Search results

Identification in a regression discontinuity (RD) design hinges on the discontinuity in the probability of treatment when a covariate (assignment variable) exceeds a known threshold. If the assignment variable is measured with error, however, the discontinuity in the relationship between the probability of treatment and the observed mismeasured assignment variable may disappear. Therefore, the presence of measurement error in the assignment variable poses a challenge to treatment effect identification. This chapter provides sufficient conditions to identify the RD treatment effect using the mismeasured assignment variable, the treatment status and the outcome variable. We prove identification separately for discrete and continuous assignment variables and study the properties of various estimation procedures. We illustrate the proposed methods in an empirical application, where we estimate Medicaid takeup and its crowdout effect on private health insurance coverage.

Productivity growth in Italy has been persistently anemic and lagged that of the euro area over the period 1999–2015, while the indebtedness of its corporate sector increased. Using the ORBIS firm-level database, this chapter studies the long-term impact of persistent corporate-debt accumulation on the productivity growth of Italian firms, and investigates whether total factor productivity (TFP) growth varies with the level of corporate indebtedness. The authors employ a novel estimation technique proposed by Chudik, Mohaddes, Pesaran, & Raissi (2017) to account for dynamics, bi-directional feedback effects, cross-firm heterogeneity, and cross-sectional dependence arising from unobserved common factors (e.g., oil price shocks, labor and product market frictions, and the stance of the global financial cycle). Filtering out the effects of unobserved common factors and controlling for firm-specific characteristics, the authors find significant negative effects of persistent corporate-debt build-up on firms’ TFP growth on average, and weak evidence of a threshold level of corporate debt, beyond which productivity growth drops off significantly. The results have strong policy implications, for example the design of the tax system should discourage persistent corporate-debt accumulation, and effective and timely frameworks to reduce corporate-debt overhangs are essential.

The central bank policy instruments have become less effective in an environment where economies are integrated with sophisticated financial products. We argue that economic stability is a function of interactions between financial and commodity markets. We utilize MGARCH models to identify volatility comovements between these markets in the United States since 2000. Our results suggest that financial markets have strong impacts on prices and volatility in commodity markets which could be due to intertemporal capital mobility. Thus, understanding commodity markets is inseparable from understanding financial market activities, and must now be included in an economic equation to achieve an effective policy.

This paper examines a nonparametric CUSUM-type test for common trends in large panel data sets with individual fixed effects. We consider, as in Zhang, Su, and Phillips (2012), a partial linear regression model with unknown functional form for the trend component, although our test does not involve local smoothings. This conveniently forgoes the need to choose a bandwidth parameter, which due to a lack of a clear and sensible information criteria is difficult for testing purposes. We are able to do so after making use that the number of individuals increases with no limit. After removing the parametric component of the model, when the errors are homoscedastic, our test statistic converges to a Gaussian process whose critical values are easily tabulated. We also examine the consequences of having heteroscedasticity as well as discussing the problem of how to compute valid critical values due to the very complicated covariance structure of the limiting process. Finally, we present a small Monte Carlo experiment to shed some light on the finite sample performance of the test.

With the growth of income at the global level, the World Bank data show that there are rising levels of income disparity across countries, groups, regions and within the countries. This fact otherwise hints at the inter-country divergence in incomes, particularly between the developed and developing countries of the world. This chapter, therefore, attempts to examine the convergence or divergence in credit, GDP and HDI across the 10 selected countries for the period of 1990–2019 applying the neoclassical growth approach and the time series approach. The results of the exercise in line with the neoclassical theories on absolute convergence and sigma convergence show that the countries are unquestionably converging in GDP and HDI with mixed results in case of credit. The results of convergence in GDP and HDI in all the countries and their developed and developing counterparts provide a possible explanation as to why the cross countries’ income inequalities as well as world inequality in income and development are reducing over time. On the other hand, the results of the time series approach display that credit and HDI are converging in both absolute and conditional terms but the countries are converging in conditional terms only for GDP. Thus, the claims of the World Bank are not valid for the selected countries in the chapter, rather, they can be verified by taking other countries and groups into consideration.

This chapter proposes semiparametric estimation of the relationship between growth rate of GDP per capita, growth rates of physical and human capital, labor as well as other covariates and common trends for a panel of 23 OECD countries observed over the period 1971–2015. The observed differentiated behaviors by country reveal strong heterogeneity. This is the motivation behind using a mixed fixed- and random coefficients model to estimate this relationship. In particular, this chapter uses a semiparametric specification with random intercepts and slopes coefficients. Motivated by Lee and Wand (2016), the authors estimate a mean field variational Bayes semiparametric model with random coefficients for this panel of countries. Results reveal nonparametric specifications for the common trends. The use of this flexible methodology may enrich the empirical growth literature underlining a large diversity of responses across variables and countries.

This chapter develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group (MG) estimator, provided the random coefficients are conditionally homoskedastic. The authors consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. The authors apply them to the MG estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and the authors derive limiting distribution of each estimator. As an empirical illustration, the authors consider the effect of police on property crime rates using the US state-level panel data.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.

A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this chapter, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in Austria, and discuss implementation issues that may arise in similar settings, including the use of bandwidth selection algorithms and bias-correction procedures. Although recent developments in nonparametric estimation (Calonico, Cattaneo, & Farrell, 2014; Imbens & Kalyanaraman, 2012) are sometimes interpreted by practitioners as pointing to a default estimation procedure, we show that in any given application different procedures may perform better or worse. In particular, Monte Carlo simulations based on data-generating processes that closely resemble the data from our application show that some asymptotically dominant procedures may actually perform worse than “sub-optimal” alternatives in a given empirical application.

Over the last decade, latent growth modeling (LGM) utilizing hierarchical linear models or structural equation models has become a widely applied approach in the analysis of change. By analyzing two or more variables simultaneously, the current method provides a straightforward generalization of this idea. From a theory of change perspective, this chapter demonstrates ways to prescreen the covariance matrix in repeated measurement, which allows for the identification of major trends in the data prior to running the multivariate LGM. A three-step approach is suggested and explained using an empirical study published in the Journal of Applied Psychology.