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

Homoskedasticity is an important assumption in ordinary least squares (OLS) regression. Although the estimator of the regression parameters in OLS regression is unbiased when the homoskedasticity assumption is violated, the estimator of the covariance matrix of the parameter estimates can be biased and inconsistent under heteroskedasticity, which can produce significance tests and confidence intervals that can be liberal or conservative. After a brief description of heteroskedasticity and its effects on inference in OLS regression, we discuss a family of heteroskedasticity-consistent standard error estimators for OLS regression and argue investigators should routinely use one of these estimators when conducting hypothesis tests using OLS regression. To facilitate the adoption of this recommendation, we provide easy-to-use SPSS and SAS macros to implement the procedures discussed here.

https://link.springer.com/content/pdf/10.3758/BF03192961.pdf

Using heteroskedasticity-consistent standard error estimators in OLS regression: an introduction and software implementation.

Determinants of economic growth: A cross-country empirical study.

Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding.

Foundations of Linear and Generalized Linear Models

The paradox of plenty

Quantile Regression: Theory and Applications

The paper investigated whether information about the health benefit produced by lycopene could influence consumers’ willingness to pay (WTP) for canned crushed tomatoes enriched with lycopene. An additional aim was to determine whether the main socio-demographic variables, such as sex, age, income and selected attitudinal factors, affect WTP. To this end, a non-hypothetical experimental auction was carried on with five repeated rounds. Results show a relevant impact of information on WTP in the case of lycopene-enriched products, whereas no difference in bids emerges for the conventional product, either on average or at the quantiles. Previous knowledge seems to have a modest influence upon WTP, but it shows a significant interaction with the information shock provided during the experiment, so that the effect of the latter is more pronounced when previous knowledge is low. In addition, age, sex, food technology neophobia, trust in science and implicit attitudes towards food technology significantly affect participants’ WTP.

https://www.mdpi.com/2072-6643/11/9/2173/pdf

The Role of Health Information in Consumers' Willingness to Pay for Canned Crushed Tomatoes Enriched with Lycopene.

To deal with heteroskedasticity of unknown form, this paper suggests to robustly estimate the regression coefficients and then to implement an heteroskedasticity consistent covariance matrix estimator. The robust regression reduces the sample bias of the heteroskedasticity consistent covariance matrix estimator, and does not require the specification of a functional form for heteroskedasticity. Indeed, it replaces each variance with the squared errors, which are in turn estimated by the squared residuals from the robust regression. A Monte-Carlo study verifies the behavior of the proposed estimator, using real data.

A Robust Heteroskedasticity Consistent Covariance Matrix Estimator

In heteroskedastic regression models, the least squares (OLS) covariance matrix estimator is inconsistent and inference is not reliable. To deal with inconsistency one can estimate the regression coefficients by OLS, and then implement a heteroskedasticity consistent covariance matrix (HCCM) estimator. Unfortunately the HCCM estimator is biased. The bias is reduced by implementing a robust regression, and by using the robust residuals to compute the HCCM estimator (RHCCM). A Monte-Carlo study analyzes the behavior of RHCCM and of other HCCM estimators, in the presence of systematic and random heteroskedasticity, and of outliers in the explanatory variables.

Small sample behavior of a robust heteroskedasticity consistent covariance matrix estimator

The paper compares the existing tests for parameter instability in quantile regression One is based on the estimated objective function and the other on the gradient Their definition determines their characteristics and helpfulness The former allows to check if the impact of a break on the entire equation changes across quantiles while a modified version of the latter verifies if the break affects only some coefficients or all of them and helps locating the break point In addition the paper presents a Lagrange multiplier test for structural break The advantage of the LM test is in the ease of implementation, since it simply requires the estimation of an auxiliary regression An example shows the characteristics of each test A Monte Carlo study concludes the analysis

Marilena Furno

Papers

Quantile Regression: Theory and Applications

The Role of Health Information in Consumers' Willingness to Pay for Canned Crushed Tomatoes Enriched with Lycopene.

A Robust Heteroskedasticity Consistent Covariance Matrix Estimator

Small sample behavior of a robust heteroskedasticity consistent covariance matrix estimator

Tests for structural break in quantile regressions