Endogenous Variable (endogenous + variable)

Distribution by Scientific Domains
Distribution within Business, Economics, Finance and Accounting


Selected Abstracts


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

ECONOMETRICA, Issue 4 2005
Joseph G. Altonji
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. [source]


Competition on the London Stock Exchange

EUROPEAN FINANCIAL MANAGEMENT, Issue 4 2002
Nicholas Taylor
This paper investigates the determinants of the level of competition on the order,driven market organised by the London Stock Exchange. In contrast to previous empirical market microstructure studies, we treat the level of competition as an endogenous variable. The statistical nature of the measures of competitive activity used in this paper necessitate use of a count regression model. Using a sample 50 stocks, we find that users of the system tend to follow the lead of other users (termed the ,herding effect') and that competition is greater during the period when the US exchanges are open (termed the ,US effect'). In addition, the level of competition is positively related to the bid,ask spread pertaining to a particular stock (termed the ,spread effect'). The latter result is most likely due to traders following a strategy where trade immediacy is traded off against price advantage. Finally, we find that the magnitude of the herding effect, the spread effect, and the fit of the count regression models (termed the ,fit effect') vary in a predictable manner across the liquidity of stocks. [source]


On the use of reactive power as an endogenous variable in short-term load forecasting

INTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 5 2003
P. Jorge Santos
Abstract In the last decades, short-term load forecasting(STLF) has been the object of particular attention in the power systems field. STLF has been applied almost exclusively to the generation sector, based on variables, which are transversal to most models. Among the most significant variables we can find load, expressed as active power (MW), as well as exogenous variables, such as weather and economy-related ones; although the latter are applied in larger forecasting horizons than STLF. In this paper, the application of STLF to the distribution sector is suggested including inductive reactive power as a forecasting endogenous variable. The inclusion of this additional variable is mainly due to the evidence that correlations between load and weather variables are tenuous, due to the mild climate of the actual case-study system and the consequent feeble penetration of electrical heating ventilation and air conditioning loads. Artificial neural networks (ANN) have been chosen as the forecasting methodology, with standard feed forward back propagation algorithm, because it is a largely used method with generally considered satisfactory results. Usually the input vector to ANN applied to load forecasting is defined in a discretionary way, mainly based on experience, on engineering judgement criteria and on concern about the ANN dimension, always taking into consideration the apparent (or actually evaluated) correlations within the available data. The approach referred in the paper includes pre-processing the data in order to influence the composition of the input vector in such a way as to reduce the margin of discretion in its definition. A relative entropy analysis has been performed to the time series of each variable. The paper also includes an illustrative case study. Copyright © 2003 John Wiley & Sons, Ltd. [source]


Bias from Farmer Self-Selection in Genetically Modified Crop Productivity Estimates: Evidence from Indian Data

JOURNAL OF AGRICULTURAL ECONOMICS, Issue 1 2007
Benjamin Crost
Q12; D81 Abstract In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed. [source]


Probability matching priors for an extended statistical calibration model

THE CANADIAN JOURNAL OF STATISTICS, Issue 1 2001
Daniel R. Eno
Abstract Statistical calibration or inverse prediction involves data collected in two stages. In the first stage, several values of an endogenous variable are observed, each corresponding to a known value of an exogenous variable; in the second stage, one or more values of the endogenous variable are observed which correspond to an unknown value of the exogenous variable. When estimating the value of the latter, it has been suggested that the variability about the regression relationship should not be assumed to be equal for the two stages of data collection. In this paper, the authors present a Bayesian method of analysis based on noninformative priors that takes this heteroscedasticity into account. Le problème de la calibration statistique ou de la prévision inverse concerne des données recueillies en deux temps. La variable endogène est d'abord observée à plusieurs reprises pour des valeurs connues de la variable exogène; puis, la variable endogène est mesurée sur certains individus pour lesquels la variable exogène est indéterminée. Pour estimer la valeur prise par cette dernière, on suppose généralement que la variance du terme d'erreur du modèle de régression n'est pas la même pour les deux phases de cueillette. Dans cet article, les auteurs présentent une méthode d'analyse bayésienne à base de lois a priori non informatives qui tient compte de cette hétéroscédasticité. [source]


Bootstrap inference in a linear equation estimated by instrumental variables

THE ECONOMETRICS JOURNAL, Issue 3 2008
Russell Davidson
Summary, We study several tests for the coefficient of the single right-hand-side endogenous variable in a linear equation estimated by instrumental variables. We show that writing all the test statistics,Student's t, Anderson,Rubin, the LM statistic of Kleibergen and Moreira (K), and likelihood ratio (LR),as functions of six random quantities leads to a number of interesting results about the properties of the tests under weak-instrument asymptotics. We then propose several new procedures for bootstrapping the three non-exact test statistics and also a new conditional bootstrap version of the LR test. These use more efficient estimates of the parameters of the reduced-form equation than existing procedures. When the best of these new procedures is used, both the K and conditional bootstrap LR tests have excellent performance under the null. However, power considerations suggest that the latter is probably the method of choice. [source]


Decision Theory Applied to an Instrumental Variables Model

ECONOMETRICA, Issue 3 2007
Gary Chamberlain
This paper applies some general concepts in decision theory to a simple instrumental variables model. There are two endogenous variables linked by a single structural equation; k of the exogenous variables are excluded from this structural equation and provide the instrumental variables (IV). The reduced-form distribution of the endogenous variables conditional on the exogenous variables corresponds to independent draws from a bivariate normal distribution with linear regression functions and a known covariance matrix. A canonical form of the model has parameter vector (,, ,, ,), where ,is the parameter of interest and is normalized to be a point on the unit circle. The reduced-form coefficients on the instrumental variables are split into a scalar parameter ,and a parameter vector ,, which is normalized to be a point on the (k,1)-dimensional unit sphere; ,measures the strength of the association between the endogenous variables and the instrumental variables, and ,is a measure of direction. A prior distribution is introduced for the IV model. The parameters ,, ,, and ,are treated as independent random variables. The distribution for ,is uniform on the unit circle; the distribution for ,is uniform on the unit sphere with dimension k-1. These choices arise from the solution of a minimax problem. The prior for ,is left general. It turns out that given any positive value for ,, the Bayes estimator of ,does not depend on ,; it equals the maximum-likelihood estimator. This Bayes estimator has constant risk; because it minimizes average risk with respect to a proper prior, it is minimax. The same general concepts are applied to obtain confidence intervals. The prior distribution is used in two ways. The first way is to integrate out the nuisance parameter ,in the IV model. This gives an integrated likelihood function with two scalar parameters, ,and ,. Inverting a likelihood ratio test, based on the integrated likelihood function, provides a confidence interval for ,. This lacks finite sample optimality, but invariance arguments show that the risk function depends only on ,and not on ,or ,. The second approach to confidence sets aims for finite sample optimality by setting up a loss function that trades off coverage against the length of the interval. The automatic uniform priors are used for ,and ,, but a prior is also needed for the scalar ,, and no guidance is offered on this choice. The Bayes rule is a highest posterior density set. Invariance arguments show that the risk function depends only on ,and not on ,or ,. The optimality result combines average risk and maximum risk. The confidence set minimizes the average,with respect to the prior distribution for ,,of the maximum risk, where the maximization is with respect to ,and ,. [source]


Mode Choice, Commuting Cost, and Urban Household Behavior,

JOURNAL OF REGIONAL SCIENCE, Issue 3 2005
Joseph S. DeSalvo
DeSalvo demonstrated that the urban model of Muth (1969) was robust to the extension to leisure choice. We show that the model is robust to mode choice as well. In addition, we derive the comparative static results that commuters choose higher speed modes for longer commutes, at higher wage rates, with greater tastes for housing, and with lower housing prices. Also, for a given distance commuted, we derive the comparative static result that commuters chose shorter duration commutes at higher wage rates. Whereas it is typically assumed that marginal commuting cost is positive and non-increasing with distance, we derive these results. Moreover, we derive the results that marginal commuting cost rises with an exogenous increase in housing price and falls with increased tastes for housing. We also explore the effects of exogenous commuting-cost changes on the endogenous variables of the model. The remaining comparative static results on housing consumption and location are qualitatively the same as in DeSalvo. [source]


Relationship Marketing and Supplier Logistics Performance: An Extension of the Key Mediating Variables Model

JOURNAL OF SUPPLY CHAIN MANAGEMENT, Issue 4 2005
Matthew Morris
Summary Firms are increasingly relying on relational exchanges to govern buyer,supplier relationships. While the antecedents to these relationships have been studied extensively in the marketing channels and supply chain management literature, relatively little research has considered the performance outcomes of such exchanges. The current study contributes to this stream of research by extending Morgan and Hunt's key mediating variables (KMV) model to examine how the five key endogenous variables from the KMV model affect supplier logistics performance. The findings suggest that cooperation and uncertainty are significantly related to supplier logistics performance, while supplier acquiescence, functional conflict and propensity to leave the relationship have no significant impact. [source]


Dragon Children: Identifying the Causal Effect of the First Child on Female Labour Supply with the Chinese Lunar Calendar,

OXFORD BULLETIN OF ECONOMICS & STATISTICS, Issue 3 2008
James P. Vere
Abstract Instrumental variables (IV) estimates of the effect of fertility on female labour supply have only been able to identify the causal effect of second and higher parity children. This study uses exogenous variation in fertility caused by the Chinese lunar calendar to identify the effect of the first child. Additionally, weighting formulas are presented to interpret IV estimates as weighted average treatment effects in the case of multiple endogenous variables, which are useful when children vary in intensity by both number and age. The effect of the first child is found to be much greater than that of other children. [source]


Calorie intake and income elasticities in EU countries: A convergence analysis using cointegration,

PAPERS IN REGIONAL SCIENCE, Issue 2 2001
Ana M. Angulo
European food demand; calorie intake; cointegration; convergence Abstract. We want to determine here whether the trans-European consumer reacts to changes in total food consumption or changes in income equalise in the long run. Do total calorie intake elasticities and income elasticities converge in the long-run? A demand system is estimated for each European country. The proportional caloric intakes of the various food groups are analyzed as endogenous variables, and two exogenous variables (total calorie intake and income), are both defined in log terms. As all variables are I(1) and non-cointegrated, demand systems are specified in first differences. Finally, we use Johansen and Juselius's multivariate cointegration tests to test for the convergence of calorie intake and income elasticities. Empirical results indicate a very limited convergence between certain products and countries considered, suggesting that country idiosyncrasies still play an important role in consumer behavior. [source]