Volume 13, Issue 1

This study applies the date-stamping methodologies for explosive behaviors proposed in the seminal work of Phillips, P. C. B., and J. Yu. (2011. “Dating the Timeline of Financial Bubbles during the Subprime Crisis.” Quantitative Economics 2 (3): 455–91), Phillips, P. C. B., S. Shi, and J. Yu. (2015a. “Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500.” International Economic Review 56 (4): 1043–78), and Phillips, P. C. B., S. Shi, and J. Yu. (2015b. “Testing for Multiple Bubbles: Limit Theory of Real Time Detectors.” International Economic Review 56 (4): 1079–134) to a large dimensional factor model. To this end, we compare two methods of identifying common and idiosyncratic components: the Panel Analysis of Nonstationarity in Idiosyncratic and Common Components (PANIC) method by Bai, J., and S. Ng. (2004. “A Panic Attack on Unit Roots and Cointegration.” Econometrica 72 (4): 1127–77) and the Cross-Sectional regression (CS) method by Yamamoto, Y., and T. Horie. (2022. “A Cross-Sectional Method for Right-Tailed PANIC Tests under a Moderately Local to Unity Framework.” Econometric Theory (forthcoming)). We show that, when the explosive behavior lies only in the common component, the origination and termination dates are precisely estimated by either method. However, when the explosive behaviors exist in idiosyncratic components, only the CS method can detect them. We apply our method to the U.S. state-level real house price indices. We find that the 2000s boom was driven by not only the national bubble factors but also local components, while the 2010s onward expansion is dominated by the effect of national components.

We study the Feasible Generalized Least-Squares (FGLS) estimation of the parameters of a linear regression model in the presence of heteroskedasticity of unknown form in the errors. We suggest a Lasso based procedure to estimate the skedastic function of the residuals. The advantage of using Lasso is that it can handle a large number of potential covariates, yet still yields a parsimonious specification. Using extensive simulation experiments, we show that our suggested procedure always provide some improvements in the precision of the parameter of interest (lower Mean-Squared Errors) when heteroskedasticity is present and is equivalent to OLS when there is none. It also performs better than previously suggested procedures. Since the fitted value of the skedastic function falls short of the true specification, we form confidence intervals using a bias-corrected version of the usual heteroskedasticity-robust covariance matrix estimator. These have the correct size and substantially shorter length than when using OLS. Our method is applicable to both cross-section (with a random sample) and time series models, though here we concentrate on the former.

This paper proposes a novel estimator for nonparametric instrumental regression while controlling for additive two-way fixed effects. In particular, the Landweber–Fridman regularization, to overcome the ill-posed inverse problem in the nonparametric instrumental regression procedure, is combined with the local-within two-ways fixed effect estimator presented by Lee, Y., D. Mukherjee, and A. Ullah. (2019. “Nonparametric Estimation of the Marginal Effect in Fixed-Effect Panel Data Models.” Journal of Multivariate Analysis 171: 53–67). Compared to other estimators in this context, an appealing feature is its flexible applicability with respect to different panel model specifications, i.e. models comprising either individual, temporal, or two-way fixed effects. The estimator’s performance is tested on simulated data, where a Monte Carlo study reveals good finite sample behaviour. Confidence intervals are provided by applying the wild bootstrap.

We investigate the properties of a systematic bias that arises in the synthetic control estimator in panel data settings with finite pre-treatment periods, offering intuition and guidance to practitioners. The bias comes from matching to idiosyncratic error terms (noise) in the treated unit and the donor units’ pre-treatment outcome values. This in turn leads to a biased counterfactual for the post-treatment periods. We use Monte Carlo simulations to evaluate the determinants of the bias in terms of error term variance, sample characteristics and DGP complexity, providing guidance as to which situations are likely to yield more bias. We also offer a procedure to reduce the bias using a direct computational bias-correction procedure based on re-sampling from a pilot model that can reduce the bias in empirically feasible implementations. As a final potential solution, we compare the performance of our corrections to that of an Interactive Fixed Effects model. An empirical application focused on trade liberalization indicates that the magnitude of the bias may be economically meaningful in a real world setting.

We investigate whether receiving health information changes human behavior by using a novel approach to inference in the fuzzy regression discontinuity design. The approach is robust to the strength of identification and allows for mean squared error optimal bandwidths as well as undersmoothing. It is based on the Anderson-Rubin test in the instrumental variable literature augmented with either robust bias correction or critical value adjustment. We find that the resulting confidence sets of the treatment effect are mostly wide or even unbounded. These findings indicate that we could not rule out most magnitudes of behavior change, including zero and non-zero ones.

This paper describes a simple and interesting application of structural equation modeling for a single lecture in an undergraduate econometrics course to introduce students to the concept of using data to recover latent variables. The application centers around using hourly observations on ride wait times at Disney’s Magic Kingdom to infer how crowded it is at the theme park. Pedagogically, the material is presented in the context of the linear regression model, so the discussion works to enhance students’ understanding of core material, not to introduce new disparate methods. The application provides interesting economic-based insights, like which ride’s wait times are categorically most informative about how crowded it is at the park.

When a sample combines data from two or more groups, multivariate regression yields a matrix-weighted average of the group-specific coefficient vectors. However, it is possible that the weighted average of a specific coefficient falls outside the range of the group-specific coefficients, and it may even have a different sign compared to both group-level coefficients, a manifestation of Simpson’s paradox. The result of the combined regression is then prone to misinterpretation. The purpose of this paper is to raise awareness of this problem and to state conditions under which such non-convex weighting or sign reversal can arise, for a model with two regressors and two groups. Two illustrative examples, an investment equation estimated with panel data, and a cross-sectional earnings equation for men and women, highlight the relevance of these findings for applied work.

In this paper, we review several estimators of the average treatment effect (ATE) that belong to three main groups: regression, weighting and doubly robust methods. We unify the exposition of these estimators within an M-estimation framework and we derive their variance estimators from the sandwich form variance-covariance matrix of the M-Estimator. Additionally, we re-estimate the causal return to higher education on earnings by the reviewed methods using the rich dataset provided by the British National Child Development Study (NCDS) as an empirical illustration.