Too many theories,too few facts? What the data tell us about the link between span of control,compensation and career dynamics

In this paper, we use a unique personnel dataset from a large European firm in an high tech manufacturing industry that provides information about hierarchical relationships. This unusually rare feature allows us to identify the chain of command. We provide a few stylized facts about the link between span of control, compensation and career dynamics and relate our findings to the existing theoretical literature of hierarchies in organizations: the assignment model, the incentives model, the information processing model, the supervision model, and the knowledge-based hierarchy model. We observe an increase in the span, an increase in wage inequality between job levels, and the introduction of a new hierarchical level. We also find that higher spans of control are associated with higher wages. The knowledge-based hierarchy provides the most likely explanation for these results when communication costs are decreasing. However, we also find evidence of learning and reallocation of talent within and across job levels, a finding that can not be explained by a static model of knowledge based hierarchy but rather by dynamic models of careers in organizations. Finally, we provide a few suggestions to enrich the existing theoretical literature and reconcile it with the facts.     

Reconciled boosted models for GEFCom2017 hierarchical probabilistic load forecasting

When forecasting time series in a hierarchical configuration, it is necessary to ensure that the forecasts reconcile at all levels. The 2017 Global Energy Forecasting Competition (GEFCom2017) focused on addressing this topic. Quantile forecasts for eight zones and two aggregated zones in New England were required for every hour of a future month. This paper presents a new methodology for forecasting quantiles in a hierarchy which outperforms a commonly-used benchmark model. A simulation-based approach was used to generate demand forecasts. Adjustments were made to each of the demand simulations to ensure that all zonal forecasts reconciled appropriately, and a weighted reconciliation approach was implemented to ensure that the bottom-level zonal forecasts summed correctly to the aggregated zonal forecasts. We show that reconciling in this manner improves the forecast accuracy. A discussion of the results and modelling performances is presented, and brief reviews of hierarchical time series forecasting and gradient boosting are also included.     

Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives

Forecast reconciliation is a post-forecasting process aimed to improve the quality of the base forecasts for a system of hierarchical/grouped time series. Cross-sectional and temporal hierarchies have been considered in the literature, but generally, these two features have not been fully considered together. The paper presents two new results by adopting a notation that simultaneously deals with both forecast reconciliation dimensions. (i) The closed-form expression of the optimal (in the least squares sense) point forecasts fulfilling both contemporaneous and temporal constraints. (ii) An iterative procedure that produces cross-temporally reconciled forecasts by alternating forecast reconciliation along one single dimension (either cross-sectional or temporal) at each iteration step. The feasibility of the proposed procedures, along with first evaluations of their performance as compared to the most performing ‘single dimension’ (either cross-sectional or temporal) forecast reconciliation procedures, is studied through a forecasting experiment on the 95 quarterly time series of the Australian Gross Domestic Product from Income and Expenditure sides. For this dataset, the new procedures, in addition to providing fully coherent forecasts in both cross-sectional and temporal dimensions, improve the forecast accuracy of the state-of-the-art point forecast reconciliation techniques.     

Design and cost optimization for hierarchical data

In this paper, we consider balanced hierarchical data designs for both one‐sample and two‐sample (two‐treatment) location problems. The variances of the relevant estimates and the powers of the tests strongly depend on the data structure through the variance components at each hierarchical level. Also, the costs of a design may depend on the number of units at different hierarchy levels, and these costs may be different for the two treatments. Finally, the number of units at different levels may be restricted by several constraints. Knowledge of the variance components, the costs at each level, and the constraints allow us to find the optimal design. Solving such problems often requires advanced optimization tools and techniques, which we briefly explain in the paper. We develop new analytical tools for sample size calculations and cost optimization and apply our method to a data set on Baltic herring.     

Tinbergen's Hierarchy Hypothesis Revisited

In this study, Tinbergen's hierarchy hypothesis is extended to include Dokmeci's optimization of the hierarchical production model. The optimal location of hierarchically coordinated plants is determined on a non-homogenous plane by taking into consideration price-elastic demand, production cost and transportation cost. The objective is to determine the maximum-profit location while satisfying the income constraint of the region. A stepwise heuristic approach is used for the solution. In the region, the markets are divided into optimum subsets according to a chosen number of plants in each level. Market demand is calculated with respect to a selected uniform price. The optimum location of plants is calculated iteratively by the use of Dökmeci's model in each level of the hierarchy. Then, the same procedure is repeated for different numbers of plants in each level of the hierarchy by taking into consideration the interdependence among the levels. The alternative which produces the maximum profits within the limits of regional income is determined as the best system.     

Online hierarchical forecasting for power consumption data

This paper proposes a three-step approach to forecasting time series of electricity consumption at different levels of household aggregation. These series are linked by hierarchical constraints—global consumption is the sum of regional consumption, for example. First, benchmark forecasts are generated for all series using generalized additive models. Second, for each series, the aggregation algorithm ML-Poly, introduced by Gaillard, Stoltz, and van Erven in 2014, finds an optimal linear combination of the benchmarks. Finally, the forecasts are projected onto a coherent subspace to ensure that the final forecasts satisfy the hierarchical constraints. By minimizing a regret criterion, we show that the aggregation and projection steps improve the root mean square error of the forecasts. Our approach is tested on household electricity consumption data; experimental results suggest that successive aggregation and projection steps improve the benchmark forecasts at different levels of household aggregation.     

Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks

We present a hierarchical architecture based on recurrent neural networks for predicting disaggregated inflation components of the Consumer Price Index (CPI). While the majority of existing research is focused on predicting headline inflation, many economic and financial institutions are interested in its partial disaggregated components. To this end, we developed the novel Hierarchical Recurrent Neural Network (HRNN) model, which utilizes information from higher levels in the CPI hierarchy to improve predictions at the more volatile lower levels. Based on a large dataset from the US CPI-U index, our evaluations indicate that the HRNN model significantly outperforms a vast array of well-known inflation prediction baselines. Our methodology and results provide additional forecasting measures and possibilities to policy and market makers on sectoral and component-specific price changes.     

Principles and Applications of Multilevel Modeling in Human Resource Management Research

Multilevel modeling is important for human resource management (HRM) research in that it often analyzes and interprets hierarchal data residing at more than one level of analysis. However, HRM research in general lags behind other disciplines, such as education, health, marketing, and psychology in the use of a multilevel analytical strategy. This article integrates the most recent literature into the theoretical and applied basics of multilevel modeling applicable to HRM research. A range of multilevel modeling issues have been discussed and they include statistical logic underpinning multilevel modeling, level conceptualization of variables, data aggregation, hypothesis tests, reporting mediation paths, and cross‐level interactions. An empirical example concerning complex cross‐level mediated moderation is presented that will suffice to illustrate the principles and the procedures for implementing a multilevel analytical strategy in HRM research. © 2015 Wiley Periodicals, Inc.     

Improving forecasting by estimating time series structural components across multiple frequencies

Identifying the most appropriate time series model to achieve a good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy. Multiple time series are constructed from the original time series, using temporal aggregation. These derivative series highlight different aspects of the original data, as temporal aggregation helps in strengthening or attenuating the signals of different time series components. In each series, the appropriate exponential smoothing method is fitted and its respective time series components are forecast. Subsequently, the time series components from each aggregation level are combined, then used to construct the final forecast. This approach achieves a better estimation of the different time series components, through temporal aggregation, and reduces the importance of model selection through forecast combination. An empirical evaluation of the proposed framework demonstrates significant improvements in forecasting accuracy, especially for long-term forecasts.     

Statistical efficiency in the linear combination of forecasts

In seeking an efficient combination of forecasts which minimises the forecast error variance, many methods have been suggested. Through analysis, simulation and case studies, this paper seeks to develop insights into the statistical circumstances which influence the relative accuracy of six of these methods. The six methods chosen have all been advocated in various publications and consist of ‘equal weighting’ (i.e., pooled average), ‘optimal’ (i.e., error variance minimising), ‘optimal with independence assumption’ (i.e., error variance minimising assuming zero correlation between individual forecast errors) and three variations on the formulation of a Bayesian combination based upon posterior probabilities. The statistical circumstances reflected varying conditions of relative forecast errors, error correlations and outliers.     

Influence costs and hierarchy

In an internal capital market, individual departments may compete for a share of the firm’s budget by engaging in wasteful influence activities. We show that firms with more levels of hierarchy may experience lower influence costs than less hierarchical firms, even though the former provide more opportunities for exerting influence. The unique influence-cost minimizing hierarchy is strongly asymmetric. With a linear production technology this is also the optimal hierarchy. If individual departments have different productivities, however, and the production technology exhibits decreasing returns to scale, a symmetric hierarchy that does not minimize influence costs may be optimal.Received: July 2004, Accepted: October 2004, JEL Classification: D74, G31, G34We thank Martin Hellwig, seminar participants at the University of Mannheim, and an anonymous referee for helpful comments, and Kai Konrad for handling the editorial tasks on this paper. Financial support from Deutsche Forschungsgemeinschaft, Sonderforschungsbereich 504 (Inderst and Müller) and the Bank of Sweden Tercentenary Foundation (Wärneryd) is gratefully acknowledged.     

Alignment mechanisms for supplier-initiated innovation: Results from the logistics service industry

In service outsourcing, buying organizations frequently experience shortcomings in supplier-initiated innovation. To address this problem, we develop a model that distinguishes two mechanisms for buyer-supplier alignment (i.e. relational and formal innovation alignment) and outlines how their effectiveness differs depending on the level of hierarchy in the relationship. Using data from the logistics service industry, it is shown that when the level of hierarchy is comparatively low, where buyers and suppliers interact participatively, both relational and formal innovation alignment are effective in fostering supplier-initiated innovation. Yet, as the relationship becomes more hierarchical and dominated by the buyer, the effect of relational innovation alignment diminishes and eventually turns negative, while the effect of formal innovation alignment is strengthened.     

Dimensionality reduction in forecasting with temporal hierarchies

Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. It can be challenging to estimate the full cross-covariance matrix for a temporal hierarchy, which can easily be of very large dimension, yet it is difficult to know a priori which part of the error structure is most important. To address these issues, we propose to use eigendecomposition for dimensionality reduction when reconciling forecasts to extract as much information as possible from the error structure given the data available. We evaluate the proposed estimator in a simulation study and demonstrate its usefulness through applications to short-term electricity load and financial volatility forecasting. We find that accuracy can be improved uniformly across all aggregation levels, as the estimator achieves state-of-the-art accuracy while being applicable to hierarchies of all sizes.     

Longitudinal Meta-analysis

The goal of meta-analysis is to integrate the research results of a number of studies on a specific topic. Characteristic for meta-analysis is that in general only the summary statistics of the studies are used and not the original data. When the published research results to be integrated are longitudinal, multilevel analysis can be used for the meta-analysis. We will demonstrate this with an example of longitudinal data on the mental development of infants. We distinguish four levels in the data. The highest level (4) is the publication, in which the results of one or more studies are published. The third level consists of the separate studies. At this level we have knowledge about the degree of prematurity of the group of infants in the specific study. The second level are the repeated measures. We have data about the test age, the mental development, the corresponding standard deviations, and the sample sizes. The lowest level is needed for the specification of the meta-analysis model. Both the way in which the multilevel model has to be specified (the Mln-program is used) as the results will be presented and interpreted.     

以人为本,促进城市经济建设可持续发展——2007-2015年黑龙江省人才资源需求预测

本文依据相关统计数据及其相互关系,推算出黑龙江省历年从业人才资源总量,并构建GM(1,1)模型和二元线性回归模型,在此基础上,分别对黑龙江省2007-2015年从业人才需求进行了预测,并运用二模型最优组合预测对预测结果进行修正.由此,提出了实现预测目标的策略建议,为黑龙江省人才开发和培养决策提供参考.     

Judgmental adjustment of forecasts: A comparison of methods

Attention has recently been given to combinations of subjective and objective forecasts to improve forecast accuracy. This research offers an extension on this theme by comparing two methods that can be used to adjust an objective forecast. Wolfe and Flores (1990) show that forecasts can be judgmentally adjusted by analysts using a structured approach based on Saaty's analytic hierarchy process (AHP). In this study, the centroid method is introduced as a vehicle for forecast adjustment and is compared to the AHP. While the AHP allows for finer tuning in reflecting decision maker judgement, the centroid method produces very similar results and is much simpler to use in the forecast adjustment process.     

Analysis of longitudinal data using the hierarchical linear model

The hierarchical linear model in a linear model with nested random coefficients, fruitfully used for multilevel research. A tutorial is presented on the use of this model for the analysis of longitudinal data, i.e., repeated data on the same subjects. An important advantage of this approach is that differences across subjects in the numbers and spacings of measurement occasions do not present a problem, and that changing covariates can easily be handled. The tutorial approaches the longitudinal data as measurements on populations of (subject-specific) functions.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

FFORMPP: Feature-based forecast model performance prediction

This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features calculated from historical time series with an efficient Bayesian multivariate surface regression approach. The minimum predicted forecast error is then used to identify an individual model or a combination of models to produce the final forecasts. It is well known that the performance of most meta-learning models depends on the representativeness of the reference dataset used for training. In such circumstances, we augment the reference dataset with a feature-based time series simulation approach, namely GRATIS, to generate a rich and representative time series collection. The proposed framework is tested using the M4 competition data and is compared against commonly used forecasting approaches. Our approach provides comparable performance to other model selection and combination approaches but at a lower computational cost and a higher degree of interpretability, which is important for supporting decisions. We also provide useful insights regarding which forecasting models are expected to work better for particular types of time series, the intrinsic mechanisms of the meta-learners, and how the forecasting performance is affected by various factors.     

Data visualization and forecast combination for probabilistic load forecasting in GEFCom2017 final match

This paper describes the methods used by Team Cassandra, a joint effort between IBM Research Australia and the University of Melbourne, in the GEFCom2017 load forecasting competition. An important first phase in the forecasting effort involved a deep exploration of the underlying dataset. Several data visualisation techniques were applied to help us better understand the nature and size of gaps, outliers, the relationships between different entities in the dataset, and the relevance of custom date ranges. Improved, cleaned data were then used to train multiple probabilistic forecasting models. These included a number of standard and well-known approaches, as well as a neural-network based quantile forecast model that was developed specifically for this dataset. Finally, model selection and forecast combination were used to choose a custom forecasting model for every entity in the dataset.