Health and political regimes: Evidence from quantile regression

This study investigates how political regimes affect health conditions such as infant and child mortality rates and life expectancy using data from 180 countries observed between 1960 and 2013. Panel quantile regression is used to examine the effects at different intervals throughout the distribution of health outcomes. The estimation results indicate that democracy has significant positive effects on health outcomes and that its impacts are greater when health outcomes are worse. These results are robust to different democracy and health indices. The effects of different types of democracies and dictatorships are also considered, that is parliamentary, mixed (semi-presidential) and presidential democracies, and civilian, military and royal dictatorships. The parliamentary form of democracy has the largest positive impact on health outcomes at the worst quantile of health outcomes, although the difference in the impacts of the three types of democracies is not necessarily large. Furthermore, all types of dictatorships have a negative impact on health outcomes, with military dictatorship having the worst outcome when health outcomes are worse. Finally, the effects of democratization on health outcomes are significantly positive when the health outcomes are worse.     

Large deviation asymptotics for statistical treatment rules

This note applies large deviation-based optimality theory to evaluate treatment rules for treatment assignment problems. We find nearly optimal treatment rules whose asymptotic maximum large deviation risks can be arbitrary close to the corresponding minimax bounds.     

Conditional empirical likelihood estimation and inference for quantile regression models

This paper considers two empirical likelihood-based estimation, inference, and specification testing methods for quantile regression models. First, we apply the method of conditional empirical likelihood (CEL) by Kitamura et al. [2004. Empirical likelihood-based inference in conditional moment restriction models. Econometrica 72, 1667–1714] and Zhang and Gijbels [2003. Sieve empirical likelihood and extensions of the generalized least squares. Scandinavian Journal of Statistics 30, 1–24] to quantile regression models. Second, to avoid practical problems of the CEL method induced by the discontinuity in parameters of CEL, we propose a smoothed counterpart of CEL, called smoothed conditional empirical likelihood (SCEL). We derive asymptotic properties of the CEL and SCEL estimators, parameter hypothesis tests, and model specification tests. Important features are (i) the CEL and SCEL estimators are asymptotically efficient and do not require preliminary weight estimation; (ii) by inverting the CEL and SCEL ratio parameter hypothesis tests, asymptotically valid confidence intervals can be obtained without estimating the asymptotic variances of the estimators; and (iii) in contrast to CEL, the SCEL method can be implemented by some standard Newton-type optimization. Simulation results demonstrate that the SCEL method in particular compares favorably with existing alternatives.     

Panel Asymptotics and Statistical Decision Theory

This paper develops some applications of asymptotic statistical decision theory in econometrics, focusing on settings where the data are organized into groups or cells with heterogeneous parameters. Even if the groups are of different sizes, local asymptotic normality holds under suitable regularity conditions, and this can greatly simplify analysis of different types of econometric problems. We apply these results to the analysis of treatment assignment rules, and to estimators of cell‐specific parameters that employ shrinkage towards parametric models.     

CROSS‐BORDER M&As AND FIRM VALUE: A COMPARISON OF CHINA‐ AND US‐JAPAN M&As

We examine the effect of M&As conducted by U.S. and Chinese bidders (US‐Japan and China‐Japan M&As) on the stock prices of Japanese targets. We find that both types of M&As tend to increase the stock prices of the Japanese targets and that market reactions are significantly greater for US‐Japan M&As than for China‐Japan M&As. Additionally, capital participation produces greater market reactions to China‐Japan M&As than other structures, while acquisition produces this effect in US‐Japan M&As. Our results are consistent with previous research indicating that market reactions increase for bidders operating in a developed country with high‐quality institutions and corporate governance. (JEL G32, G34)     

Empirical study on the determinants of CO2 emissions: evidence from OECD countries

This article empirically investigates the Environmental Kuznets Curve (EKC) for CO₂ emissions in the cases of 11 OECD countries by taking into account the role of nuclear energy in electricity production. The autoregressive distributed lag approach to cointegration is employed as the estimation method. Our results indicate that energy consumption has a positive impact on CO₂ emissions in most countries in the study. However, the impact of trade is not statistically significant. The results provide evidence for the role of nuclear power in reducing CO₂ emissions only in some countries. Additionally, although the estimated long-run coefficients of income and its square satisfy the EKC hypothesis in Finland, Japan, Korea and Spain, only Finland's EKC turning point is inside the sample period of the study, providing poor evidence in support of the EKC hypothesis.     

Local GMM estimation of time series models with conditional moment restrictions

This paper investigates statistical properties of the local generalized method of moments (LGMM) estimator for some time series models defined by conditional moment restrictions. First, we consider Markov processes with possible conditional heteroskedasticity of unknown forms and establish the consistency, asymptotic normality, and semi-parametric efficiency of the LGMM estimator. Second, we undertake a higher-order asymptotic expansion and demonstrate that the LGMM estimator possesses some appealing bias reduction properties for positively autocorrelated processes. Our analysis of the asymptotic expansion of the LGMM estimator reveals an interesting contrast with the OLS estimator that helps to shed light on the nature of the bias correction performed by the LGMM estimator. The practical importance of these findings is evaluated in terms of a bond and option pricing exercise based on a diffusion model for spot interest rate.     

Recent Political Change in Myanmar and Its Impact on her Economic Growth

The present study estimates the causal effect of a process of political change, namely, a recent constitutional referendum, on economic growth in Myanmar. To analyze the impact of this process, this study compares the trajectories of actual and counterfactual GDP per capita after the referendum using the synthetic control method. We calculate the counterfactual GDP per capita using country‐level panel data from 2002 to 2013, with Myanmar as the treated country and a set of developing countries in East and South Asia, the Pacific and sub‐Saharan Africa as the control group. The results of the synthetic analysis suggest that the recent process of political change in Myanmar had a positive and significant effect on GDP per capita but not on per capita foreign direct investment or trade.     

Testing for non-nested conditional moment restrictions using unconditional empirical likelihood

We propose non-nested hypothesis tests for conditional moment restriction models based on the method of generalized empirical likelihood (GEL). By utilizing the implied GEL probabilities from a sequence of unconditional moment restrictions that contains equivalent information of the conditional moment restrictions, we construct Kolmogorov–Smirnov and Cramér–von Mises type moment encompassing tests. Advantages of our tests over Otsu and Whang’s (2011) tests are: (i) they are free from smoothing parameters, (ii) they can be applied to weakly dependent data, and (iii) they allow non-smooth moment functions. We derive the null distributions, validity of a bootstrap procedure, and local and global power properties of our tests. The simulation results show that our tests have reasonable size and power performance in finite samples.     

Corruption and natural resource rents: evidence from quantile regression

This study examines the impacts of oil rents on corruption for 157 countries. While existing studies have primarily focused on average effects, we employ quantile regression to estimate the effects of natural resource abundance for different corruption levels. We consider the effects of natural resource rents, mainly oil rents and then compare them with those of total and non-oil natural resources rents. The estimation results show that, generally, more oil rents increase corruption. Specifically, impacts are larger in countries with an intermediate level of corruption and smaller in highly corrupt countries. While total resource rents increase corruption significantly, non-oil resource rents do not. This may be due to non-oil resource rent management (mainly inland) being more subject to public scrutiny. Non-oil natural resources are concentrated in the less-developed sub-Saharan African countries, where corruption is prevalent; therefore, the impacts of natural resource rents are unremarkable.