Predicting relative forecasting performance: An empirical investigation

The relative performances of forecasting models change over time. This empirical observation raises two questions. First, is the relative performance itself predictable? Second, if so, can it be exploited in order to improve the forecast accuracy? We address these questions by evaluating the predictive abilities of a wide range of economic variables for two key US macroeconomic aggregates, namely industrial production and inflation, relative to simple benchmarks. We find that business cycle indicators, financial conditions, uncertainty and measures of past relative performances are generally useful for explaining the models’ relative forecasting performances. In addition, we conduct a pseudo-real-time forecasting exercise, where we use the information about the conditional performance for model selection and model averaging. The newly proposed strategies deliver sizable improvements over competitive benchmark models and commonly-used combination schemes. The gains are larger when model selection and averaging are based on both financial conditions and past performances measured at the forecast origin date.     

Real-time macroeconomic forecasting with leading indicators: An empirical comparison

This paper demonstrates that the Conference Board’s Composite Leading Index (CLI) has significant real-time predictive ability for Industrial Production (IP) growth rates at horizons from one to six months ahead over the period 1989-2009. A popular but unrealistic analysis, which combines real-time data for CLI and final vintage data for IP as predictor variables, obscures the actual predictive content of the CLI, in the sense that in that case, the improvements in forecast accuracy relative to a univariate AR model are not significant. The CLI appears to be less useful for forecasting growth rates of the Conference Board’s Composite Coincident Index (CCI) in real time, as a univariate AR model performs better. This result is mostly due to its disappointing performance during the first five years of the forecast period. The CLI may not be the best way of exploiting the information contained in the underlying individual leading indicator variables. The use of principal components instead of CLI leads to more accurate real-time forecasts for both IP and CCI growth rates.     

An empirical investigation of water consumption forecasting methods

Many regions on earth face daily limitations in the quantity and quality of the water resources available. As a result, it is necessary to implement reliable methodologies for water consumption forecasting that will enable the better management and planning of water resources. This research analyses, for the first time, a large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas of Europe, which faces issues of droughts and overconsumption in the hot summer months. Using the R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting techniques including a machine-learning algorithm, with very promising results.     

Demand forecasting and order planning for humanitarian logistics: An empirical assessment

Humanitarian aid organizations are most known for their short-term emergency relief. While getting aid items to those in need can be challenging, long-term projects provide an opportunity for demand planning supported by forecasting methods. Based on standardized consumption data of the Operational Center Amsterdam of Médecins Sans Frontières (MSF-OCA) regarding nineteen longer-term aid projects and over 2000 medical items consumed in 2013, we describe and analyze the forecasting and order planning process. We find that several internal and external factors influence forecast and order planning performance, be it indirectly through demand volatility and safety markup. Moreover, we identify opportunities for further improvement for MSF-OCA, and for humanitarian logistics organizations in general.     

An empirical comparison of cross-impact models for forecasting sales

This paper compares a set of four cross-impact models: (1) additive, (2) likelihood multiplier, (3) R-space, and (4) a model constructed by the author. This is done by examining a forecasting problem encountered by an industrial firm. The forecasting problem was to study the market trend in order to decide whether to expand the production capacity of a ceramics plant. In spite of their different theoretical premises, the models yielded similar results. However, only the R-space model produced results that differed from the others. The paper also suggests a method that should avoid some internal contradictions of the cross-impact models.     

The use of multiple scenarios in sales forecasting: An empirical test

Nearly 500 annual sales forecasts were generated from the responses of 82 subjects who were presented with either a time-series plot of historical sales data by itself or with the same plus three scenarios, and were then asked to make forecasts. Sales forecasts were made in either a stable or an unstable environment. The findings did not support the claims made by scenario advocates. Scenarios did not make unexpected outcomes less surprising. Instead, scenarios were found to increase confidence in a favored forecast. Furthermore, no support was found for the contention that scenarios improved upon ‘eyeball’ extrapolations or made judgmental sales forecasts more accurate than quantitative extrapolations. Scenarios were found to be tainted by many of the same biases previously identified by cognitive psychologists.     

Political events and the foreign direct investment decision: An empirical examination

This study investigates the impact of political events on US manufacturing direct investment in Latin America. The relationship between foreign direct investment and political events (and economic factors) is examined through regression analysis of pooled time-series (21 years) and cross-sectional (8 countries) data. In contrast to previous studies using an econometric approach, this study finds that political events do affect direct investment decisions. Specifically, both intra-nation and inter-nation conflict and co-operation have an impact on US direct investment decisions concerning Latin American countries. In addition, the host country's market size and market growth affect these decisions by US multinational firms.     

Overconfidence and disappointment in venture capital decision making: An empirical examination

We examine the effects of overconfidence in venture capital investing. Overconfidence in financial decision making is a robust, well‐established finding, and its consequences for decisions by equity market investors, startup entrepreneurs, and CEO's of large firms have been comprehensively examined and documented. This paper considers the behavioral consequences of overconfidence by venture capitalists, comparing their anticipated returns to actual average returns. Our primary metric is Bell's disappointment, a measure of the difference between anticipated and actual payoff. We construct both deterministic and stochastic models, based on empirical data. We find that the average venture capitalist will experience substantial disappointment.     

The location of initial public offering headquarters: An empirical examination

We study the headquarters location of U.S. firms with an initial public offering (IPO) over the 2001–2011 period. Specifically, we examine IPO intensity, defined as IPOs in a state scaled by state population. We find that IPO intensity is positively related to various measures of education. We also find that IPO intensity is positively related to an economic climate (freedom) index, degree of urbanization, and whether a state contains a financial center. Some economists see IPOs as a driver of economic growth. Thus, our results suggest factors that government officials may consider to increase the number of IPOs headquartered in their states.     

New perspectives on forecasting inflation in emerging market economies: An empirical assessment

We use a broad-range set of inflation models and pseudo out-of-sample forecasts to assess their predictive ability among 14 emerging market economies (EMEs) at different horizons (1–12 quarters ahead) with quarterly data over the period 1980Q1-2016Q4. We find, in general, that a simple arithmetic average of the current and three previous observations (the RW-AO model) consistently outperforms its standard competitors—based on the root mean squared prediction error (RMSPE) and on the accuracy in predicting the direction of change. These include conventional models based on domestic factors, existing open-economy Phillips curve-based specifications, factor-augmented models, and time-varying parameter models. Often, the RMSPE and directional accuracy gains of the RW-AO model are shown to be statistically significant. Our results are robust to forecast combinations, intercept corrections, alternative transformations of the target variable, different lag structures, and additional tests of (conditional) predictability. We argue that the RW-AO model is successful among EMEs because it is a straightforward method to downweight later data, which is a useful strategy when there are unknown structural breaks and model misspecification.     

Fiscal forecasting performance in an emerging economy: An empirical assessment of Brazil

This study makes use of Brazilian data to analyze government budget balance forecast errors. Besides the analysis of the quality and efficiency of budget balance forecasts, economic, political, and institutional and governance dimensions are explored. The findings show that the data forecasts have low quality and efficiency. Furthermore, it is observed that the budget forecast error is subject to a backward-looking effect, a bias in the economic growth forecasts, as well as cyclical fluctuations. Finally, electoral cycles represent a source of overestimated forecasts, and strong institutions and governance supported by the public are able to suppress opportunistic motivations in budget forecasts.     

An empirical examination of strategic sourcing dominant logic: Strategic sourcing centricity

Recently, much of the thought in strategic sourcing, i.e., dominant logic, has shifted away from the exchange of tangible goods and toward the exchange of intangibles, specialized skills and knowledge, and processes. This study refers to strategic sourcing dominant logic as strategic sourcing centricity (SSC) and describes it as a sourcing management′s mindset based on learning, performance, planning, and relational orientations and manifests itself in the implementation of SS to meet supply management objectives and satisfy stakeholder requirements. Building on insights on intangibility derived from resource-based theory (RBV), the study proposes and empirically tests strategic sourcing centricity (SSC). The authors test the operational measures of SSC dimensions and its impact on performance on a sample of 174 supply management executives. The results indicate strong support for the theorized framework. Managerial implications and future research agenda are provided.     

Retailer-manufacturer price and profit relationships along the advertising life cycle: An empirical examination

Competitive intra-channel forces are an important factor to the success (or failure) of newly introduced brands. The present study offers an extended theoretical framework of the advertising life-cycle concept, which illuminates the changes in the manufacturer-retailer balance of power within the marketing channel along the development path of new brands. The theoretical conclusions are then tested against a unique data set. The empirical findings confirm the prediction that dynamic competitive elements will change the intra-channel pricing balance.     

How supply chain architecture and product architecture impact firm performance: An empirical examination

In order to enhance performance, manufacturing firms are building various capabilities and utilizing supply chain networks that are geographically dispersed around the globe. This study examines how decisions related to supply chain architecture and product architecture influence organizational competitiveness. Building on the co-specialization perspective, we evaluate the performance implications of product modularity and supply chain agility. While, product modularity constitutes a product configuration related to sourcing and assembling of products, supply chain agility is a supply chain configuration related to velocity (sensing, comprehending, and responding) and visibility (supplier network, internal operations, and external environment). Both the product and supply chain architectural decisions can impact performance, individually and in conjunction with each other. We empirically test the hypothesized relationships using data collected from 103 manufacturing firms. Results show that supply chain agility and product modularity directly enhance responsiveness and enable organizations to reduce cost. Furthermore, supply chain agility partially mediates the relationship between product modularity and both responsiveness and cost reduction. We elaborate on the key contributions of this study for both research and practice, discuss limitations, and also offer various avenues for further research.     

Factors affecting the nature and effectiveness of subjective revision in sales forecasting: An empirical study

Recent empirical results have indicated that sales forecasts obtained from a quantitative model can be improved through subjective revision undertaken by management. This study seeks to determine the extent to which the improvement in forecast performance is dependent upon the individual revising the forecast rather than the specific product under consideration. As any improvement in the forecast is a direct consequence of the adjustment effected, differences in the nature of revision among the individuals concerned and between different types of product are also examined. The results reveal no statistically significant differences between the managers involved in revision activity in terms of their ability to improve the forecast; on the other hand, significant differences in the number of forecasts improved were found between different types of product. As far as the nature of subjective revision is concerned, the results show a significant variation both between different types of product and between managers. Further, the improvement in forecast performance obtained through revision was found to be positively related to the relative size of the adjustment applied.     

Environmental,social and governance initiatives and wealth creation for firms: An empirical examination

This study examines the effect of environmental, social, and governance (ESG) activities on firm performance of 4,887 global companies. Mean difference test shows that firms with a high level of ESG activities are different from their low-ESG counterparts. The two-stage least square results suggest that ESG activities on (a) the welfare for internal stakeholders and best corporate governance practices are beneficial for firm performance and (b) antitakeover mechanisms (pollution control) adopted by firms are negatively (positively) valued by market players. Overall, this is the first study to examine the effects of ESG on the market-based and accounting-based performance of global firms.     

An empirical analysis of neural network memory structures for basin water quality forecasting

This research investigates the cumulative multi-period forecast accuracy of a diverse set of potential forecasting models for basin water quality management. The models are characterized by their short-term (memory by delay or memory by feedback) and long-term (linear or nonlinear) memory structures. The experiments are conducted as a series of forecast cycles, with a rolling origin of a constant fit size. The models are recalibrated with each cycle, and out-of-sample forecasts are generated for a five-period forecast horizon. The results confirm that the JENN and GMNN neural network models are generally more accurate than competitors for cumulative multi-period basin water quality prediction. For example, the JENN and GMNN models reduce the cumulative five-period forecast errors by as much as 50%, relative to exponential smoothing and ARIMA models. These findings are significant in view of the increasing social and economic consequences of basin water quality management, and have the potential for extention to other scientific, medical, and business applications where multi-period predictions of nonlinear time series are critical.     

Learning and relationship orientation: an empirical examination in European museums

• The purpose of this paper is to assess the interrelations between learning orientation, innovation strategy, relationship orientation and economic and social performance in the management of cultural organizations such as museums. We first provide a review of the literature addressing the main constructs involved in the research: learning orientation, innovation, relationship orientation and performance, and we detail the model's hypotheses reflecting the interrelations amongst the proposed variables. Building on extensive literature, a model is developed and empirically tested using survey data collected from 491 European museums in Spain, France, Italy and the UK. Data are analysed through structural equation modelling. In the present study, evidence is found to support the positive and significant link between learning orientation and internal (organizational innovation) as well as external (relationship orientation) changes in museums. Further, we find that organizational innovation and relationship orientation aid the introduction of greater technological developments in these organizations. We also find evidence to support the idea that learning orientation, innovation strategy and relationship orientation impact the economic and social performance of museums. Findings clearly show that achieving organizational objectives through learning processes necessarily entails the introduction of internal changes—innovation—and external relationships—relationship orientation.

Copyright © 2014 John Wiley & Sons, Ltd.     

Role of explicit and tacit knowledge in Six Sigma projects: An empirical examination of differential project success

This research develops a conceptual model for predicting success of process improvement projects as a result of knowledge-creation practices employed in the projects. The model is empirically examined in the context of Six Sigma black belt projects. New scales are developed to measure explicit- and tacit-knowledge-creation practices in process improvement. Data is gathered via a cross-sectional sample, and the hypotheses are tested using hierarchical regression. Our results support the notion that knowledge-creation practices influence the success of process improvement projects. Specifically, the inclusion of softer, people-oriented practices for capturing tacit knowledge explains a significant amount of variance in project success, as much as the more analytically focused practices that capture explicit knowledge. This research offers practical insights about the influence of practices that project managers use to create new knowledge by capturing explicit and tacit knowledge, and seeks to advance theoretical understanding of process improvement.