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Monte Carlo simulations are a very powerful way to demonstrate the basic sampling properties of various statistics in econometrics. The commercial software package Stata makes these methods accessible to a wide audience of students and practitioners. The purpose of this chapter is to present a self-contained primer for conducting Monte Carlo exercises as part of an introductory econometrics course. More experienced econometricians that are new to Stata may find this useful as well. Many examples are given that can be used as templates for various exercises. Examples include linear regression, confidence intervals, the size and power of t-tests, lagged dependent variable models, heteroskedastic and autocorrelated regression models, instrumental variables estimators, binary choice, censored regression, and nonlinear regression models. Stata do-files for all examples are available from the authors' website http://learneconometrics.com/pdf/MCstata/.

The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems.

The author details the main issues with the log-linear model, drawing from the applied econometric literature in favor of estimating multiplicative models for non-count data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE).

The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the log-linear model, as well as other alternative models, such as Tobit and two-part models.

The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.

This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

In this chapter we demonstrate the construction of inverse test confidence intervals for the turning-points in estimated nonlinear relationships by the use of the marginal or first derivative function. First, we outline the inverse test confidence interval approach. Then we examine the relationship between the traditional confidence intervals based on the Wald test for the turning-points for a cubic, a quartic, and fractional polynomials estimated via regression analysis and the inverse test intervals. We show that the confidence interval plots of the marginal function can be used to estimate confidence intervals for the turning-points that are equivalent to the inverse test. We also provide a method for the interpretation of the confidence intervals for the second derivative function to draw inferences for the characteristics of the turning-point.

This method is applied to the examination of the turning-points found when estimating a quartic and a fractional polynomial from data used for the estimation of an Environmental Kuznets Curve. The Stata do files used to generate these examples are listed in Appendix A along with the data.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.

Lewbel and Pendakur (2009) developed the idea of implicit Marshallian demands. Implicit Marshallian demand systems allow the incorporation of both unobserved preference heterogeneity and complex Engel curves into consumer demand analysis, circumventing the standard problems associated with combining rationality with either unobserved heterogeneity or high rank in demand (or both). They also developed the exact affine Stone index (EASI) implicit Marshallian demand system wherein much of the demand system is linearised and thus relatively easy to implement and estimate. This chapter offers a less technical introduction to implicit Marshallian demands in general and to the EASI demand system in particular. I show how to implement the EASI demand system, paying special attention to tricks that allow the investigator to further simplify the problem without sacrificing too much in terms of model flexibility. STATA code to implement the simplified models is included throughout the text and in an appendix.

Like many countries, the US government-imposed travel restriction policies on selected countries with a high spread of COVID-19 airports to prevent the introduction and spread of COVID-19. Between March 2020 and October 2021, travellers from China, Iran, European Schengen countries, the United Kingdom, Republic of Ireland, Brazil, South Africa and India were restricted with some exceptions. The main objective with this study was to explore the associations between COVID-19 cases and death rates, and the proximity to airports, train stations and time of public transportation. To address the study objective, the authors used the most recent JHU COVID-19 database, the American Community Survey and Airport and Amtrak data from the Bureau of Transportation Statistics from 3,132 US counties. The authors categorised the counties into three groups according to their distance from an airport: less than 25 miles, between 25 and 50 miles and more than 50 miles. The authors then ran negative binomial regressions and Cox regression models, adjusted for population density, population race/ethnicity, travel time, being close to an international airport and the main sources of commutes. The findings showed that the number of airports, the number of train station and the length of commuting time were predictors for the number of deaths and cases in a county. The authors found that counties within 25 miles of an airport had 1.372 times the rate of COVID-19 cases and 1.338 times the rate of COVID-19 deaths compared to the counties that were more than 50 miles from an airport. To prevent the introduction and spread of COVID-19 and any similar pandemic that transfers by air, the timing of the travel restriction policy is a crucial element. Policymakers and officials in transportation and public health should collaborate to promulgate policies and procedures to prevent the spread of airborne infectious diseases.

In this chapter, we explicate two related techniques that help quantify the sensitivity of a given causal inference to potential omitted variables and/or other sources of unexplained heterogeneity. In particular, we describe the Impact Threshold of a Confounding Variable (ITCV) and the Robustness of Inference to Replacement (RIR). The ITCV describes the minimum correlation necessary between an omitted variable and the focal parameters of a study to have created a spurious or invalid statistical inference. The RIR is a technique that quantifies the percentage of observations with nonzero effects in a sample that would need to be replaced with zero effects in order to overturn a given causal inference at any desired threshold. The RIR also measures the percentage of a given parameter estimate that would need to be biased in order to overturn an inference. Each of these procedures is critical to help establish causal inference, perhaps especially for research urgently studying the COVID-19 pandemic when scholars are not afforded the luxury of extended time periods to determine precise magnitudes of relationships between variables. Over the course of this chapter, we define each technique, illustrate how they are applied in the context of seminal strategic management research, offer guidelines for interpreting corresponding results, and delineate further considerations.

This study assessed the effectiveness of malaria classroom corner (MCC), school-based intervention in the promotion of basic malaria awareness and common control practices among children of primary school age.

A quasi-experimental design was employed, involving 206 children of primary 5 and 6 classes from two randomly selected public primary schools in Owerri, South Eastern Nigeria. The MCC was designed and set up in the intervention school (with 103 children) while the control school (with 103 children) was offered malaria health talk. Structured pre-tested questionnaire was used to collect data pre- and post-intervention in both schools. Data was analysed using Statistical Package – Stata version 14.1 (Stata Corp, College Station, TX, USA).

Results show that there was a significant enhancement of basic malaria awareness (p = 0.0003) and common preventive and management practices (p = 0.0202) among children in the intervention primary school compared to those in the control primary school.

The study did not account for actual behaviour change, as its scope was within basic malaria awareness and common control practices.

This approach could enhance awareness and proactiveness of school children towards malaria prevention and overall health consciousness.

This could help in achieving a healthy population of school children with a positive effect on their school performance.

The MCC could provide a simple, participatory and effective approach for the promotion of basic malaria awareness and common control practices among primary school-age children in malaria endemic areas. Such children could, in turn, become malaria conversation drivers and behaviour change agents in their homes and communities, thereby contributing to the malaria elimination efforts.