Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Summary. Probabilistic forecasts of continuous variables take the form of predictive densities or predictive cumulative distribution functions. We propose a diagnostic approach to the evaluation of predictive performance that is based on the paradigm of maximizing the sharpness of the predictive distributions subject to calibration. Calibration refers to the statistical consistency between the distributional forecasts and the observations and is a joint property of the predictions and the events that materialize. Sharpness refers to the concentration of the predictive distributions and is a property of the forecasts only. A simple theoretical framework allows us to distinguish between probabilistic calibration, exceedance calibration and marginal calibration. We propose and study tools for checking calibration and sharpness, among them the probability integral transform histogram, marginal calibration plots, the sharpness diagram and proper scoring rules. The diagnostic approach is illustrated by an assessment and ranking of probabilistic forecasts of wind speed at the Stateline wind energy centre in the US Pacific Northwest. In combination with cross-validation or in the time series context, our proposal provides very general, nonparametric alternatives to the use of information criteria for model diagnostics and model selection.

https://hal.archives-ouvertes.fr/hal-00363242/document

Probabilistic forecasts, calibration and sharpness

https://www.robjhyndman.com/papers/ijf25.pdf

25 years of time series forecasting

Why do firms cluster near one another? We test Marshall's theories of industrial agglomeration by examining which industries locate near one another, or coagglomerate. We construct pairwise coagglomeration indices for US manufacturing industries from the Economic Census. We then relate coagglomeration levels to the degree to which industry pairs share goods, labor, or ideas. To reduce reverse causality, where collocation drives input-output linkages or hiring patterns, we use data from UK industries and from US areas where the two industries are not collocated. All three of Marshall's theories of agglomeration are supported, with input-output linkages particularly important. (JEL L14, L60, O33, R23, R32)

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What Causes Industry Agglomeration? Evidence from Coagglomeration Patterns

Proximity and Innovation: A Critical Assessment

Different tourists to different destinations. Evidence from spatial interaction models

In the last decade there has been an upsurge of studies on international comparisons of Total Factor Productivity (TFP). The empirical evidence suggests that countries and regions differ not only in traditional factor endowments (labour and physical capital) but mainly in productivity and technology. Therefore, a crucial issue is the analysis of the determinants of such differences in the efficiency levels across economies.In this paper we try to assess these issues by pursuing a twofold aim. First, we derive a regression based measure of regional TFP, which has the nice advantage of not imposing a priori restrictions on the inputs elasticities; this is done by estimating a spatial Cobb-Douglas production function relationship for 199 European regions over the period 1985-2006. Secondly, we investigate the determinants of the TFP levels by analyzing the role played by intangible factors: human capital, social capital and technological capital. The estimations are carried out by applying the spatial 2SLS method and the SHAC estimator to account for both heteroskedasticity and spatial autocorrelation. It turns out that a large part of TFP differences across the European regions are explained by the disparities in the endowments of these intangible assets. This outcome indicates the importance of policy strategies which aim at increasing the level of knowledge and social capital as stressed by the Lisbon agenda.

Total Factor Productivity, Intangible Assets and Spatial Dependence in the European Regions

The recent history of Europe is characterized by a dual picture showing the Old and New countries in sharp contrast with respect to their industrial specialisation and economic performance. We aim at analyzing the intertwined performance of regions and industries in New and Old European economies by investigating the effects of local agglomeration externalities (mainly specialisation and diversity externalities) on total factor productivity dynamics. We also analyse the potential influence of regional intangible assets such as human and technological capital. The econometric analysis makes use of spatial econometric techniques to take into account the possibility of crossborder externalities.

https://people.unica.it/stefanousai/files/2013/10/lecture_6.pdf

Productivity growth in the Old and New Europe: the role of agglomeration externalities

Proximity, networking and knowledge production in Europe: What lessons for innovation policy?

There is a large consensus among social researchers on the positive role that human capital plays in economic performances. The standard way to measure the human capital endowment is to consider th...

/pdf/education-or-creativity-what-matters-most-for-economic-1skzdsqylq.pdf

Emanuela Marrocu

Papers

Different tourists to different destinations. Evidence from spatial interaction models

Total Factor Productivity, Intangible Assets and Spatial Dependence in the European Regions

Productivity growth in the Old and New Europe: the role of agglomeration externalities

Proximity, networking and knowledge production in Europe: What lessons for innovation policy?

Education or Creativity: What Matters Most for Economic Performance?