Learning trajectory in offshore OEM cooperation: Transaction value for local suppliers in the emerging economies

We focus on the unique role of offshore OEM cooperation as a learning opportunity for local suppliers in the emerging economies. In particular, we conceptually argue and empirically confirm the links between learning intent, capability enhancement, governance mode, and overall innovativeness from the perspective of local suppliers in offshore OEM cooperation in China as well as the influence of local business culture within these links. We address two issues: (1) offshore OEM cooperation can be a unique opportunity for local suppliers with strong intent to learn from foreign buyers for capability enhancement and (2) the moderating effects of trust and contact as two distinctive governance modes. Addressing the two issues, we make two specific contributions to the research on inter-firm cooperation, both of which derive from the often-neglected theoretical lens of transaction value. Our general contribution lies in the extension of the transaction value perspective by specifying its dual tenets of inter-firm co-specialization and shared-trust as the central theme of inter-firm cooperation to complement the transaction cost economics and also contextualize the research on inter-firm cooperation in terms of different partners’ perspectives as well as different governance modes.     

How much nominal rigidity is there in the US economy? Testing a new Keynesian DSGE model using indirect inference

We evaluate the Smets-Wouters New Keynesian model of the US postwar period, using indirect inference, the bootstrap and a VAR representation of the data. We find that the model is strongly rejected. While an alternative (New Classical) version of the model fares no better, adding limited nominal rigidity to it produces a ‘weighted’ model version closest to the data. But on data from 1984 onwards - the ‘great moderation’ - the best model version is one with a high degree of nominal rigidity, close to New Keynesian. Our results are robust to a variety of methodological and numerical issues.     

The impact of power and relationship commitment on the integration between manufacturers and customers in a supply chain

Supply chain integration (SCI) has received increasing attention from scholars and practitioners in recent years. However, our knowledge of what influences SCI is still very limited. Although marketing and management researchers have investigated power and relationship commitment issues between organizations, few have examined their impact on SCI. This paper extends the power–relationship commitment theory established in Western marketing literature and links it with SCI in China, through examining the relationship between power, relationship commitment and the integration between manufacturers and their customers. We propose and empirically test a model using data collected from 617 manufacturing companies in China. The results show that different types of customer power impact manufacturers’ relationship commitment in different ways. Expert power, referent power and reward power are important in improving manufacturers’ normative relationship commitment, while reward power and coercive power enhance instrumental relationship commitment. We also found that normative relationship commitment had a greater impact on customer integration than instrumental relationship commitment. These findings are interpreted in light of national culture differences between China and the U.S. in terms of power distance and collectivism, which provide a new perspective on SCI.     

Forecasting national activity using lots of international predictors: An application to New Zealand

We assess the marginal predictive content of a large international dataset for forecasting GDP in New Zealand, an archetypal small open economy. We apply “data-rich” factor and shrinkage methods to efficiently handle hundreds of predictor series from many countries. The methods covered are principal components, targeted predictors, weighted principal components, partial least squares, elastic net and ridge regression. We find that exploiting a large international dataset can improve forecasts relative to data-rich approaches based on a large national dataset only, and also relative to more traditional approaches based on small datasets. This is in spite of New Zealand’s business and consumer confidence and expectations data capturing a substantial proportion of the predictive information in the international data. The largest forecasting accuracy gains from including international predictors are at longer forecast horizons. The forecasting performance achievable with the data-rich methods differs widely, with shrinkage methods and partial least squares performing best in handling the international data.     

Trade-offs in make-buy decisions

While the previous literature uses transaction cost economics and the resource-based view to theoretically derive the benefits of outsourcing, it has so far overlooked how these theoretical underpinnings must be used to assess trade-offs involved in make-buy decisions as well. This study investigates trade-offs in make-buy decisions for the buying firm. It takes an operations strategy perspective and links manufacturing firms’ competitive priorities to outsourcing motives and resulting capabilities. Survey data from a representative sample of 136 manufacturing plants in Sweden is subjected to regressions analysis. In contrast to earlier empirical research, this study shows that resulting capabilities of strategic outsourcing initiatives are distinct, and, furthermore, do not emerge cumulatively. This has important implications for the ongoing debate over trade-offs in the operations strategy literature. Findings are clearly in support of the trade-off model and extend current research into the theoretical domain of make-buy decisions.     

Buyer–seller networks with demand shocks and intermediation

We present a model in which buyers and sellers use links to trade with each other. Each seller produces a good which can be one of two types. Buyers are ex ante identical but receive specification or valuation shocks after the links are formed. We show that efficient networks are stable and that severing a link in an efficient network results in a higher price for the buyer but a lower price for the seller. We also examine network intermediation when sellers (buyers) form links sequentially. When sellers form links sequentially, the first seller becomes an intermediary and shares links with other sellers; this makes all sellers better off. However, when buyers form links sequentially, buyers may or may not share links. If links are shared multiple intermediaries result.     

A nonlinear panel data model of cross-sectional dependence

This paper proposes a nonlinear panel data model which can endogenously generate both ‘weak’ and ‘strong’ cross-sectional dependence. The model’s distinguishing characteristic is that a given agent’s behaviour is influenced by an aggregation of the views or actions of those around them. The model allows for considerable flexibility in terms of the genesis of this herding or clustering type behaviour. At an econometric level, the model is shown to nest various extant dynamic panel data models. These include panel AR models, spatial models, which accommodate weak dependence only, and panel models where cross-sectional averages or factors exogenously generate strong, but not weak, cross sectional dependence. An important implication is that the appropriate model for the aggregate series becomes intrinsically nonlinear, due to the clustering behaviour, and thus requires the disaggregates to be simultaneously considered with the aggregate. We provide the associated asymptotic theory for estimation and inference. This is supplemented with Monte Carlo studies and two empirical applications which indicate the utility of our proposed model as a vehicle to model different types of cross-sectional dependence.     

Network formation models with costs for establishing links

Abstract. In this paper we study endogenous formation of communication networks in situations where the economic possibilities of groups of players can be described by a cooperative game. We concentrate on the influence that the existence of costs for establishing communication links has on the communication networks that are formed. The starting points in this paper are two game-theoretic models of the formation of communication links that were studied in the literature fairly recently, the extensive-form model by Aumann and Myerson (1988) and the strategic-form model that was studied by Dutta et al. (1998). We follow their analyses as closely as possible and use an extension of the Myerson value to determine the payoffs to the players in communication situations when forming links is not costless. We find that it is possible that as the costs of establishing links increase, more links are formed.     

Sequential control of time series by functionals of kernal-weighted empirical processes under local alternatives

Motivated in part by applications in model selection in statistical genetics and sequential monitoring of financial data, we study an empirical process framework for a class of stopping rules which rely on kernel-weighted averages of past data. We are interested in the asymptotic distribution for time series data and an analysis of the joint influence of the smoothing policy and the alternative defining the deviation from the null model (in-control state). We employ a certain type of local alternative which provides meaningful insights. Our results hold true for short memory processes which satisfy a weak mixing condition. By relying on an empirical process framework we obtain both asymptotic laws for the classical fixed sample design and the sequential monitoring design. As a by-product we establish the asymptotic distribution of the Nadaraya-Watson kernel smoother when the regressors do not get dense as the sample size increases.Acknowledgements The author is grateful to two anonymous referees for their constructive comments, which improved the paper. One referee draws my attention to Lifshits paper. The financial support of the Collaborative Research Centre Reduction of Complexity in Multivariate Data Structures (SFB 475) of the German Research Foundation (DFG) is greatly acknowledged.     

Demand and supply estimation biases due to omission of durability

We build a dynamic equilibrium model of a durable goods oligopoly with a competitive secondary market to evaluate the bias in estimating the structural parameters of demand and supply when durability is omitted. We simulate data from our dynamic model and use them to estimate the model’s static counterpart. We find that the static estimate of the elasticity of demand is an overestimate of the true elasticity and that the static estimate of the markup is an underestimate. Our results provide a benchmark on the magnitude and sign of the bias when static models are used for economic inference.     

Risk behavior in the presence of government programs

Our paper assesses the impacts of the 1996 US Farm Bill on production decisions. We apply the expected utility model to analyze farmers’ behavior under risk and assess how farmers’ production decisions change in the presence of government programs. Specifically, we empirically evaluate the relative price and the risk-related effects of farm policy changes at the intensive margin of production, as well as the extra value that these policies add to farmers’ certainty equivalent. We use farm-level data collected in Kansas to estimate the model. We find evidence that decoupled government programs have only negligible impacts on production decisions.     

Nonparametric inference based on conditional moment inequalities

This paper develops methods of inference for nonparametric and semiparametric parameters defined by conditional moment inequalities and/or equalities. The parameters need not be identified. Confidence sets and tests are introduced. The correct uniform asymptotic size of these procedures is established. The false coverage probabilities and power of the CS’s and tests are established for fixed alternatives and some local alternatives. Finite-sample simulation results are given for a nonparametric conditional quantile model with censoring and a nonparametric conditional treatment effect model. The recommended CS/test uses a Cramér–von-Mises-type test statistic and employs a generalized moment selection critical value.     

The construction of empirical credit scoring rules based on maximization principles

We examine the econometric implications of the decision problem faced by a profit/utility-maximizing lender operating in a simple “double-binary” environment, where the two actions available are “approve” or “reject”, and the two states of the world are “pay back” or “default”. In practice, such decisions are often made by applying a fixed cutoff to the maximum likelihood estimate of a parametric model of the default probability. Following (Elliott and Lieli, 2007), we argue that this practice might contradict the lender’s economic objective and, using German loan data, we illustrate the use of “context-specific” cutoffs and an estimation method derived directly from the lender’s problem. We also provide a brief discussion of how to incorporate legal constraints, such as the prohibition of disparate treatment of potential borrowers, into the lender’s problem.     

A Bradley-Terry type model for forecasting tennis match results

The paper introduces a model for forecasting match results for the top tier of men’s professional tennis, the ATP tour. Employing a Bradley-Terry type model, and utilising the data available on players’ past results and the surface of the contest, we predict match winners for the coming week’s matches, having updated the model parameters to take the previous week’s results into account. We compare the model to two logit models: one using official rankings and another using the official ranking points of the two competing players. Our model provides superior forecasts according to each of five criteria measuring the predictive performance, two of which relate to betting returns.     

Robust firm pricing with panel data

Firms often have imperfect information about demand for their products. We develop an integrated econometric and theoretical framework to model firm demand assessment and subsequent pricing decisions with limited information. We introduce a panel data discrete choice model whose realistic assumptions about consumer behavior deliver partially identified preferences and thus generate ambiguity in the firm pricing problem. We use the minimax-regret criterion as a decision-making rule for firms facing this ambiguity. We illustrate the framework’s benefits relative to the most common discrete choice analysis approach through simulations and empirical examples with field data.     

On the robustness of location estimators in models of firm growth under heavy-tailedness

Focusing on the model of demand-driven innovation and spatial competition over time in Jovanovic and Rob (1987), we study the effects of the robustness of estimators employed by firms to make inferences about their markets on the firms’ growth patterns. We show that if consumers’ signals in the model are moderately heavy-tailed and the firms use the sample mean of the signals to estimate the ideal product, then the firms’ output levels exhibit positive persistence. In such a setting, large firms have an advantage over their smaller counterparts. These properties are reversed for signals with extremely heavy-tailed distributions. In such a case, the model implies anti-persistence in output levels, together with a surprising pattern of oscillations in firm sizes, with smaller firms being likely to become larger ones next period, and vice versa. We further show that the implications of the model under moderate heavy-tailedness continue to hold under the only assumption of symmetry of consumers’ signals if the firms use a more robust estimator of the ideal product, the sample median.     

Making unilateral trade liberalisation beneficial to the poor

In this paper, we investigate the impacts of unilateral import liberalisation by a representative South Asian developing economy, Nepal, and demonstrate those conditions required to make the impacts ‘pro-poor.’ Applying the Computable General Equilibrium model to Social Accounting Matrix data, we conclude that import liberalisation is growth-enhancing but that, unfortunately, the rich benefit more than do the poor. We envisage a restructured but plausible model economy that requires a transformational period of ten years, and simulate unilateral trade liberalisation but, in the context of a dynamic model. We conclude that improvement in efficiency parameters, reorganisation of investment patterns, along with reallocation of factors of production by both household group and activity type are required to make growth accrued by import liberalisation ‘pro-poor’ in developing economies such as that of Nepal.     

Modelling and forecasting government bond spreads in the euro area: A GVAR model

This paper proposes an extension to Global Vector Autoregressive (GVAR) models to capture time-varying interdependence among financial variables. Government bond spreads in the euro area feature a time-varying pattern of co-movement that poses a serious challenge for econometric modelling and forecasting. This pattern of the data is not captured by the standard specification that model spreads as persistent processes reverting to a time-varying mean determined by two factors: a local factor, driven by fiscal fundamentals and growth, and a global world factor, driven by the market’s appetite for risk. This paper argues that a third factor, expectations of exchange rate devaluation, gained traction during the crises. This factor is well captured via a GVAR that models the interdependence among spreads by making each country’s spread function of global European spreads. Global spreads capture the exposure of each country’s spread to other spreads in the euro area in terms of the time-varying ‘distance’ between their fiscal fundamentals. This new specification dominates the standard one in modelling the time-varying pattern of co-movements among spreads and the response of euro area spreads to the Greek debt crisis.     

The econometrics of auctions with asymmetric anonymous bidders

We consider standard auction models when bidders’ identities are not-or are only partially-observed by the econometrician. We first adapt the definition of identifiability to a framework with anonymous bids and explore the extent to which anonymity reduces the possibility of identifying private value auction models. Second, in the asymmetric independent private value model which is nonparametrically identified, we generalize Guerre, Perrigne and Vuong’s estimation procedure [Optimal nonparametric estimation of first-price auctions, Econometrica 68 (2000) 525-574] and consider the asymptotic properties of our multi-step kernel-based estimator. Monte Carlo simulations illustrate the practical relevance of our estimation procedure in small data sets.     

Improving footprint calculations of small open economies: combining local with multi-regional input–output tables

In a small, open and resource-poor economy, import and export dependency have an ever-growing impact on local policy decisions, which makes local (environmental) policy-makers increasingly depend on global data. This increases the interest in models that link local production and consumption data to global production, trade and environmental data. The recent increase in availability of global environmentally extended multi-regional input-output tables (EE-MRIO tables) provides an opportunity to link them with existing local environmentally extended input-output tables (EE-RIO tables). These combined tables make it possible (1) to analyse the links between local and global production and consumption and (2) to study global value chains, material use and environmental impacts simultaneously. However, estimations using input-output (I–O) analyses contain errors due to imperfect databases. In this article the magnitude of specification, aggregation and time errors are estimated and compared. The results show the need to combine local datasets with multi-regional ones and show that highest detailed (country and sector levels) as well as time series of I–O tables are the way forward for using I–O analyses in local policy-making. The paper provides guidance on trading off investments in model adoption and/or extension and the reliability of estimation results.