, it is open to criticism, first because of its failure to make the best use of the available data and of theoretical knowledge, and secondly because of its 'black box' nature. An alternative two-stage strategy is developed. The first stage involves constructing projections using a new curve-fitting method which takes account of within cohort life-cycle headship rate changes. The second is a method of analysing the resulting projections by modelling transition rates between different household states. Worked examples of both methods are presented."     

Using social media mining technology to improve stock price forecast accuracy

Many stock investors make investment decisions based on stock-price-related chip indicators. However, in addition to quantified data, financial news often has a nonnegligible impact on stock price. Nowadays, as new reviews are posted daily on social media, there may be value in using web opinions to improve the performance of stock price prediction. To this end, we use logistic regression to screen the chip indicators and establish a basic stock price prediction model. Then, we employ text mining technology to quantify the unstructured data of social media opinions on stock-related news into sentiment scores, which are found to correlate significantly with the change extent of the stock price. Based on the findings that the higher the sentiment scores, the lower the prediction accuracy of the logistic regression model, we propose an improved prediction approach that integrates sentiment scores into the logistic regression model. Our results show that the proposed model can improve the prediction accuracy for stock prices, and can thus provide a new reference for investment strategies for stock investors.     

基于改进神经网络的厚煤层采煤方法预测模型

针对厚煤层采煤方法选择多目标非线性的问题,在影响因素分析的基础上,建立了预测仿真模型,利用神经网络改进算法训练网络,通过早停的方式解决网络过拟合问题。通过计算机仿真结合现场应用表明,该模型给出了最优方案,可为厚煤层采煤方法的合理选择和工作面主要经济技术指标的预测提供一种新的研究思路,在煤矿开采中具有广阔的应用前景。     

Hybrid Forecasting with Estimated Temporally Aggregated Linear Processes

We introduce a new strategy for the prediction of linear temporal aggregates; we call it ‘hybrid’ and study its performance using asymptotic theory. This scheme consists of carrying out model parameter estimation with data sampled at the highest available frequency and the subsequent prediction with data and models aggregated according to the forecasting horizon of interest. We develop explicit expressions that approximately quantify the mean square forecasting errors associated with the different prediction schemes and that take into account the estimation error component. These approximate estimates indicate that the hybrid forecasting scheme tends to outperform the so‐called ‘all‐aggregated’ approach and, in some instances, the ‘all‐disaggregated’ strategy that is known to be optimal when model selection and estimation errors are neglected. Unlike other related approximate formulas existing in the literature, those proposed in this paper are totally explicit and require neither assumptions on the second‐order stationarity of the sample nor Monte Carlo simulations for their evaluation. Copyright © 2014 John Wiley & Sons, Ltd.     

Bootstrap Replacement to Validate the Influence of the Economic Cycle on the Structure and the Accuracy Level of Business Failure Prediction Models

The aim of this study was to answer the question of how the economic cycle affects the stability and efficiency of business failure prediction models, using bootstrap replacement method for validation. We analyse 2228 Spanish small and medium‐sized enterprises for the period 2001–2009, and divide it into three different phases of the economic cycle (growth, crisis, recession). We find that the structure and the ability of business failure prediction models are different according to the economic cycle. These findings are relevant for the debate on the most suitable financial ratios when developing business failure prediction models and to pose their accuracy level in these prediction models. Copyright © 2015 John Wiley & Sons, Ltd.     

What does the tail of the distribution of current stock prices tell us about future economic activity?

This paper proposes three leading indicators of economic conditions estimated using current stock returns. The assumption underlying our approach is that current asset prices reflect all the available information about future states of economy. Each of the proposed indicators is related to the tail of the cross‐sectional distribution of stock returns. The results show that the leading indicators have strong correlation with future economic conditions and usually make better out‐of‐sample predictions than two traditional competitors (random walk and the average of previous observations). Furthermore, quantile regressions reveal that the leading indicators have strong connections with low future economic activity.     

基于消费者类型的耐用品租赁定价研究

基于消费者类型,从效用角度分析了新、旧耐用品对消费者的市场覆盖;讨论了耐用品垄断商在两周期上最优租赁定价策略及相关特征。发现第一期新耐用品的最优租赁定价高于第二期新耐用品的最优租赁定价,但第一期新耐用品的市场覆盖高于第二期新耐用品的市场覆盖;垄断商在第一期对新耐用品的最优租赁定价先随着耐用度的增加而增加,然后随着耐用度的增加而下降,在第二期,新耐用品的最优租赁定价不受耐用度影响。     

A re‐examination of the excess smoothness puzzle when consumers estimate the income process

The excess smoothness puzzle is explored using a simple version of the permanent income hypothesis. The new feature is that consumers do not know the observed data‐generating process for income. Instead they estimate the income process every period using the past income data and update their income forecasts as new data arrive. Two scenarios are examined: first, where the income has a linear deterministic trend and second, where the income has a constant trend. There is a misspecification bias in the estimate of the marginal propensity to consume (MPC). This bias is of second‐order importance in the first scenario while it is of first‐order importance in the second. We conclude that the second scenario, which may be relevant for less developed countries, may offer a potential solution to the excess smoothness puzzle. Copyright © 2001 John Wiley & Sons, Ltd.     

Forecasting the Treasury's balance at the Fed

As part of the Fed's daily operating procedure, the Federal Reserve Bank of New York, the Board of Governors and the Treasury make a forecast of that day's Treasury balance at the Fed. These forecasts are an integral part of the Fed's daily operating procedure. Errors in these forecasts can generate variation in reserve supply and, consequently, the federal funds rate. This paper evaluates the accuracy of these forecasts. The evidence suggests that each agency's forecast contributes to the optimal, i.e., minimum variance, forecast and that the Trading Desk of the Federal Reserve Bank of New York incorporates information from all three of the agency forecasts in conducting daily open market operations. Moreover, these forecasts encompass the forecast of an economic model. Copyright © 2004 John Wiley & Sons, Ltd.     

Nowcasting quarterly GDP growth in a monthly coincident indicator model

This paper presents an extension of the Stock and Watson coincident indicator model that allows one to include variables available at different frequencies while taking care of missing observations at any time period. The proposed procedure provides estimates of the unobserved common coincident component, of the unobserved monthly series underlying any included quarterly indicator, and of any missing values in the series. An application to a coincident indicator model for the Portuguese economy is presented. We use monthly indicators from business surveys whose results are published with a very short delay. By using the available data for the monthly indicators and for quarterly real GDP, it becomes possible to produce simultaneously a monthly composite index of coincident indicators and an estimate of the latest quarter real GDP growth well ahead of the release of the first official figures. Copyright © 2005 John Wiley & Son, Ltd.     

Second‐Generation Prediction Markets for Information Aggregation: A Comparison of Payoff Mechanisms

Initial applications of prediction markets (PMs) indicate that they provide good forecasting instruments in many settings, such as elections, the box office, or product sales. One particular characteristic of these ‘first‐generation’ (G1) PMs is that they link the payoff value of a stock's share to the outcome of an event. Recently, ‘second‐generation’ (G2) PMs have introduced alternative mechanisms to determine payoff values which allow them to be used as preference markets for determining preferences for product concepts or as idea markets for generating and evaluating new product ideas. Three different G2 payoff mechanisms appear in the existing literature, but they have never been compared. This study conceptually and empirically compares the forecasting accuracy of the three G2 payoff mechanisms and investigates their influence on participants' trading behavior. We find that G2 payoff mechanisms perform almost as well as their G1 counterpart, and trading behavior is very similar in both markets (i.e. trading prices and trading volume), except during the very last trading hours of the market. These results indicate that G2 PMs are valid instruments and support their applicability shown in previous studies for developing new product ideas or evaluating new product concepts. Copyright © 2011 John Wiley & Sons, Ltd.     

Accounting for Word‐of‐Mouth Effects in Preference‐Based Market Forecasts

Consumers differ in their involvement in new product purchase decisions. Opinion leaders usually show a higher involvement in their purchase decisions than other consumers. This leads to a higher stability in their answers when being asked about their preferences. An important question that previous research has not analyzed yet is whether and how to capture this finding in preference‐based market forecasts. The authors study these aspects for a representative sample of 364 consumers in the mobile phone market of a large European country. They find that assigning higher weights to the preferences of opinion leaders in aggregate market forecasts results in estimates that are more consistent with observed market shares than forecasts in which all consumers are given equal weights. The authors further test different measures of opinion leadership and find that sociometric indicators outperform psychographic constructs to account for the outcome of opinion leadership in preference‐based market forecasts. Copyright © 2013 John Wiley & Sons, Ltd.     

International interdependence of business cycles in the manufacturing industry: The use of leading indicators for forecasting and analysis

This article introduces new leading indicators for fifteen industrialized countries which enable the business cycle in manufacturing to be forecast fairly reliably between 4 and 6 months ahead. These indicators are based on an improved variant of the NBER method, yielding a composite leading indicator characterized by less erratic movements and clear turning points. The indicators are used to explore the international interdependence of business cycles and to examine the degree to which this interdependence is affected by growing economic integration, as in the EC. For each of the countries studied, the various foreign economies affecting the local business climate are identified. Since the business cycles of some countries clearly lead those of others, this international interdependence can be used to further improve the predictive power of the leading indicators in the lagging countries.     

Predictions in Overdispersed Series of Counts Using an Approximate Predictive Likelihood

The generalized autoregression model or GARM, originally used to model series of non-negative data measured at irregularly spaced time points (Lambert, 1996a), is considered in a count data context. It is first shown how the GARM can be expressed as a GLM in the special case of a linear model for some transform of the location parameter. The Butler approximate predictive likelihood (Butler, 1986, Rejoinder) is then used to define likelihood prediction envelopes. The width of these intervals is shown to be slightly wider than the Fisher (1959, pp. 128–33) and Lejeune and Faulkenberry (1982) predictive likelihood-based envelopes which assume that the parameters have fixed known values (equal to their maximum likelihood estimates). The method is illustrated on a small count data set showing overdispersion.© 1997 John Wiley & Sons, Ltd.