Covariate Measurement Error in Endogenous Stratified Samples

In this paper we propose a general framework to deal with the presence of covariate mea-surement error in endogenous stratifield samples. Using Chesher’s (2000) methodology, we develop approximately consistent estimators for the parameters of the structural model, in the sense that their inconsistency is of smaller order than that of the conventional estimators which ignore the existence of covariate measurement error. The approximate bias corrected estimators are obtained by applying the generalized method of moments (GMM) to a modifeld version of the moment indicators suggested by Imbens and Lancaster (1996) for endogenous stratified samples. Only the specification of the conditional distribution of the response vari-able given the latent covariates and the classical additive measurement error model assumption are required, the availability of information on both the marginal probability of the strata in the population and the variance of the measurement error not being essential. A score test to detect the presence of covariate measurement error arises as a by-product of this approach. Monte Carlo evidence is presented which suggests that, in endogenous stratified samples of moderate sizes, the modified GMM estimators perform well

Explaining the Negative Coefficient Associated with Human Capital in Augmented Solow Growth Regressions

In this paper we consider different explanations for why the coefficient associated with human capital is often negative in growth regressions once country-specific effects are controlled for whereas the coefficient in question is strongly positive in cross-sectional or panel results based on the pooling estimator. In turn, we explore: (i) additional sources of unobserved heterogeneity stemming from country-specific rates of labor-augmenting technological change, (ii) measurement error in the human capital series being used, and (iii) the lack of variability in the human capital series once the usual covariance transformations are implemented. Remaining unobserved country-specific heterogeneity and measurement error alone are shown to be inadequate explanations. The lack of variability in the human capital series is tackled using a new GMM-based estimator that combines the Hausman-Taylor (1981) approach, in which the impact of time-invariant covariates can be identified through use of covariance transformations of the variables themselves as instruments, with the orthogonality conditions of the Arellano-Bond (1991) estimator.panel estimation, measurement error, human capital, economic growth

Public bus transport demand elasticities in India

A number of static and dynamic specifications of a log linear demand function for public transport are estimated using aggregate panel data for 22 Indian states over the period 1990 to 2001. Demand has been defined as total passenger kilometers to capture actual market transactions, while the regressors include public transit fare, per capita income, service quality, and other demographic and social variables. In all cases, transit demand is significant and inelastic to the fare. Service quality is the most significant policy variable. Finally, social and demographic variables highlight the complex nature of public bus transit demand in India.Demand Elasticities, Dynamic Panel Data, Bus Transport, India

A Spatial Quantile Regression Hedonic Model of Agricultural Land Prices

Abstract Land price studies typically employ hedonic analysis to identify the impact of land characteristics on price. Owing to the spatial fixity of land, however, the question of possible spatial dependence in agricultural land prices arises. The presence of spatial dependence in agricultural land prices can have serious consequences for the hedonic model analysis. Ignoring spatial autocorrelation can lead to biased estimates in land price hedonic models. We propose using a flexible quantile regression-based estimation of the spatial lag hedonic model allowing for varying effects of the characteristics and, more importantly, varying degrees of spatial autocorrelation. In applying this approach to a sample of agricultural land sales in Northern Ireland we find that the market effectively consists of two relatively separate segments. The larger of these two segments conforms to the conventional hedonic model with no spatial lag dependence, while the smaller, much thinner market segment exhibits considerable spatial lag dependence. Un mod�le h�donique � r�gression quantile spatiale des prix des terrains agricoles R�sum� Les �tudes sur le prix des terrains font g�n�ralement usage d'une analyse h�donique pour identifier l'impact des caract�ristiques des terrains sur le prix. Toutefois, du fait de la fixit� spatiale des terrains, la question d'une �ventuelle d�pendance spatiale sur la valeur des terrains agricoles se pose. L'existence d'une d�pendance spatiale dans le prix des terrains agricoles peut avoir des cons�quences importantes sur l'analyse du mod�le h�donique. En ignorant cette corr�lation s�rielle, on s'expose au risque d'�valuations biais�es des mod�les h�doniques du prix des terrains. Nous proposons l'emploi d'une estimation � base de r�gression flexible du mod�le h�donique � d�calage spatial, tenant compte de diff�rents effets des caract�ristiques, et surtout de diff�rents degr�s de corr�lations s�rielles spatiales. En appliquant ce principe � un �chantillon de ventes de terrains agricoles en Irlande du Nord, nous d�couvrons que le march� se compose de deux segments relativement distincts. Le plus important de ces deux segments est conforme au mod�le h�donique traditionnel, sans d�pendance du d�calage spatial, tandis que le deuxi�me segment du march�, plus petit et beaucoup plus �troit, pr�sente une d�pendance consid�rable du d�calage spatial. Un modelo hed�nico de regresi�n cuantil espacial de los precios del terreno agr�cola Resumen T�picamente, los estudios del precio de la tierra emplean un an�lisis hed�nico para identificar el impacto de las caracter�sticas de la tierra sobre el precio. No obstante, debido a la fijeza espacial de la tierra, surge la cuesti�n de una posible dependencia espacial en los precios del terreno agr�cola. La presencia de dependencia espacial en los precios del terreno agr�cola puede tener consecuencias graves para el modelo de an�lisis hed�nico. Ignorar la autocorrelaci�n espacial puede conducir a estimados parciales en los modelos hed�nicos del precio de la tierra. Proponemos el uso de una valoraci�n basada en una regresi�n cuantil flexible del modelo hed�nico del lapso espacial que tenga en cuenta los diversos efectos de las caracter�sticas y, particularmente, los diversos grados de autocorrelaci�n espacial. Al aplicar este planteamiento a una muestra de ventas de terreno agr�cola en Irlanda del Norte, descubrimos que el mercado consiste efectivamente de dos segmento relativamente separados. El m�s grande de estos dos segmentos se ajusta al modelo hed�nico convencional sin dependencia del lapso espacial, mientras que el segmento m�s peque�o, y mucho m�s fino, muestra una dependencia considerable del lapso espacial.Spatial lag, quantile regression, hedonic model, C13, C14, C21, Q24,

Quantum Tomography

This is the draft version of a review paper which is going to appear in "Advances in Imaging and Electron Physics"Comment: To appear in "Advances in Imaging and Electron Physics". Some figs with low resolutio

Why we measure period fertility

Four reasons for measuring period fertility are distinguished: to explain fertility time trends, to anticipate future fertility, to construct theoretical models and to communicate with non-specialist audiences. The paper argues that not all measures are suitable for each purpose, and that tempo adjustment may be appropriate for some objectives but not others. In particular, it is argued that genuine timing effects do not bias or distort measures of period fertility as dependent variable. Several different concepts of bias or distortion are identified in relation to period fertility measures. Synthetic cohort indicators are a source of confusion since they conflate measurement and forecasting. Anticipating future fertility is more akin to forecasting than to measurement. Greater clarity about concepts and measures in the fertility arena could be achieved by a stronger emphasis on validation. Period incidence and occurrence-exposure rates have a straightforward interpretation. More complex period fertility measures are meaningful only if a direct or indirect criterion can be specified against which to evaluate them. Their performance against that criterion is what establishes them as valid or useful

The Log of Gravity

Although economists have long been aware of Jensen's inequality, many econometric applications have neglected an important implication of it: the standard practice of interpreting the parameters of log-linearized models estimated by ordinary least squares as elasticities can be highly misleading in the presence of heteroskedasticity. This paper explains why this problem arises and proposes an appropriate estimator. Our criticism to conventional practices and the solution we propose extends to a broad range of economic applications where the equation under study is log-linearized. We develop the argument using one particular illustration, the gravity equation for trade, and apply the proposed technique to provide new estimates of this equation. We find significant differences between estimates obtained with the proposed estimator and those obtained with the traditional method. These discrepancies persist even when the gravity equation takes into account multilateral resistance terms or fixed effectsElasticities, Gravity equation, Heteroskedasticity, Jensens inequality, Poisson regression, Preferential-trade agreements

Should Exact Index Numbers Have Standard Errors? Theory and Application to Asian Growth

In this paper we derive the standard error of a price index when both prices and tastes or technology are treated as stochastic. Changing tastes or technology are a reason for the weights in the price index to be treated as stochastic, which can interact with the stochastic prices themselves. We derive results for the constant elasticity of substitution expenditure function (with Sato-Vartia price index), and also the translog function (with T””rnqvist price index), which proves to be more general and easier to implement. In our application to Asian growth, we construct standard errors on the total factor productivity (TFP) estimates of Hsieh (2002) for Singapore. We find that TFP growth is insignificantly different from zero in any year, but cumulative TFP over fifteen years is indeed positive.