On the Performance of Naïve, Analyst and Composite Earnings Forecasts: Evidence from Hong Kong

In this paper, we compare the information content and performance of naïve, analyst and composite forecasts in Hong Kong. Empirical evidence shows that superior performance can be obtained by a composite measure combining both analyst and naïve forecasts. In addition, analyst forecasts become more conditionally efficient over the naïve model as the actual announcement approaches. The superiority and timing advantage of analyst forecasts suggest that more emphasis should be placed on the services of analysts for predicting future earnings figures, particularly when the announcement is approaching.     

Feature weighted naïve Bayes algorithm for information retrieval of enterprise systems

Automated information retrieval is critical for enterprise information systems to acquire knowledge from the vast amount of data sets. One challenge in information retrieval is text classification. Current practices rely heavily on the classical naïve Bayes algorithm due to its simplicity and robustness. However, results from this algorithm are not always satisfactory. In this article, the limitations of the naïve Bayes algorithm are discussed, and it is found that the assumption on the independence of terms is the main reason for an unsatisfactory classification in many real-world applications. To overcome the limitations, the dependent factors are considered by integrating a term frequency–inverse document frequency (TF-IDF) weighting algorithm in the naïve Bayes classification. Moreover, the TF-IDF algorithm itself is improved so that both frequencies and distribution information are taken into consideration. To illustrate the effectiveness of the proposed method, two simulation experiments were conducted, and the comparisons with other classification methods have shown that the proposed method has outperformed other existing algorithms in terms of precision and index recall rate.     

Bait and ditch: Consumer naïveté and salesforce incentives

We analyze a model of price competition between a transparent retailer and a deceptive one in a market where a fraction of consumers is naïve. The transparent retailer is an independent shop managed by its owner. The deceptive retailer belongs to a chain and is operated by a manager. The two retailers sell an identical base product, but the deceptive one also offers an add‐on. Rational consumers never consider buying the add‐on while naïve ones can be “talked” into buying it. By offering the manager a contract that pushes him to never sell the base good without the add‐on, the chain can induce an equilibrium in which both retailers obtain more‐than‐competitive profits. The equilibrium features price dispersion and market segmentation, with the deceptive retailer targeting only naïve consumers whereas the transparent retailer serves only rational ones.     

The usefulness of oil price forecasts—Evidence from survey predictions

This paper evaluates survey forecasts for crude oil prices and discusses the implications for decision makers. A novel disaggregated data set incorporating individual forecasts for Brent and Western Texas Intermediate is used. We carry out tests for unbiasedness, sign accuracy, and forecast encompassing, followed by the computation of coefficients for topically oriented trend adjustments and the Theil's U measure. We also control for the forecast horizon finding heterogeneous results. Forecasts are more precise for shorter horizons, but less accurate than the naïve prediction. For longer horizons, topically oriented trend adjustments become more pronounced, but forecasters tend to outperform the naïve predictions.     

Maximum likelihood estimation of a binomial proportion using one‐sample misclassified binary data

In this article, we construct two likelihood‐based confidence intervals (CIs) for a binomial proportion parameter using a double‐sampling scheme with misclassified binary data. We utilize an easy‐to‐implement closed‐form algorithm to obtain maximum likelihood estimators of the model parameters by maximizing the full‐likelihood function. The two CIs are a naïve Wald interval and a modified Wald interval. Using simulations, we assess and compare the coverage probabilities and average widths of our two CIs. Finally, we conclude that the modified Wald interval, unlike the naïve Wald interval, produces close‐to‐nominal CIs under various simulations and, thus, is preferred in practice. Utilizing the expressions derived, we also illustrate our two CIs for a binomial proportion parameter using real‐data example.     

MILTON FRIEDMAN ON THE INEFFECTIVENESS OF FISCAL POLICY

This article examines the evolution of the views of Milton Friedman on the (lack of) effectiveness of fiscal policy as compared with monetary policy. Though his views changed, it would not be accurate to say that he began as a naïve Keynesian. The empirical evidence bears out Friedman's later views.     

Time Series Based Errors and Empirical Errors in Fertility Forecasts in the Nordic Countries*

We use ARCH time series models to derive model based prediction intervals for the Total Fertility Rate (TFR) in Norway, Sweden, Finland, and Denmark up to 2050. For the short term (5–10 yrs), expected TFR‐errors are compared with empirical forecast errors observed in historical population forecasts prepared by the statistical agencies in these countries since 1969. Medium‐term and long‐term (up to 50 years) errors are compared with error patterns based on so‐called naïve forecasts, i.e. forecasts that assume that recently observed TFR‐levels also apply for the future.     

Technology in the time of corona: A critical institutional reading

Drawing on institutional theory and using examples from Israel, we offer a critique of technology's deployment in responses to the coronavirus disease (COVID-19). We distinguish between technologies-in-use (“small ‘t' technologies”), the bundle of artifacts and practices that bring them into being, and “Big ‘T' Technology,” the latter being technology as an institution – shared meanings, structures, and practices that govern thought and action. Using the conceptual tool kit of institutional theory, we make three interrelated arguments. First, the deployment of technologies-in-use in response to the pandemic is embedded in diverse and contradictory institutions, the institution of technology among them. These technologies participate in the very construction of crisis, which fosters the revert to known and established ways of being and doing. Thus, technologies-in-use are not necessarily the most efficient and rational but rather the most legitimate and readily available. Second, putting certain technologies into action has not been happening by itself. Instead, we have witnessed contestations among relevant agents – like politicians and experts – who engage in institutional work to serve their interests. Third, despite its global reach, technology is locally adapted and implemented in specific contexts. All in all, institutional theory helps us to explore further and critique the naïve belief, common in public discourse, in technology as a remedy of all things. Instead, it offers a more critical understanding of the cultural dynamics involved in putting technology to work in the coronavirus crisis. This critical lens carries implications for policymaking and implementation in times of crisis.     

Return Migration,Self‐selection and Entrepreneurship

Are return migrants more entrepreneurial? Existing literature has not addressed how estimating the impact of return migration on entrepreneurship is affected by double unobservable migrant self‐selection, both at the initial outward migration and at the final inward return migration stages. This paper exploits exogenous variation provided by the civil war and the incidence of agricultural plagues in Mozambique, as well as social unrest and other shocks in migrant destination countries. The results lend support to overall negative unobservable return migrant self‐selection, which results in an under‐estimation of the effects of return migration on entrepreneurial outcomes when using a ‘naïve’ estimator that does not control for self‐selection at both the initial migration and at the final return migration stages.     

信息化时代行政事业单位会计核算

论文主要针对信息化时代行政事业单位会计核算展开深入研究,首先阐述了信息化时代行政事业单位会计核算的现状,如思想观念落后、对信息化建设的认识不到位,内部控制乏力、信息化程度不高,缺乏信息化人才、单位内部活力不足等,然后提出了几点会计核算的措施,如对信息化建设给予高度重视、加强行政事业单位内部控制、打造专业化的会计核算队伍,以期通过以上措施,促进行政事业单位发展。     

Sign reversal in LIVE treatment effect estimates: The effect of vocational training on unemployment duration

We use administrative records in Israel to identify the treatment effect of vocational training for the unemployed on the duration of unemployment. We randomize training using data on the availability of courses in and around the time of becoming unemployed. This assumes that the timing of entry into unemployment is random, and course availability around the time of entry is independent of current labor market conditions. According to naïve estimators, which ignore self-selectivity, training reduces unemployment durations. The same applies to linear (IV) estimators, which control for self-selectivity. However, the opposite is found when non-linear IV estimators are used, i.e. training prolongs unemployment durations. Sign reversal depends on how the first stage is specified. A non-nested test indicates that the nonlinear estimate of the treatment effect is preferable to its linear counterpart, in which case training prolongs unemployment spells.     

‘Accounting Talk’ Through Metaphorical Representations: Change Agents and Organisational Change in Home-Based Elderly Care

This paper has analysed a change process within an organisation providing home-based elderly care. Using a theoretical framework from metaphor theory and insights from the literature on ‘accounting talk’, we followed how metaphorical representations of accounting were introduced and developed by the change agent. New core values and practices emerged within the home help unit that were in line with the ideas and inferences made by these accounting metaphors. The metaphorical representations of accounting concepts linked the unfamiliar domain of accounting to a more familiar domain, and provided rationales for organisational change. Our findings highlight the importance of change agents and ‘accounting talk’ for determining the trajectory of organisational change processes. The findings also suggest that metaphors are a potentially powerful tool for both changing organisational members’ general understanding of financial issues, and forging specific links between accounting concepts and work practices.     

Bourdieu Comes to Town: Part II

This essay introduces the second selection of essays associated with the Interventions forum ‘Bourdieu Comes to Town’, the first of which appeared in 2018. There seems to be a tendency to conflate the relevance of Bourdieu for urban studies by focusing specifically on particular contentious concepts that have been fashioned from Bourdieu's own tools. I briefly discuss this point with respect to the concepts of ‘elective belonging', which I helped introduce, and ‘territorial stigmatization’, which Loïc Wacquant himself has popularized. I argue that whatever the virtues or otherwise of these concepts, Bourdieu offers substantially more to urban studies, namely the capacity to synthesize across sites, to link micro and macro, to engage pragmatist concerns with daily life to a wider political economy, and to foreground the role of accumulation. I briefly show how the four essays in this selection exemplify these qualities.     

Co-evolutionary Dynamics Within and Between Firms: From Evolution to Co-evolution

abstract The extensive selection–adaptation literature spans diverse theoretical perspectives, but is inconclusive on the role of managerial intentionality in organizational adaptation. Indeed this voluminous literature has more to say about selection and sources and causes of structural inertia than about self‐renewing organizations that might counteract such inertia. In this introductory essay, we identify four co‐evolutionary generative mechanisms (engines) – naïve selection, managed selection, hierarchical renewal and holistic renewal – which illustrate the extensive range of evolutionary paths that can take place in a population of organizations. In particular, the managed selection engine provides the foundations of the underlying principles of co‐evolving self‐renewing organizations: managing internal rates of change, optimizing self‐organization, and balancing concurrent exploration and exploitation. However, it is altogether clear that empirical co‐evolution research represents the next frontier for empirically resolving the adaptation selection debate. The essay concludes with a discussion of requirements for co‐evolutionary empirical research and introduces the empirical papers in this Special Research Symposium.     

院校实验室科学管理思考

增强科学研究能力是当前实验室建设的主要任务。如何完成好这一任务、做好实验室的管理工作,是当前院校值得探讨的问题。从更新教育观念、健全规章制度、优化队伍结构、实行开放管理、营造和谐氛围5个方面,提出了提高院校实验室科学管理水平的对策。     

Unexplained gaps and Oaxaca–Blinder decompositions

We analyze four methods to measure unexplained gaps in mean outcomes: three decompositions based on the seminal work of Oaxaca (1973) and Blinder (1973) and an approach involving a seemingly naïve regression that includes a group indicator variable. Our analysis yields two principal findings. We show that the coefficient on a group indicator variable from an OLS regression is an attractive approach for obtaining a single measure of the unexplained gap. We also show that a commonly-used pooling decomposition systematically overstates the contribution of observable characteristics to mean outcome differences when compared to OLS regression, therefore understating unexplained differences. We then provide three empirical examples that explore the practical importance of our analytic results.     

Principles and algorithms for forecasting groups of time series: Locality and globality

Global methods that fit a single forecasting method to all time series in a set have recently shown surprising accuracy, even when forecasting large groups of heterogeneous time series. We provide the following contributions that help understand the potential and applicability of global methods and how they relate to traditional local methods that fit a separate forecasting method to each series:

• •Global and local methods can produce the same forecasts without any assumptions about similarity of the series in the set.
• •The complexity of local methods grows with the size of the set while it remains constant for global methods. This result supports the recent evidence and provides principles for the design of new algorithms.
• •In an extensive empirical study, we show that purposely naïve algorithms derived from these principles show outstanding accuracy. In particular, global linear models provide competitive accuracy with far fewer parameters than the simplest of local methods.

     

Confronting Price Endogeneity in a Duration Model of Residential Subdivision Development

Spatial equilibrium implies that distant factors are correlated with local prices through market mechanisms. Using this logic, we develop a novel approach for handling price endogeneity in land use models. We combine a control function approach with a duration model to identify the impact of prices in influencing land conversion. We find that failure to control for endogeneity results in large differences in elasticities. Specifically, we find an elasticity of 2.06 compared to 0.67 in a model without instrumentation. This difference is significant as it suggests that price‐based policies, such as ‘green taxes’, are likely more effective in altering development patterns than would be expected from a naïve estimation that ignores price endogeneity. Copyright © 2016 John Wiley & Sons, Ltd.     

Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)

While combining forecasts is well-known to reduce error, the question of how to best combine forecasts remains. Prior research suggests that combining is most beneficial when relying on diverse forecasts that incorporate different information. Here, I provide evidence in support of this hypothesis by analyzing data from the PollyVote project, which has published combined forecasts of the popular vote in U.S. presidential elections since 2004. Prior to the 2020 election, the PollyVote revised its original method of combining forecasts by, first, restructuring individual forecasts based on their underlying information and, second, adding naïve forecasts as a new component method. On average across the last 100 days prior to the five elections from 2004 to 2020, the revised PollyVote reduced the error of the original specification by eight percent and, with a mean absolute error (MAE) of 0.8 percentage points, was more accurate than any of its component forecasts. The results suggest that, when deciding about which forecasts to include in the combination, forecasters should be more concerned about the component forecasts’ diversity than their historical accuracy.     

Nonparametric estimation for a current status right‐censored data model

We consider the Case 1 interval censoring approach for right‐censored survival data. An important feature of the model is that right‐censored event times are not observed exactly, but at some inspection times. The model covers as particular cases right‐censored data, current status data, and life table survival data with a single inspection time. We discuss the nonparametric estimation approach and consider three nonparametric estimators for the survival function of failure time: maximum likelihood, pseudolikelihood, and the naïve estimator. We establish strong consistency of the estimators with the L₁ rate of convergence. Simulation results confirm consistency of the estimators.