Panel Data Discrete Choice Models with Lagged Dependent Variables

In this paper, we consider identification and estimation in panel data discrete choice models when the explanatory variable set includes strictly exogenous variables, lags of the endogenous dependent variable as well as unobservable individual‐specific effects. For the binary logit model with the dependent variable lagged only once, Chamberlain (1993) gave conditions under which the model is not identified. We present a stronger set of conditions under which the parameters of the model are identified. The identification result suggests estimators of the model, and we show that these are consistent and asymptotically normal, although their rate of convergence is slower than the inverse of the square root of the sample size. We also consider identification in the semiparametric case where the logit assumption is relaxed. We propose an estimator in the spirit of the conditional maximum score estimator (Manski (1987)) and we show that it is consistent. In addition, we discuss an extension of the identification result to multinomial discrete choice models, and to the case where the dependent variable is lagged twice. Finally, we present some Monte Carlo evidence on the small sample performance of the proposed estimators for the binary response model.     

The Model Confidence Set

This paper introduces the model confidence set (MCS) and applies it to the selection of models. A MCS is a set of models that is constructed such that it will contain the best model with a given level of confidence. The MCS is in this sense analogous to a confidence interval for a parameter. The MCS acknowledges the limitations of the data, such that uninformative data yield a MCS with many models, whereas informative data yield a MCS with only a few models. The MCS procedure does not assume that a particular model is the true model; in fact, the MCS procedure can be used to compare more general objects, beyond the comparison of models. We apply the MCS procedure to two empirical problems. First, we revisit the inflation forecasting problem posed by Stock and Watson (1999), and compute the MCS for their set of inflation forecasts. Second, we compare a number of Taylor rule regressions and determine the MCS of the best regression in terms of in‐sample likelihood criteria.     

Testing the Time‐Variancy of Explanatory Factors of Strategic Change1

The article proposes an empirical framework able to: (1) assess the relative validity of both adaptive and inertial views of strategic change and (2) verify the potential time‐ or context‐dependency by testing the structural stability of the empirical model, in Spanish banks, 1983–1997. Results offer inconclusive findings regarding (1) but strong evidence to answer (2). The assumption of structural stability is rejected and the effect of many explanatory factors considered in the empirical model varies over time as some factors show different effects and/or significance levels depending on the period considered. These findings suggest that explanatory models of strategic change should be viewed as ‘time‐’ or ‘context‐dependent’. The article provides a conceptual model in which alternative explanations operate in a sequential way. The results highlight, first, that inconclusive past findings about adaptive versus inertial views should be reviewed under this new evidence, and future empirical research must assure that its methods and interpretations are robust to potential structural breakdowns; and second, the limitations raised by the static approach offered by the available theories/models when approaching the dynamic and complex nature of strategic change. Theoretical developments and implications for managerial practice are suggested.     

Cross Section and Panel Data Estimators for Nonseparable Models with Endogenous Regressors

We propose two new methods for estimating models with nonseparable errors and endogenous regressors. The first method estimates a local average response. One estimates the response of the conditional mean of the dependent variable to a change in the explanatory variable while conditioning on an external variable and then undoes the conditioning. The second method estimates the nonseparable function and the joint distribution of the observable and unobservable explanatory variables. An external variable is used to impose an equality restriction, at two points of support, on the conditional distribution of the unobservable random term given the regressor and the external variable. Our methods apply to cross sections, but our lead examples involve panel data cases in which the choice of the external variable is guided by the assumption that the distribution of the unobservable variables is exchangeable in the values of the endogenous variable for members of a group.     

A comparison of approaches to exploit budget allocation data in cross-sectional maximum likelihood estimation of multi-attribute choice models

In this paper, four calibration approaches to exploit budget allocation data in maximum likelihood estimation of multi-attribute choice models are proposed. They differ on the implicit meaning of the dependent variable: (A) share of consumers according to the preferred alternative; (B) share of sales; (C) average share of consumer's budget; and (D) share of sales according to the preferred alternative. Differences between them can be conceived as depending on two circumstances: customer loyalty and customer selectivity. These are tested in the context of spatial consumer behavior, market response to hypermarket chains being represented as a function of their location strategies. Results show that different nuances on the definition of the dependent variable lead to slightly different relationships with the explanatory variables and to different predictive capabilities.     

Cross-national distance as an explanatory variable in international management: Fundamental challenge and solution

Although recent work provides insightful theoretical and practical suggestions for improving contextual distance research in international management, the fundamental problem with using distance indicators as explanatory variables remains too little recognized and largely unaddressed. This problem is that cross-national distance metrics partially identify host and/or home countries in one's sample, what I term location-identification. Location-identification can occur irrespective of the number of home/host countries considered and means that a distance indicator partly captures country fixed effects when used as an independent or explanatory variable. As a result, in empirical distance research, genuine distance effects are often confounded with country-specific measurement error in the dependent variable as well as with direct effects due to various home- or host-country features. I present empirical evidence on the pervasiveness of this critical challenge to cross-national distance research and propose a practical and effective solution for addressing it, which is to use “pure” distance indicators that are cleansed from confounding home- and host-location influences.     

Recursive Equilibria in Dynamic Economies With Stochastic Production

In this paper, we prove the existence of recursive equilibria in a dynamic stochastic model with infinitely lived heterogeneous agents, several commodities, and general inter‐ and intratemporal production. We illustrate the usefulness of our result by providing sufficient conditions for the existence of recursive equilibria in heterogeneous agent versions of both the Lucas asset pricing model and the neoclassical stochastic growth model.     

A Test of Exogeneity Without Instrumental Variables in Models With Bunching

This paper presents a test of the exogeneity of a single explanatory variable in a multivariate model. It does not require the exogeneity of the other regressors or the existence of instrumental variables. The fundamental maintained assumption is that the model must be continuous in the explanatory variable of interest. This test has power when unobservable confounders are discontinuous with respect to the explanatory variable of interest, and it is particularly suitable for applications in which that variable has bunching points. An application of the test to the problem of estimating the effects of maternal smoking in birth weight shows evidence of remaining endogeneity, even after controlling for the most complete covariate specification in the literature.     

WHY ARE THE 2000s SO DIFFERENT FROM THE 1970s? A STRUCTURAL INTERPRETATION OF CHANGES IN THE MACROECONOMIC EFFECTS OF OIL PRICES

In the 1970s, large increases in the price of oil were associated with sharp decreases in output and large increases in inflation. In the 2000s, even larger increases in the price of oil were associated with much milder movements in output and inflation. Using a structural VAR approach, Blanchard and Gali (in J. Gali and M. Gertler (eds.) 2009, International Dimensions of Monetary Policy, University of Chicago Press, pp. 373–428) argued that this reflected a change in the causal relation from the price of oil to output and inflation. They then argued that this change could be due to a combination of three factors: a smaller share of oil in production and consumption, lower real wage rigidity, and better monetary policy. Their argument, based on simulations of a simple new‐Keynesian model, was informal. Our purpose in this paper is to take the next step, and to estimate the explanatory power and contribution of each of these factors. To do so, we use a minimum distance estimator that minimizes, over the set of structural parameters and for each of two samples (pre‐ and post‐1984), the distance between the empirical SVAR‐based impulse response functions and those implied by a new‐Keynesian model. Our empirical results point to an important role for all three factors.     

Bayesian Estimation of Dynamic Discrete Choice Models

We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the dynamic programming (DP) solution algorithm with the Bayesian Markov chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the parameters simultaneously. As a result, the computational burden of estimating a dynamic model becomes comparable to that of a static model. Another feature of our algorithm is that even though the number of grid points on the state variable is small per solution‐estimation iteration, the number of effective grid points increases with the number of estimation iterations. This is how we help ease the “curse of dimensionality.” We simulate and estimate several versions of a simple model of entry and exit to illustrate our methodology. We also prove that under standard conditions, the parameters converge in probability to the true posterior distribution, regardless of the starting values.     

Toward an Understanding of the Behavioral Intention to Use an Information System

Organizational investment in information systems is often large and risky given the variety of information requirements placed on systems today. To make more informed decisions and to meet the challenge of developing systems that satisfy these demands, system developers need to achieve a better understanding of factors that ultimately lead to system usage. To enhance this understanding, we posit a holistic framework to examine several constructs suggested in the literature that lead to the behavioral intention to use an information system. Our framework includes situational involvement, intrinsic involvement, argument for change, perceived usefulness, ease of use, prior usage, and attitude constructs. We extend the Davis, Bagozzi, and Warshaw Technology Acceptance Model (TAM), which is founded on the Theory of Reasoned Action. A diverse sample from industry is used to test our model. Structural equation modeling is used to examine the entire pattern of intercorrelations among the constructs and to test related propositions. A hierarchical structure is used to compare the explanatory ability of TAM with our extension. Our model explains a large portion of the covariance among the constructs that lead to a user's behavioral intention to use an information system and compares favorably with TAM. The results indicate that (1) the direct effect of situational involvement on behavioral intention as well as attitude is significant in the negative direction, (2) attitude seems to play a mediating role, and (3) intrinsic involvement plays a significant role in shaping perceptions. Finally, we conclude that the user involvement construct needs to be separated into its psychological as well as its participative components for developers to understand its impact on the systems development process.     

Technological alliance portfolio configuration and firm performance

Firms pursuing technological alliances to gain competitive advantages have become a ubiquitous phenomenon in today’s business environment. This article examines which technological alliance portfolio configuration is better for focal firm performance using a portfolio rather than a dyadic perspective. To assess technological alliance portfolio effects on Korean pharmaceutical and biotechnology firms, we adopted three explanatory variables—number of alliances, number of partners, and spanning structural holes. The growth rate of revenue and the growth rate of profit are used as dependent variables. We identify two characteristics of technological alliance portfolios from the two-step generalized method of moments estimates. First, we find that between two firms with the same number of alliances, the firm with the larger number of partners would have a better performance. This result is unlike those in previous studies because it distinguishes between the number of alliances and number of partners based on the network theory. Second, we find that spanning structural holes affects firm performance rather like a double-edge sword—it positively affects the growth rate of profit but negatively affects the growth rate of revenue of firms. In short, spanning structural holes is simultaneously beneficial for firm profitability and unfavorable for firm growth. This result differs from those of earlier studies because it shows that a firm spanning structural holes among alliance partners produces either a positive or a negative effect, suggesting that a firm should vary its strategy depending on whether it prioritizes profitability or growth.     

Reputation in Continuous‐Time Games

We study reputation dynamics in continuous‐time games in which a large player (e.g., government) faces a population of small players (e.g., households) and the large player's actions are imperfectly observable. The major part of our analysis examines the case in which public signals about the large player's actions are distorted by a Brownian motion and the large player is either a normal type, who plays strategically, or a behavioral type, who is committed to playing a stationary strategy. We obtain a clean characterization of sequential equilibria using ordinary differential equations and identify general conditions for the sequential equilibrium to be unique and Markovian in the small players' posterior belief. We find that a rich equilibrium dynamics arises when the small players assign positive prior probability to the behavioral type. By contrast, when it is common knowledge that the large player is the normal type, every public equilibrium of the continuous‐time game is payoff‐equivalent to one in which a static Nash equilibrium is played after every history. Finally, we examine variations of the model with Poisson signals and multiple behavioral types.     

Markov Perfect Industry Dynamics With Many Firms

We propose an approximation method for analyzing Ericson and Pakes (1995)‐style dynamic models of imperfect competition. We define a new equilibrium concept that we call oblivious equilibrium, in which each firm is assumed to make decisions based only on its own state and knowledge of the long‐run average industry state, but where firms ignore current information about competitors' states. The great advantage of oblivious equilibria is that they are much easier to compute than are Markov perfect equilibria. Moreover, we show that, as the market becomes large, if the equilibrium distribution of firm states obeys a certain “light‐tail” condition, then oblivious equilibria closely approximate Markov perfect equilibria. This theorem justifies using oblivious equilibria to analyze Markov perfect industry dynamics in Ericson and Pakes (1995)‐style models with many firms.     

Changing a Leopard's Spots: A New Research Direction for Organizational Culture in the Operations Management Field

Operations Management (OM) research on organizational culture has to change to be able to inform practice. Currently, organizational culture research in OM is largely confined to narrow topical and methodological niches and culture is most frequently used as an explanatory variable in quantitative, survey‐based research. We argue that the relegation of culture to this niche is due to self‐imposed methodological blinders that hobble the OM field. We then present four research imperatives to reinvigorate organizational culture research within our field. We urge OM scholars to view culture as a dynamic concept that can be influenced, to adopt alternative methods, to use non‐traditional data sources, and to rethink assumptions about dependent variables. We also identify gaps in the current knowledge and new research questions for the OM domain. We conclude that the field of OM could greatly expand its understanding of organizational culture and in so doing greatly improve business practice, but that to do so will require a change in the culture of the operations management research community.     

Uniqueness of equilibria in atomic splittable polymatroid congestion games

We study uniqueness of Nash equilibria in atomic splittable congestion games and derive a uniqueness result based on polymatroid theory: when the strategy space of every player is a bidirectional flow polymatroid, then equilibria are unique. Bidirectional flow polymatroids are introduced as a subclass of polymatroids possessing certain exchange properties. We show that important cases such as base orderable matroids can be recovered as a special case of bidirectional flow polymatroids. On the other hand we show that matroidal set systems are in some sense necessary to guarantee uniqueness of equilibria: for every atomic splittable congestion game with at least three players and non-matroidal set systems per player, there is an isomorphic game having multiple equilibria. Our results leave a gap between base orderable matroids and general matroids for which we do not know whether equilibria are unique.     

Transformational leadership and optimal functioning at work: On the mediating role of employees' perceived job characteristics and motivation

This study aimed to deepen our understanding of the motivational mechanisms involved in the relationship between transformational leadership (TFL) and employee functioning. Drawing on the TFL literature, the job demands–resources model and self-determination theory, we propose an integrative model that relates TFL to employee psychological health (burnout and psychological distress), attitudes (occupational commitment and turnover intention) and performance (professional efficacy, self-reported individual and objective organizational performance) through two explanatory mechanisms: perceived job characteristics (job demands and resources) and employee motivation (autonomous and controlled). This research was conducted in two occupational settings (nurses and school principals), using a distinct variable operationalization for each. Results of both studies provide support for the hypothesized model, suggesting that TFL relates to optimal job functioning (psychological health, job attitudes and performance) by contributing to favourable perceptions of job characteristics (more resources and less demands) and high-quality work motivation (more autonomous motivation and less controlled motivation) in employees. Theoretical contributions and managerial implications as well as directions for future research are presented.     

A Comprehensive Profile of the Working Poor

Abstract. In this paper, a comprehensive profile of the ‘working poor’ is presented using data from the 2003 March Supplement of the Current Population Survey. We test an earnings model with effective cost constraints. The dependent variable is the likelihood of an employed individual being a member of the working poor. The explanatory variables are the worker's occupation and firm characteristics, and cost constraints comprising, on the one hand, the worker's family characteristics (notably family income), and, on the other, the costs to the worker of signals used by firms in making employment decisions. These include not only the cost of education but also what we call ‘discriminatory signals’, e.g. gender, race, ethnicity and citizenship status. The paper provides new insight into the complex set of relationships between the signaling variables themselves, between signals and occupations, and between industries and occupations, in the formation of relative wage rates.     

非奇次空间动态极值估计的模型与应用

传统EVT方法是从静态的角度,研究超额数据的性质。然而,它没有同时考虑极端数据发生的时间所隐含的充分信息。本文首次在国内提出了非奇次空间动态极值理论(ITD-EVT)的概念,克服了EVT的上述缺陷,在极端数据的基础上考虑了时间因素,并引入多个解释变量,使极值分布的是三个参数为时变的,用二维泊松分布过程建立动态空间模型,是文中一大特色。把TD-EVT运用于极端情况下风险值的估计中,对金融风险管理、资产定价等问题有较大的理论和现实意义。     

Unsold Versus Unbought Commitment: Minimum Total Commitment Contracts with Nonzero Setup Costs

We study a minimum total commitment (MTC) contract embedded in a finite‐horizon periodic‐review inventory system. Under this contract, the buyer commits to purchase a minimum quantity of a single product from the supplier over the entire planning horizon. We consider nonstationary demand and per‐unit cost, discount factor, and nonzero setup cost. Because the formulations used in existing literature are unable to handle our setting, we develop a new formulation based on a state transformation technique using unsold commitment instead of unbought commitment as state variable. We first revisit the zero setup cost case and show that the optimal ordering policy is an unsold‐commitment‐dependent base‐stock policy. We also provide a simpler proof of the optimality of the dual base‐stock policy. We then study the nonzero setup cost case and prove a new result, that the optimal solution is an unsold‐commitment‐dependent (s, S) policy. We further propose two heuristic policies, which numerical tests show to perform very well. We also discuss two extensions to show the generality of our method's effectiveness. Finally, we use our results to examine the effect of different contract terms such as duration, lead time, and commitment on buyer's cost. We also compare total supply chain profits under periodic commitment, MTC, and no commitment.