A complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python software guides.

/pdf/introductory-econometrics-for-finance-5bha97pwpi.pdf

Introductory Econometrics for Finance

Empirical analyses in social science frequently confront quantitative data that are clustered or grouped. To account for group-level variation and improve model fit, researchers will commonly specify either a fixed- or random-effects model. But current advice on which approach should be preferred, and under what conditions, remains vague and sometimes contradictory. This study performs a series of Monte Carlo simulations to evaluate the total error due to bias and variance in the inferences of each model, for typical sizes and types of datasets encountered in applied research. The results offer a typology of dataset characteristics to help researchers choose a preferred model.

/pdf/should-i-use-fixed-or-random-effects-3ku3gntgsg.pdf

Should I Use Fixed or Random Effects

An increase in the household debt to GDP ratio predicts lower GDP growth and higher unemployment in the medium run for an unbalanced panel of 30 countries from 1960 to 2012. Low mortgage spreads are associated with an increase in the household debt to GDP ratio and a decline in subsequent GDP growth, highlighting the importance of credit supply shocks. Economic forecasters systematically over-predict GDP growth at the end of household debt booms, suggesting an important role of flawed expectations formation. The negative relation between the change in household debt to GDP and subsequent output growth is stronger for countries with less flexible exchange rate regimes. We also uncover a global household debt cycle that partly predicts the severity of the global growth slowdown after 2007. Countries with a household debt cycle more correlated with the global household debt cycle experience a sharper decline in growth after an increase in domestic household debt.

/pdf/household-debt-and-business-cycles-worldwide-4hdi29ce8l.pdf

Household Debt and Business Cycles Worldwide

The Arizona legislature passed HB 2281, which eliminated Tucson Unified School District’s (TUSD’s) Mexican American Studies (MAS) program, arguing the curriculum was too political. This program has been at the center of contentious debates, but a central question has not been thoroughly examined: Do the classes raise student achievement? The current analyses use administrative data from TUSD (2008–2011), running logistic regression models to assess the relationship between taking MAS classes and passing AIMS (Arizona state standardized tests) and high school graduation. Results indicate that MAS participation was significantly related to an increased likelihood of both outcomes occurring. The authors discuss these results in terms of educational policy and critical pedagogy as well as the role academics can play in policy formation.

/pdf/missing-the-student-achievement-forest-for-all-the-political-4ti0evncxm.pdf

Missing the (Student Achievement) Forest for All the (Political) Trees Empiricism and the Mexican American Studies Controversy in Tucson

South America’s tropical dry forests and savannas are under increasing pressure from agricultural expansion. Cattle ranching and soybean production both drive these forest losses, but their relative importance remains unclear. Also unclear is how soybean expansion elsewhere affects deforestation via pushing cattle ranching to deforestation frontiers. To assess these questions, we focused on the Chaco, a 110 million ha ecoregion extending into Argentina, Bolivia, and Paraguay, with about 8 million ha of deforestation in 2000–2012. We used panel regressions at the district level to quantify the role of soybean expansion in driving these forest losses using a wide range of environmental and socio-economic control variables. Our models suggest that soybean production was a direct driver of deforestation in the Argentine Chaco only (0.08 ha new soybean area per ha forest lost), whereas cattle ranching was significantly associated with deforestation in all three countries (0.02 additional cattle per hectare forest loss). However, our models also suggested Argentine soybean cultivation may indirectly be linked to deforestation in the Bolivian and Paraguayan Chaco. We furthermore found substantial time-delayed effects in the relationship of soybean expansion in Argentina and Paraguay (i.e., soybean expansion in one year resulted in deforestation several years later) and deforestation in the Chaco, further suggesting that possible displacement effects within and between Chaco countries may at least partly drive forest loss. Altogether, our study showed that deforestation in the Chaco appears to be mainly driven by the globally surging demand for soybean, although regionally other proximate drivers are sometimes important. Steering agricultural production in the Chaco and other tropical dry forests onto sustainable pathways will thus require policies that consider these scale effects and that account for the regional variation in deforestation drivers within and across countries.

The role of soybean production as an underlying driver of deforestation in the South American Chaco

Introduction to Econometrics provides students with clear and simple mathematics notation and step-by-step explanations of mathematical proofs, to give them a thorough understanding of the subject Extensive exercises throughout build confidence by encouraging students to apply econometric techniques Retaining its student-friendly approach, Introduction to Econometrics has a comprehensive revision guide to all the essential statistical concepts needed to study econometrics, additional Monte Carlo simulations, new summaries, and non-technical introductions to more advanced topics at the end of chapters This book is supported by an Online Resource Centre, which includes: For lecturers: DT Instructor's manual for the text and data sets, detailing the exercises and their solutions DT Customizable PowerPoint slides For students: DT Data sets referred to in the book DT A comprehensive study guide offers students the opportunity to gain experience with econometrics through practice with exercises DT Software manual DT PowerPoint slides with explanations

Introduction to Econometrics

1. Covariance, Variance, and Correlation 2. Simple Regression Analysis 3. Properties of the Regression Coefficients 4. Multiple Regression Analysis 5. Transformation of Variables 6. Dummy Variables 7. Specification of Regression Variables 8. Heterodasticity 9. Stochastic Regressors and Measurement Errors 10. Simultaneous Equations Estimation 11. Binary Choice Models and Maximum Likelihood Estimation 12. Models Using Time Series Data 13. Autocorrelation 14. Introduction to Nonstationary Time Series

/pdf/introduction-to-econometrics-50qx7qh4k1.pdf

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Introduction to Econometrics

Introduction to Econometrics