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

This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical researchers. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental" design, and summarizes different ways (with their advantages and disadvantages) of estimating RD designs and the limitations of interpreting these estimates. Concepts are discussed using examples drawn from the growing body of empirical research using RD.

Regression Discontinuity Designs in Economics

Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades, much research has been done on the econometric and statistical analysis of such causal effects. This recent theoreti- cal literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization, and other areas of empirical microeconomics. In this review, we discuss some of the recent developments. We focus primarily on practical issues for empirical research- ers, as well as provide a historical overview of the area and give references to more technical research.

/pdf/recent-developments-in-the-econometrics-of-program-42vjewfvjw.pdf

Recent developments in the econometrics of program evaluation

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

https://steadystate.org/wp-content/uploads/Stern_KuznetsCurve.pdf

The Rise and Fall of the Environmental Kuznets Curve

https://www.nber.org/system/files/working_papers/w17095/w17095.pdf

Empirical Implementation of Nonparametric First-Price Auction Models

While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, non-classical measurement error can lead to estimates which are biased and inference which lacks power. Here, we consider a particular type of non-classical measurement error: skewed errors. Unfortunately, skewed measurement error is likely to be a relatively common feature of many outcomes of interest in political science research. This study highlights the bias that can result even from relatively "small" amounts of skewed measurement error, particularly if the measurement error is heteroskedastic. We also assess potential solutions to this problem, focusing on the stochastic frontier model and nonlinear least squares. Simulations and three replications highlight the importance of thinking carefully about skewed measurement error, as well as appropriate solutions.

/pdf/accounting-for-skewed-or-one-sided-measurement-error-in-the-1z3lmwxuly.pdf

Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable

http://faculty.smu.edu/millimet/pdf/meas.pdf

Multilateral environmental agreements and the WTO

The concept of an environmental Gini coefficient has been used recently by several researchers. In line with Paglin's historical critique of the income Gini coefficient, an environmental Paglin-Gini is proposed and estimated, more carefully considering the notion of ‘perfect environmental inequality.’

An environmental Paglin-Gini

We assess the causal effect of private tutoring on the probability of university placement in Turkey. We find that tutoring increases the probability of being placed in a university when non-random selection is ignored. Moreover, among those utilizing private tutoring, greater expenditure on tutoring is also positively associated with university placement. However, we find evidence of positive selection into tutoring, but negative selection into greater expenditures among those receiving tutoring. Accounting for this pattern of non-random selection, we conclude that private tutoring has a negative causal effect on university placement overall, but conditional on receiving any tutoring, spending more on tutoring has a positive causal effect on university placement.

Daniel L. Millimet

Papers

Empirical Implementation of Nonparametric First-Price Auction Models

Accounting for Skewed or One-Sided Measurement Error in the Dependent Variable

Multilateral environmental agreements and the WTO

An environmental Paglin-Gini

Does Private Tutoring Payoff