Lock-in and unobserved preferences in server operating systems: A case of Linux vs. Windows

This paper investigates to what extent the persistence of Microsoft Windows in the market for server operating systems is due to lock-in or unobserved preferences. While the hypothesis of lock-in plays an important role in the antitrust policy debate for the operating systems market, it has not been extensively documented empirically. To account for unobserved preferences, we use a panel data identification approach based on time-variant group fixed effects, and estimate the dynamic discrete choice panel data model developed by Arellano and Carrasco (2003). Using detailed establishment-level data, we find that once we account for unobserved preferences, the estimated magnitudes of lock-in are considerably smaller than those from the conventional approaches, suggesting that unobserved preferences play a major role in the persistence of Windows. Further robustness checks are consistent with our findings.     

Estimating and Forecasting with a Dynamic Spatial Panel Data Model*

This study focuses on the estimation and predictive performance of several estimators for the dynamic and autoregressive spatial lag panel data model with spatially correlated disturbances. In the spirit of Arellano and Bond (1991) and Mutl (2006) , a dynamic spatial generalized method of moments (GMM) estimator is proposed based on Kapoor, Kelejian and Prucha (2007) for the spatial autoregressive (SAR) error model. The main idea is to mix non‐spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a linear predictor of this spatial dynamic model is derived. Using Monte Carlo simulations, we compare the performance of the GMM spatial estimator to that of spatial and non‐spatial estimators and illustrate our approach with an application to new economic geography.     

Some properties of the LIML estimator in a dynamic panel structural equation

We investigate the finite sample and asymptotic properties of the within-groups (WG), the random-effects quasi-maximum likelihood (RQML), the generalized method of moment (GMM) and the limited information maximum likelihood (LIML) estimators for a panel autoregressive structural equation model with random effects when both T (time-dimension) and N (cross-section dimension) are large. When we use the forward-filtering due to Alvarez and Arellano (2003), the WG, the RQML and GMM estimators are significantly biased when both T and N are large while T/N is different from zero. The LIML estimator gives desirable asymptotic properties when T/N converges to a constant.     

Econometric analysis of copyrights

This paper explores the impact of copyrights on firm value and on the demand for firm output. Using panel data on franchise value and ticket sales from the National Football League over the 1991–2000 period, we analyze the effect of copyrights (in this case, team logos) using several parametric estimators, the Arellano and Bond [1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297] dynamic panel data estimator, and a semi-non-parametric method based on difference-in-differences propensity score matching. We find a negative effect of logo changes on franchise value that is robust across multiple specifications. In addition, logo changes also appear to have a moderate positive, albeit not particularly robust, impact on ticket sales.     

Estimating the probability of leaving unemployment using uncompleted spells from repeated cross-section data

We propose a new econometric estimation method for analyzing the probability of leaving unemployment using uncompleted spells from repeated cross-section data, which can be especially useful when panel data are not available. The proposed method-of-moments-based estimator has two important features: (1) it estimates the exit probability at the individual level and (2) it does not rely on the stationarity assumption of the inflow composition. We illustrate and gauge the performance of the proposed estimator using Spanish Labor Force Survey data, and analyze the changes in distribution of unemployment between the 1980s and 1990s during a period of labor market reform. We find that the relative probability of leaving unemployment for the short-term unemployed compared to the long-term unemployed becomes significantly higher in the 1990s.     

Aggregation of heterogeneous beliefs

This paper is a generalization of [Calvet, L., Grandmont, J.-M., Lemaire, I., 2002. Aggregation of heterogenous beliefs and asset pricing in complete financial markets. Working paper] to a dynamic setting. We propose a method to aggregate heterogeneous individual probability beliefs, in dynamic and complete asset markets, into a single consensus probability belief. This consensus probability belief, if commonly shared by all investors, generates the same equilibrium prices as well as the same individual marginal valuation as in the original heterogeneous probability beliefs setting. As in [Calvet, L., Grandmont, J.-M., Lemaire, I., 2002. Aggregation of heterogenous beliefs and asset pricing in complete financial markets. Working paper], the construction stands on a fictitious adjustment of the market portfolio. The adjustment process reflects the aggregation bias due to the diversity of beliefs. In this setting, the construction of a representative agent is shown to be also valid.     

GDP Estimates: Rationality Tests and Turning Point Performance

The reliability of BEA’s estimates, as measured by the magnitude and pattern of revisions, is highly important to economic policy-making and business decisions. We find evidence that the revisions are partially predicable using contemporaneously available information for the current quarterly estimates of GDP. Information about national income is found to significantly supplement the information found in the final current quarterly estimates of GDP in explaining the revisions to the latest-available estimates of GDP. However, there is little evidence of the predictability of revisions in GDI or national income. Finally, both the advance and final current quarterly estimates are found to do a reliable job of measuring GDP and GDI around cyclical peaks, but a less reliable job around cyclical troughs, the declines preceding the troughs are overstated and the upturns after the troughs are understated. An earlier and somewhat expanded version of this paper, “Revisions, Rationality, and Turning Points in GDP,” was presented at the session “Tracking the Turning Points in the Economy,” AEA meetings January 3–5 2003, Washington DC. It is available in the “working papers” section of BEA’s web site, www.bea.gov     

Estimation Strategies for a Spatial Dynamic Panel using GMM. A New Approach to the Convergence Issue of European Regions

Abstract

While estimation methods for dynamic panel data and spatial econometric models are standard in economic literature, there has been a relatively recent development in methods which include spatial considerations in dynamic panel data models. This paper proposes two estimation strategies for spatial dynamic panel data models using the generalized method of moments (GMM). The first is to extend the moment restrictions of Arellano and Bond's estimator to a spatial autoregressive dynamic panel. The second allows for spatial dependence in the error process. The empirical application focuses on European regional growth over a 25-year period. We find empirical evidence of conditional convergence, which is significantly affected by spatial disparities.

Stratégies d'estimation pour un panel dynamique spatial faisant usage de GMM. Une nouvelle approche pour le problème de la convergence de régions d'Europe

Rèsumè Bien que les méthodes d'estimation pour les données de panels dynamiques, et les modèles économétriques spatiaux, sont des instruments standards dans les ouvrages d’économie, on a assisté à une évolution relativement récente des méthodes, qui comprend des considérations spatiales dans les modèles de panels dynamiques. La présente communication propose deux stratégies d'estimation concernant des modèles de données de panel dynamique spatiales faisant usage de la méthodes des moments généralisés (MMG). La première consiste à étendre les restrictions de moments de l'estimateur d'Arellano et Bond à un panel dynamique autorégressif spatial. La deuxième tient compte de la dépendance spatiale dans le processus des erreurs. L'application empirique se concentre sur l'expansion régionale en Europe au cours d'une période de 25 ans. Nous relevons des preuves empiriques de convergence conditionnelle, qui sont affectées de façon significative par des disparités spatiales.

Estrategias de estimación para un panel dinámico espacial utilizando GMM. Un nuevo planteamiento de la cuestión de la convergencia de regiones europeas

Extracto Aunque los métodos de estimación para datos dinámicos de panel y modelos econométricos espaciales son estándar en la bibliografía económica, se ha producido un desarrollo relativamente reciente en dichos métodos que incluye consideraciones espaciales en modelos de datos dinámicos de panel. Este estudio propone dos estrategias de estimación para los modelos de datos espaciales dinámicos de panel utilizando el método general de momentos (GMM). El primero sirve para extender las restricciones de momentos del estimador de Arellano y Bond a un panel espacial dinámico autorregresivo. El segundo tiene en cuenta una dependencia espacial en el proceso de error. La aplicación empírica se centra en el crecimiento regional europeo en un período de 25 años. Descubrimos evidencia empírica de convergencia condicional, que es afectada significativamente por disparidades espaciales.

     

Semiparametric efficient estimation of dynamic panel data models

This paper extends the semiparametric efficient treatment of panel data models pursued by Park and Simar [Park, B.U., Simar, L., 1994. Efficient semiparametric estimation in stochastic frontier models. Journal of the American Statistical Association 89, 929–936] and Park et al. [Park, B.U., Sickles, R.C., Simar, L., 1998. Stochastic frontiers: a semiparametric approach. Journal of Econometrics 84, 273–301; Park, B.U., Sickles, R.C., Simar, L., 2003. Semiparametric efficient estimation of AR(1) panel data models. Journal of Econometrics 117, 279–309] to a dynamic panel setting. We develop a semiparametric efficient estimator under minimal assumptions when the panel model contains a lagged dependent variable. We apply this new estimator to analyze the structure of demand between city pairs for selected U.S. airlines during the period 1979 I–1992 IV.     

Rational Irrationality and Group Size: The Effect of Biased Beliefs on Individual Contributions Towards Collective Goods

The price of irrationality or belief manipulation varies as we move from small to large group settings. Individual members of large groups can more cheaply bias downwards their beliefs as to the immorality of their free‐riding thereby circumventing internal moral constraints. The relative anonymity inherent to large number settings moreover reduces social pressures against free‐riding stemming from some common ethical or moral norms. Both selfish individuals facing an internal moral constraint to behave altruistically and those with altruistic preferences have an incentive to bias upward their belief of the decisiveness of their contribution in large number settings. In addition, the impact of symmetry and the illusion of control can introduce biases regarding the expected reactions of others to one's own decisions. The loosening of moral constraints will tend to increase free‐rider behavior while biased beliefs about the decisiveness of one's contribution or the reaction of others to one's actions will tend to decrease such behavior.     

Estimating semiparametric panel data models by marginal integration

We propose an alternative method for estimating the nonlinear component in semiparametric panel data models. Our method is based on marginal integration that allows us to recover the nonlinear component from an additive regression structure that results from the first differencing transformation. We characterize the asymptotic behavior of our estimator. We also extend the methodology to treat panel data models with two-way effects. Monte Carlo simulations show that our estimator behaves well in finite samples in both random effects and fixed effects settings.     

Differing behaviours of forecasters of UK GDP growth

The literature suggests that the dispersion of agents’ forecasts of an event flows from heterogeneity of beliefs and models. Using a data set of fixed event point forecasts of UK GDP growth by a panel of independent forecasters published by HM Treasury, we investigate three questions concerning this dispersion: (a) Are agent’s beliefs randomly distributed or do agents fall into groups with similar beliefs? (b) as agents revise their forecasts, what roles are played by their previous and consensus forecasts? and (c) is an agent’s private information of persistent value? We find that agents fall into four clusters, a large majority, a few pessimists, and two idiosyncratic agents. Our proposed model of forecast revisions shows agents are influenced positively by a change in the consensus forecast and negatively influenced by the previous distance of their forecast from the consensus. We show that the forecasts of a minority of agents significantly lead the consensus.     

Persistence of financial efficiency in indian hospitality and tourism industry: a dynamic panel Approach

This paper analyses the degree of persistence of financial efficiency for the hospitality and tourism industry in India. The paper deploys the Data Envelopment Analysis technique to generate overall technical efficiency scores as well as pure technical efficiency scores. Furthermore, a dynamic panel technique proposed by Arellano and Bond (1991), is employed to test the degree of persistence of financial efficiency and its key determinants. The results confirm positive and significant persistence of efficiency for the sample firms from the hospitality industry in India. The overall results indicate that hospitality firms in India can create entry and exit barriers to generate positive persistence of financial efficiency. The study suggests regulators specifically focus on policies that can enhance the competitive dynamics of the industry. Such measures may make it imperative for the management of the firms in the sector to streamline their financial management policies to control costs and devise methods for the enhancement of revenues.

     

Consistent inference in fixed-effects stochastic frontier models

The classical stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency. Greene (2005a, b) proposed the so-called “true” fixed-effects specification that distinguishes these two latent components. However, due to the incidental parameters problem, his maximum likelihood estimator may lead to biased variance estimates. We propose two alternative estimators that achieve consistency for $n\to \infty$ with fixed $T$. Furthermore, we extend the Chen et al. (2014) results providing a feasible estimator when the inefficiency is heteroskedastic and follows a first-order autoregressive process. We investigate the behavior of the proposed estimators through Monte Carlo simulations showing good finite sample properties, especially in small samples. An application to hospitals’ technical efficiency illustrates the usefulness of the new approach.     

Alliances and cost declaration

Our model explores the co‐existence of both cooperative and competitive behaviors in an alliance setting. Specifically, when alliance partners cooperatively choose observable contributions given reported costs, their self‐interested behavior may lead to misreporting of costs related to these contributions. We show that truthful cost reporting by an alliance firm is valuable, thereby establishing that accurate cost reports are a determinant of successful alliance performance. Next we show that an alliance firm's cost reporting behavior may depend on the type of payoffs it receives from alliance, i.e., a share of profit or revenue, and also on the type of relationship between contributions, i.e., whether they are technical substitutes or complements. While we have focussed on highlighting factors which underlie cost misreporting, this study also may serve as a basis for investigating ways to design contracts to diminish the loss an alliance suffers from cost misreporting of the firms, thus increasing the chance that an alliance will be successful. Copyright © 2004 John Wiley & Sons, Ltd.     

Cross‐sectional Dependency and Size Distortion in a Small‐sample Homogeneous Panel Data Unit Root Test*

In this paper, we investigate the effects of cross‐sectional disturbance correlation in a homogeneous panel data unit root test. As reported by other authors, the unit root test has incorrect size in the presence of cross‐sectional correlation. We suggest that a previously known estimator can be used to reduce the size distortions. We supply response surface estimates for critical values and study the size characteristics of the proposed test. We find that the suggested estimator performs well in small‐sample homogeneous panel data unit root tests. The reduction in size distortion comes at a small cost of lower power against a stationary alternative.     

Long difference instrumental variables estimation for dynamic panel models with fixed effects

This paper proposes a new instrumental variables estimator for a dynamic panel model with fixed effects with good bias and mean squared error properties even when identification of the model becomes weak near the unit circle. We adopt a weak instrument asymptotic approximation to study the behavior of various estimators near the unit circle. We show that an estimator based on long differencing the model is much less biased than conventional implementations of the GMM estimator for the dynamic panel model. We also show that under the weak instrument approximation conventional GMM estimators are dominated in terms of mean squared error by an estimator with far less moment conditions. The long difference (LD) estimator mimics the infeasible optimal procedure through its reliance on a small set of moment conditions.     

Heavy tails of OLS

Suppose the tails of the noise distribution in a regression exhibit power law behavior. Then the distribution of the OLS regression estimator inherits this tail behavior. This is relevant for regressions involving financial data. We derive explicit finite sample expressions for the tail probabilities of the distribution of the OLS estimator. These are useful for inference. Simulations for medium sized samples reveal considerable deviations of the coefficient estimates from their true values, in line with our theoretical formulas. The formulas provide a benchmark for judging the observed highly variable cross country estimates of the expectations coefficient in yield curve regressions.     

Estimating the output gap in real time: A factor model approach

By using a dynamic factor model, we can substantially improve the reliability of real-time output gap estimates for the U.S. economy. First, we use a factor model to extract a series for the common component in GDP from a large panel of monthly real-time macroeconomic variables. This series is immune to revisions to the extent that revisions are due to unbiased measurement errors or idiosyncratic news. Second, our model is able to handle the unbalanced arrival of the data. This yields favorable nowcasting properties and thus starting conditions for the filtering of data into a trend and deviations from a trend. Combined with the method of augmenting data with forecasts prior to filtering, this greatly reduces the end-of-sample imprecision in the gap estimate. The increased precision has economic importance for real-time policy decisions and improves real-time inflation forecasts.