Jumps in cross‐sectional rank and expected returns: a mixture model

We propose a new nonlinear time series model of expected returns based on the dynamics of the cross‐sectional rank of realized returns. We model the joint dynamics of a sharp jump in the cross‐sectional rank and the asset return by analyzing (1) the marginal probability distribution of a jump in the cross‐sectional rank within the context of a duration model, and (2) the probability distribution of the asset return conditional on a jump, for which we specify different dynamics depending upon whether or not a jump has taken place. As a result, the expected returns are generated by a mixture of normal distributions weighted by the probability of jumping. The model is estimated for the weekly returns of the constituents of the SP500 index from 1990 to 2000, and its performance is assessed in an out‐of‐sample exercise from 2001 to 2005. Based on the one‐step‐ahead forecast of the mixture model we propose a trading rule, which is evaluated according to several forecast evaluation criteria and compared to 18 alternative trading rules. We find that the proposed trading strategy is the dominant rule by providing superior risk‐adjusted mean trading returns and accurate value‐at‐risk forecasts. Copyright © 2008 John Wiley & Sons, Ltd.     

The dynamics of public opinion under majority rules

This note explains the process of public opinion formation via a locally interactive, space-time analysis. The model we use is a special case of the general framework for modelling social interaction proposed in Blume and Durlauf (2001). In the reduced form of the model we study how each individual, when faced with the choice of one, out of two, opinions, tends to conform to the opinion held by the majority of her neighbours. We consider different, symmetric and asymmetric, majority rules. Depending on the specific behavioral rule, the aggregate process of opinion formation may display contagion on one specific opinion, or consensus among all individuals in the population, or co-existence of both opinions. Whenever consensus obtains, we observe the formation of homogeneous areas ( clusters) that seem almost stationary along the dynamics. The authors wish to thank A. Postelwaite and two anonymous referees for very helpful suggestions.     

Policy and Planning Programs as Goals of Scientific Work: interdisciplinary Training for Social Planning as a Case Study in Applied Social Science

Abstract . Policy and planning have now become the field of a new profession—actually a series of professions—in and out of academic life as each of the specialized social and behavioral sciences is applied to the problems of society by some of its professional practitioners. Specialism, however, tends to make of problems in real life constructs that fit within the borders of one's specialty, constructs which have little relation to reality. To transcend specialism's limitations without prejudice to specialization, interdisciplinary training is proposed and worked out for a single area, social policy and social planning.     

Model selection with estimated factors and idiosyncratic components

This paper provides consistent information criteria for the selection of forecasting models that use a subset of both the idiosyncratic and common factor components of a big dataset. This hybrid model approach has been explored by recent empirical studies to relax the strictness of pure factor‐augmented model approximations, but no formal model selection procedures have been developed. The main difference to previous factor‐augmented model selection procedures is that we must account for estimation error in the idiosyncratic component as well as the factors. Our main contribution is to show the conditions required for selection consistency of a class of information criteria that reflect this additional source of estimation error. We show that existing factor‐augmented model selection criteria are inconsistent in circumstances where N is of larger order than , where N and T are the cross‐section and time series dimensions of the dataset respectively, and that the standard Bayesian information criterion is inconsistent regardless of the relationship between N and T. We therefore propose a new set of information criteria that guarantee selection consistency in the presence of estimated idiosyncratic components. The properties of these new criteria are explored through a Monte Carlo simulation study. The paper concludes with an empirical application to long‐horizon exchange rate forecasting using a recently proposed model with country‐specific idiosyncratic components from a panel of global exchange rates.     

From constrained optimization to constrained dynamics: extending analogies between economics and mechanics

Economic equilibrium models have been inspired by analogies to stationary states in classical mechanics. To extend these mathematical analogies from constrained optimization to constrained dynamics, we formalize economic (constraint) forces and economic power in analogy to physical (constraint) forces and the reciprocal value of mass. Agents employ forces to change economic variables according to their desire and their power to assert their interest. These ex-ante forces are completed by constraint forces from unanticipated system constraints to yield the ex-post dynamics. The differential-algebraic equation framework seeks to overcome some restrictions inherent to the optimization approach and to provide an out-of-equilibrium foundation for general equilibrium models. We transform a static Edgeworth box exchange model into a dynamic model with procedural rationality (gradient climbing) and slow price adaptation, and discuss advantages, caveats, and possible extensions of the modeling framework.

     

Asymptotic theory and econometric practice

The classical paradigm of asymptotic theory employed in econometrics presumes that model dimensionality, p, is fixed as sample size, n, tends to inifinity. Is this a plausible meta-model of econometric model building? To investigate this question empirically, several meta-models of cross- sectional wage equation models are estimated and it is concluded that in the wage-equation literature at least that p increases with n roughly like n ^l/4, while that hypothesis of fixed model dimensionality of the classical asymptotic paradigm is decisively rejected. The recent theoretical literature on ‘large-p’ asymptotics is then very briefly surveyed, and it is argued that a new paradigm for asymptotic theory has already emerged which explicitly permits p to grow with n. These results offer some guidance to econometric model builders in assessing the validity of standard asymptotic confidence regions and test statistics, and may eventually yield useful correction factors to conventional test procedures when p is non-negligible relative to n.     

Multiple capital inputs, Q, and investment spending

Despite their explicit treatment of dynamics and solid theoretical basis, investment models based on the Brainard-Tobin Q have recorded a generally disappointing empirical performance. When the Q model is expanded to recognize the possibility that the value of the firm depends on two or more capital inputs with differing adjustment cost technologies, the econometric equation following from optimizing behavior includes Q as well as a set of additional explanatory variables. The importance of these omitted variables is assessed, and the capital homogeneity assumption for equipment and structures implicit in Conventional Q models is rejected. The Multi-Capital Q model is then extended in two ways: (i) adding inventory, research and development, and labor as quasi-fixed factors and (ii) exploring the sensitivity of the instrumental variables estimates to normalization. We conclude that the Multi-Capital Q model is a useful extension that overcomes an important omitted variables problem in the Conventional Q framework.     

Creative Cities,Graffiti and Culture-Led Development in South Africa: Dlala Indima (‘Play Your Part’)

Creative cities and culture-led development discourses have come under increasing scrutiny as elite-centric economic development agendas tend to trump ‘civic creativity’ ideals as imagined by Charles Landry. In South Africa, culture-led development and cultural policy tends to primarily mimic that of the global North, largely focusing on culture as a catalyst for economic and property development. Public art commissioning processes tend to focus on decorative projects as part of urban upgrading, which are often associated with ensuing gentrification and displacement of the urban poor. In contrast to focusing on these kinds of regeneration strategies, this article investigates Dlala Indima, a hip-hop-led graffiti project in a rural township in the Eastern Cape of South Africa. This article situates graffiti as a critical social and spatial practice to argue that this case challenges normative cultural planning paradigms. Dlala Indima's work is an alternative approach to cultural development by and for young people who are usually marginalized by the mainstream practice of culture-led economic development. The project challenges dominant creative cities and culture-led development discourses in three ways: first, it challenges the normative processes of regeneration; secondly, it grounds participatory practice; and finally, it shifts participation from ‘tyranny to transformation’ through the ubuntu of hip-hop, the notion of ubuntu being based on the communitarian notion of ‘ubuntu, ngubuntu ngabantu’—‘I am because you are’.     

Book Reviews

Simmie, James (ed.), Innovation, networks and learning regions? Benko, Georges and Ulf Strohmayer (eds.), Space and social theory. Interpreting modernity and postmodernity Borja, Jordi and Manuel Castells, Local and global: management of cities in the information age Harris, Richard, Unplanned suburbs. Toronto’s American tragedy, 1900–1960 and Hill, Patricia, Dallas: the making of a modern city Wit, Joop W. de, Poverty, policy and politics in Madras slums: dynamics of survival, gender and leadership     

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.

     

Coherent forecasting for count time series using Box–Jenkins's AR(p) model

During the last three decades, integer‐valued autoregressive process of order p [or INAR(p)] based on different operators have been proposed as a natural, intuitive and maybe efficient model for integer‐valued time‐series data. However, this literature is surprisingly mute on the usefulness of the standard AR(p) process, which is otherwise meant for continuous‐valued time‐series data. In this paper, we attempt to explore the usefulness of the standard AR(p) model for obtaining coherent forecasting from integer‐valued time series. First, some advantages of this standard Box–Jenkins's type AR(p) process are discussed. We then carry out our some simulation experiments, which show the adequacy of the proposed method over the available alternatives. Our simulation results indicate that even when samples are generated from INAR(p) process, Box–Jenkins's model performs as good as the INAR(p) processes especially with respect to mean forecast. Two real data sets have been employed to study the expediency of the standard AR(p) model for integer‐valued time‐series data.     

Forecasting ATM cash demands using a local learning model of cerebellar associative memory network

Forecasting cash demands at automatic teller machines (ATMs) is challenging, due to the heteroskedastic nature of such time series. Conventional global learning computational intelligence (CI) models, with their generalized learning behaviors, may not capture the complex dynamics and time-varying characteristics of such real-life time series data efficiently. In this paper, we propose to use a novel local learning model of the pseudo self-evolving cerebellar model articulation controller (PSECMAC) associative memory network to produce accurate forecasts of ATM cash demands. As a computational model of the human cerebellum, our model can incorporate local learning to effectively model the complex dynamics of heteroskedastic time series. We evaluated the forecasting performance of our PSECMAC model against the performances of current established CI and regression models using the NN5 competition dataset of 111 empirical daily ATM cash withdrawal series. The evaluation results show that the forecasting capability of our PSECMAC model exceeds that of the benchmark local and global-learning based models.     

Variations in Social Quality of Life Indicators in Medium Metropolitan Areas*

Abstract . The basic objective of this paper is to develop a systematic model by which a series of social indicators can be constructed to reflect variations in the social quality of life (QOL) among the medium metropolitan areas. Statistical data which are logically representative of the indicators were collected for the 83 medium metropolitan areas with populations between 200,000 and 500,000 in 1970. More than 50 variables were evaluated under these critical social components: individual concerns, individual equality and community living conditions. The standardized “Z” score method was employed to compute, based on 1970 data, the index for each of the social QOL indicators. The 83 SMSAs were then rated by the indexes. These indexes will help decision makers to identify their regional weaknesses and strengths in comparison to others so that appropriate action can be taken toward the improvement of social QOL in those areas.     

Exact and asymptotic tests for possibly non-regular hypotheses on stochastic volatility models

We study the problem of testing hypotheses on the parameters of one- and two-factor stochastic volatility models (SV), allowing for the possible presence of non-regularities such as singular moment conditions and unidentified parameters, which can lead to non-standard asymptotic distributions. We focus on the development of simulation-based exact procedures–whose level can be controlled in finite samples–as well as on large-sample procedures which remain valid under non-regular conditions. We consider Wald-type, score-type and likelihood-ratio-type tests based on a simple moment estimator, which can be easily simulated. We also propose a C(α)-type test which is very easy to implement and exhibits relatively good size and power properties. Besides usual linear restrictions on the SV model coefficients, the problems studied include testing homoskedasticity against a SV alternative (which involves singular moment conditions under the null hypothesis) and testing the null hypothesis of one factor driving the dynamics of the volatility process against two factors (which raises identification difficulties). Three ways of implementing the tests based on alternative statistics are compared: asymptotic critical values (when available), a local Monte Carlo (or parametric bootstrap) test procedure, and a maximized Monte Carlo (MMC) procedure. The size and power properties of the proposed tests are examined in a simulation experiment. The results indicate that the C(α)-based tests (built upon the simple moment estimator available in closed form) have good size and power properties for regular hypotheses, while Monte Carlo tests are much more reliable than those based on asymptotic critical values. Further, in cases where the parametric bootstrap appears to fail (for example, in the presence of identification problems), the MMC procedure easily controls the level of the tests. Moreover, MMC-based tests exhibit relatively good power performance despite the conservative feature of the procedure. Finally, we present an application to a time series of returns on the Standard and Poor’s Composite Price Index.     

The Identification of a Mixture of First‐Order Binary Markov Chains*

Let S be the number of components in a finite discrete mixing distribution. We prove that the number of waves of panel being greater than or equal to 2S is a sufficient condition for global identification of a dynamic binary choice model in which all the parameters are heterogeneous. This model results in a mixture of S binary first‐order Markov Chains.     

The role of the physical work environment for creative employees – a case study of digital artists

The purpose of the study was to explore the role of the physical environment for creative employees. The results are based on interviews with office-working digital artists. The physical work environment was considered to offer three types of support for creative work for the participants: functional, psychosocial and inspirational. Creative processes would find better breeding ground if functional support, such as adequate lighting and tools, and psychosocial support, such as spatial possibilities for both privacy and communication, were provided. Without inspirational support, such as brainstorming rooms, dynamic planning and imaginative interior design, the work outcome was believed to become less creative. The physical environmental support model can be used by companies with an interest to provide creativity supportive workplaces.     

Does global inflation help forecast inflation in industrialized countries?

Ciccarelli and Mojon (CM; Review of Economics and Statistics, 2010, 92(3), 524–535) propose an inflation forecasting model incorporating a global inflation factor and show that it consistently beats several standard forecasting benchmarks. We show that CM's global inflation model does not improve upon the Atkeson and Ohanian (AO; Federal Reserve Bank of Minneapolis Quarterly Review, 2001, 25(1), 2–11) naive benchmark. However, we find that augmenting the AO model with a global inflation factor improves forecast accuracy at longer horizons, supporting CM's claim about the usefulness of global inflation.     

FEASIBLE CROSS-VALIDATORY MODEL SELECTION FOR GENERAL STATIONARY PROCESSES

Cross-validation is a method used to estimate the expected prediction error of a model. Such estimates may be of interest in themselves, but their use for model selection is more common. Unfortunately, cross-validation is viewed as being computationally expensive in many situations. In this paper it is shown that the h-block cross-validation function for least-squares based estimators can be expressed in a form which can enormously impact on the amount of calculation required. The standard approach is of O(T²) where T denotes the sample size, while the proposed approach is of O(T) and yields identical numerical results. The proposed approach has widespread potential application ranging from the estimation of expected prediction error to least squares-based model specification to the selection of the series order for non-parametric series estimation. The technique is valid for general stationary observations. Simulation results and applications are considered. © 1997 by John Wiley & Sons, Ltd.     

Model selection in univariate time series forecasting using discriminant analysis

When a large number of time series are to be forecast on a regular basis, as in large scale inventory management or production control, the appropriate choice of a forecast model is important as it has the potential for large cost savings through improved accuracy. A possible solution to this problem is to select one best forecast model for all the series in the dataset. Alternatively one may develop a rule that will select the best model for each series. Fildes (1989) calls the former an aggregate selection rule and the latter an individual selection rule. In this paper we develop an individual selection rule using discriminant analysis and compare its performance to aggregate selection for the quarterly series of the M-Competition data. A number of forecast accuracy measures are used for the evaluation and confidence intervals for them are constructed using bootstrapping. The results indicate that the individual selection rule based on discriminant scores is more accurate, and sometimes significantly so, than any aggregate selection method.     

A Meta-Analysis of the Relationship Between Procedural Justice and Distributive Justice: Implications for Justice Research

Although there are many studies that utilize the constructs of procedural and distributive justice, this research tends to ignore the implications of the bivariate relationship between the two constructs. The stronger the relationship between the two constructs, the more problematic ignoring this relationship becomes. Therefore, a meta-analysis was conducted to estimate the relationship between procedural and distributive justice. We also conducted an initial assessment of extent to which the relationship between procedural and distributive justice was context sensitive. Finally, a series of methodological moderators was evaluated. Results indicated that the relationship between procedural and distributive justice is strong (= .64) across all studies. However, this relationship was moderated by research context, and even within research context, there was substantial evidence of variability. The results of the meta-analysis were discussed in terms of implications for theory, research methods, and construct measurement in justice research.