Local identification of ARMAX structures subject to nonlinear constraints

Summary We obtain necessary and sufficient conditions for the local identification of a simultaneous ARMAX structure whose parameters are subject to nonlinear constraints. A global identification condition is also obtained. The results generalize theWegge [1965] andRothenberg [1971] identification criteria for a classical simultaneous equations model.This paper is preliminary and confidential. Please do not quote its contents without the written permission of the author.     

Antecedents of organizational identification in multinational corporations: fostering psychological attachment to the local subsidiary and the global organization

This article reports the results of an empirical case study of the antecedents of organizational identification among local managers in a multinational corporation (MNC). Organizational identification, which refers to an individual's psychological attachment to the organization, has gained increasing attention because of its assumed link with behaviour associated with enhanced organizational performance. Yet little work has been done on what fosters organizational identification, particularly within the context of a MNC. Moreover, there is empirical evidence showing that managerial employees of MNCs draw a distinction between their local subsidiary and the global organization as manifest in separate group identifications. This suggests that there may be differential sets of antecedents of identification with the local subsidiary and with the global organization. The results of the present study indicate that there are different sets of factors that promote identification with the local and global levels of the organization. The results further show a complementarity of causal variables; identification with the local subsidiary is fostered primarily by factors which pertain to the local company context, while identification with the global organization is fostered primarily by factors which pertain to the MNC as a global entity.     

Multivariate choices and identification of social interactions

This paper considers the identification of social interaction effects in the context of multivariate choices. First, we generalize the theoretical social interaction model to allow individuals to make interdependent choices in different activities. Based on the theoretical model, we propose a simultaneous equation network model and discuss the identification of social interaction effects in the econometric model. We also provide an empirical example to show the empirical salience of this model. Using the Add Health data, we find that a student's academic performance is not only affected by academic performance of his peers but also affected by screen‐related activities of his peers.     

Land use dynamics and the environment

This paper builds a benchmark framework to study optimal land use, encompassing land use activities and environmental degradation. We focus on the spatial externalities of land use as drivers of spatial patterns: land is immobile by nature, but local actions affect the whole space since pollution flows across locations resulting in both local and global damages. We prove that the decision maker problem has a solution, and characterize the corresponding social optimum trajectories by means of the Pontryagin conditions. We also show that the existence and uniqueness of time-invariant solutions are not in general guaranteed. Finally, a global dynamic algorithm is proposed in order to illustrate the spatial-dynamic richness of the model. We find that our simple set-up already reproduces a great variety of spatial patterns related to the interaction between land use activities and the environment. In particular, abatement technology turns out to play a central role as pollution stabilizer, allowing the economy to reach a time-invariant equilibrium that can be spatially heterogeneous.     

Instrumental variable estimation of nonseparable models

There are many environments where knowledge of a structural relationship is required to answer questions of interest. Also, nonseparability of a structural disturbance is a key feature of many models. Here, we consider nonparametric identification and estimation of a model that is monotonic in a nonseparable scalar disturbance, which disturbance is independent of instruments. This model leads to conditional quantile restrictions. We give local identification conditions for the structural equations from those quantile restrictions. We find that a modified completeness condition is sufficient for local identification. We also consider estimation via a nonparametric minimum distance estimator. The estimator minimizes the sum of squares of predicted values from a nonparametric regression of the quantile residual on the instruments. We show consistency of this estimator.     

Transboundary pollution externalities: Think globally,act locally?

We analyze the implications of transboundary pollution externalities on environmental policymaking in a spatial setting, in which pollution diffuses across the global spatial economy independently of the specific location in which it is originally generated. This framework gives rise to a simple regional optimal pollution control problem allowing us to compare the global and local solutions in which, respectively, the transboundary externality is and is not taken into account in the determination of the optimal policy by individual local policymakers. We show that it is not obvious that transboundary externalities are a source of inefficiency per se since this is strictly related to the spatial features of the initial distribution of pollution. If the initial pollution distribution is spatially homogeneous then the local and global solutions will coincide and thus no efficiency loss will arise from transboundary externalities, but if it is spatially heterogeneous the local solution will be suboptimal and thus a global approach to environmental problems will be needed to achieve efficiency. From a normative perspective, in this latter (and most realistic) case we also quantify the amount of policy intervention needed at local level in order to achieve the globally desirable goal of pollution eradication in the long run. Our conclusions hold true in a number of different settings, including situations in which the spatial domain is either bounded or unbounded, and situations in which macroeconomic–environmental feedback effects are taken into account.     

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.

     

On illiquidity of an emerging sovereign bond market

This study offers an analysis of a sovereign bond market in an emerging country, Turkey, and its illiquidity. We employ the Nelson-Siegel model to generate a term structure for interest rates directly from daily bond price quotes in the Turkish market. We take the noise measure, which is the byproduct of term structure estimation, as a proxy for market-wide illiquidity. Our results show that this noise measure can capture the illiquidity in the Turkish fixed-income market from global financial turbulence as well as local dynamics. Inflation uncertainty and sentiment are the major macro drivers of liquidity crunches. It has also become clear that liquidity in an emerging market such as Turkey in the aftermath of the 2008 crisis has been driven by global forces, however, since 2013 local factors have taken over. This apparent decoupling in liquidity between a major emerging market and global markets followed the approaching end of quantitative easing and a rise in economic turbulence in the country since then.     

Identification and estimation of Gaussian affine term structure models

This paper develops new results for identification and estimation of Gaussian affine term structure models. We establish that three popular canonical representations are unidentified, and demonstrate how unidentified regions can complicate numerical optimization. A separate contribution of the paper is the proposal of minimum-chi-square estimation as an alternative to MLE. We show that, although it is asymptotically equivalent to MLE, it can be much easier to compute. In some cases, MCSE allows researchers to recognize with certainty whether a given estimate represents a global maximum of the likelihood function and makes feasible the computation of small-sample standard errors.     

Local and global indeterminacy in two-sector models of endogenous growth

We consider a two-sector endogenous growth model where the productions of the final good and human capital require economy-wide external effects. Assuming constant returns to scale at the private and social levels, we show that local and global indeterminacy of equilibrium paths are compatible with any values for the elasticity of intertemporal substitution in consumption and any sign for the capital intensity difference across the two sectors. We also show that for any value of the elasticity of intertemporal substitution in consumption, poverty traps may occur when the final good sector is capital intensive in human capital.     

Social interactions and the prophylaxis of SI epidemics on networks

We investigate the containment of epidemic spreading in networks from a normative point of view. We consider a susceptible/infected model in which agents can invest in order to reduce the contagiousness of network links. In this setting, we study the relationships between social efficiency, individual behaviours and network structure. First, we characterise individual and socially efficient behaviour using the notions of communicability and exponential centrality. Second we show, by computing the Price of Anarchy, that the level of inefficiency can scale up linearly with the number of agents. Third, we prove that policies of uniform reduction of interactions satisfy some optimality conditions in a vast range of networks. In setting where no central authority can enforce such stringent policies, we consider as a type of second-best policy the implementation of cooperation frameworks that allow agents to subsidise prophylactic investments in the global rather than in the local network. We then characterise the scope for Pareto improvement opened by such policies through a notion of Price of Autarky, measuring the ratio between social welfare at a global and a local equilibrium. Overall, our results show that individual behaviours can be extremely inefficient in the face of epidemic propagation but that policy can take advantage of the network structure to design welfare improving containment policies.     

Patterns of dominant flows in the world trade web

The large-scale organization of the world economies is exhibiting increasing levels of local heterogeneity and global interdependency. Understanding the relation between local and global features calls for analytical tools able to uncover the global emerging organization of the international trade network. Here we analyze the world network of bilateral trade imbalances and characterize its overall flux organization, unraveling local and global high-flux pathways that define the backbone of the trade system. We develop a general procedure capable to progressively filter out in a consistent and quantitative way the dominant trade channels. This procedure is completely general and can be applied to any weighted network to detect the underlying structure of transport flows. The trade fluxes properties of the world trade web determine a ranking of trade partnerships that highlights global interdependencies, providing information not accessible by simple local analysis. The present work provides new quantitative tools for a dynamical approach to the propagation of economic crises.     

Identification of simultaneous equation models with measurement error: a computerized evaluation

Rank conditions for identification in structural models are often difficult evaluate. Here we consider simultaneous equation models with measurement error and we show that previously published rank conditions for identification are not well-suited for evaluation. An alternative rank condition is derived and a computer algebra program is presented that takes care of both the construction and the computation of the rank of the relevant Jacobian matrix. It uses the parameter restrictions as input in order to characterize the identification situation of the individual parameters in the output.     

Global financial uncertainty

We estimate a novel measure of global financial uncertainty (GFU) with a dynamic factor framework that jointly models global, regional, and country-specific factors. We quantify the impact of GFU shocks on global output with a VAR analysis that achieves set identification via a combination of narrative, sign, ratio, and correlation restrictions. We find that the contraction in world output during the Great Recession would have been 13% milder in absence of GFU shocks. We also find support for a global finance uncertainty multiplier: the more global financial conditions deteriorate after a GFU shock, the larger the world output contraction is.     

Relational consumption and nonlinear dynamics in an overlapping generations model

In this paper, we show that incorporating the relational dimension into an otherwise standard OLG model and focusing on dynamic leisure externalities leads to dramatically different predictions. Here, we show that when the old perceive private and relational consumption as substitutable goods, a series of interesting dynamic outcomes—such as local indeterminacy, nonlinear phenomena (including chaotic dynamics) and even multiple equilibria with global indeterminacy—may arise. We also draw some welfare implications and relate them to the well-known “happiness paradox” arising within contemporary affluent societies.     

Regional integration,international liberalisation and the dynamics of industrial agglomeration

This paper presents a 3-region footloose-entrepreneur new economic geography model. Two symmetric regions are part of an economically integrated area (the Union), while the third region represents an outside trade partner. We explore how the spatial allocation of industrial production and employment within the Union is affected by changes in two aspects of trade liberalisation, regional integration and globalisation, conditional to the skill endowment and the market size of the outside region. Our main contribution pertains to the analysis of the local and global dynamics of the specified factor mobility process. We show that significant parameter ranges exist for which an asymmetric distribution of economic activities is one of the possible long-run outcomes which may allow a smooth transition to agglomeration (in contrast to the NEG typical catastrophic scenario). In addition, we show that multistability is pervasive and that some attractors are Milnor attractors. Both results reinforce the NEG narrative on the importance of initial conditions for the long-run location of industrial activity.     

Balancing global and local strategic contexts: Expatriate knowledge transfer,applications, and learning within a transnational organization

We investigate how expatriates contribute to the transnational firm's strategic objectives of global efficiency, national (”local”) responsiveness, and worldwide learning. We focus on expatriate knowledge application and experiential learning achievements, two assignment‐based outcomes of potential strategic value to the firm. We assess how the individual's everyday knowledge access and communication activities, measured by frequency and geographic extent, affect these assignment outcomes. Within our case organization, a prototype transnational firm, we find that expatriate knowledge applications result from frequent knowledge access and communication with the corporate headquarters and other global units of the firm. In contrast, their experiential learning derives from frequent access to hostcountry (local) knowledge that subsequently is adapted to the global corporate context. From a practical perspective, we conclude that experiential learning is an invaluable resource for both present and future corporate assignments. © 2007 Wiley Periodicals, Inc.     

A nonlinear simultaneous probit-model for the investigation of decision-making processes: modelling the process of setting up a family in partnerships

This article proposes an approach for modelling the interactive influences of two (or more) actors in decision-making processes. We use a nonlinear simultaneous probit-model and show how the problem of identification for estimating the relative effects of the actors can be solved. The formal model will be applied to examine the decision-making process for setting up a family in partnerships. We model a trivariate distribution consisting of the wifes’ desire to have a child or disposition toward the generative decision, the husbands’ disposition and the joint generative decision. We show how the parameter can be used to assess the relative importance of both partners’ dispositions for the decision, the reciprocal influence of both partners’ dispositions within the interaction process, and the relevance of both partners’ biographical contexts in relation to their own disposition as well as to that of the partner. The analysis is based on a three-stage estimation strategy which is implemented in MECOSA 3 and we use data from the Bamberger married couple panel.     

Identification of rational expectations models

This paper deals with the problem of the identification of simultaneous Rational Expectations (RE) models. In the case of RE models with current expectations of the endogenous variables, the necessary and sufficient conditions for the global identification are derived explicitly in terms of the structural parameters and the linear homogenous identifying restrictions. It is shown that in the absence of a priori restrictions on the processes generating the exogenous variables and the disturbances, RE models and general distributed lag models are ‘observationally equivalent’. In the case of RE models with future expectations of the endogenous variables, a general solution that highlights the ‘non-uniqueness’ problem and from which other solutions such as forward or backward solutions can be obtained, is derived. It is shown that untestable and often quite arbitrary restrictions are needed if RE models with future expectations are to be identifiable. Certain order conditions similar to those obtained for the identification of RE models with current expectations are also derived for this case.     

A neural network unification

We study the relation between the asymptotic behaviour of synchronous Boltzmann machines and synchronous Hopfield networks. More specifically, we consider the relation between the pseudo consensus function that is used in analyzing Boltzmann machines and the energy function that is used in the study of Hopfield networks. We show that for small values of the control parameter, synchronous Boltzmann machines and synchronous Hopfield networks compute global respectively local maxima of the same function.