Nonparametric estimation in case of endogenous selection

This paper addresses the problem of estimation of a nonparametric regression function from selectively observed data when selection is endogenous. Our approach relies on independence between covariates and selection conditionally on potential outcomes. Endogeneity of regressors is also allowed for. In the exogenous and endogenous case, consistent two-step estimation procedures are proposed and their rates of convergence are derived. Pointwise asymptotic distribution of the estimators is established. In addition, bootstrap uniform confidence bands are obtained. Finite sample properties are illustrated in a Monte Carlo simulation study and an empirical illustration.     

金融创新如何影响了中国货币需求的流动性陷阱效应——来自金融理财产品的证据

本文利用变系数状态空间模型,考察了近年来中国以互联网金融理财产品和银行理财产品为代表的金融创新对货币流动性陷阱效应的影响,主要结论为:(1)货币需求流动性陷阱在中国的确比较显著地存在,其作用机制基本符合投资组合假说,但显著性与货币层次划分密切相关,M2和M1相对显著,而M0则不显著;(2)与成熟市场经济国家不同,金融创新强化而非弱化了货币需求的流动性陷阱效应,这可能与中国金融产品仍不够丰富有关,但互联网金融对货币投机性需求因“功能主序效应”而对流动性陷阱影响不显著,银行理财产品虽然影响显著,但对不同层次货币表现出“结构差异效应”;(3)利率市场化改革增强了金融创新对流动性陷阱效应的边际影响,其作用机制是利率市场化改革弱化了金融市场的分割,强化了利率在微观经济主体金融资产组合中的信号作用。本文的政策含义是,面对当前金融风险仍较高和金融稳定任务仍较重的现实,低利率货币政策的效果有限,金融危机期间欧美所采用的数量型非常规货币政策的借鉴意义须充分重视,且货币政策操作应当注意与中国金融结构变迁阶段特征相耦合。     

Fertility and labor supply: evidence from the One-Child Policy in China

This study provides new evidence on on the causal effect of fertility on maternal labor supply in rural China, using the fact that in some parts of rural China couples are allowed to have a second child if their firstborn is female. Estimates show that a second child reduces maternal labor force participation by 4.6 percentage points, labor supply intensity (hours worked conditional on employment) by 1.4 h per week and monthly income by 54.5 Chinese Yuan (18.7 percent). Further, the labor supply of mothers whose husbands are rural-to-urban migrants is the most sensitive to having an additional child, likely because they have more difficulty balancing farming and childcare. Conversely, labor supply is not reduced by fertility for mothers living in three-generation families, most likely because grandparents can provide both time and money to help with childcare.     

Exponentially tilted likelihood inference on growing dimensional unconditional moment models

Growing-dimensional data with likelihood function unavailable are often encountered in various fields. This paper presents a penalized exponentially tilted (PET) likelihood for variable selection and parameter estimation for growing dimensional unconditional moment models in the presence of correlation among variables and model misspecification. Under some regularity conditions, we investigate the consistent and oracle properties of the PET estimators of parameters, and show that the constrained PET likelihood ratio statistic for testing contrast hypothesis asymptotically follows the chi-squared distribution. Theoretical results reveal that the PET likelihood approach is robust to model misspecification. We study high-order asymptotic properties of the proposed PET estimators. Simulation studies are conducted to investigate the finite performance of the proposed methodologies. An example from the Boston Housing Study is illustrated.     

Rainy day politics. An instrumental variables approach to the effect of parties on political outcomes

Scholars have grappled with the question of how parties affect policy. Here I propose and test an instrumental variable approach using rainfall. In Norwegian municipal elections, potential left wing voters are likely to abstain from voting with election day rain, whereas the opposite holds for right-wingers. Then rainfall provides an exogenous source of variation, and hence an instrument, for the party composition of the municipal council. A strengthening of the right wing parties due to rainfall shifts expenditures toward education, but reduces total spending. This also shows that political competition does not drive party platforms to converge.     

Stock market volatility: Identifying major drivers and the nature of their impact

Financial-market risk, commonly measured in terms of asset-return volatility, plays a fundamental role in investment decisions, risk management and regulation. In this paper, we investigate a new modeling strategy that helps to better understand the forces that drive market risk. We use componentwise gradient boosting techniques to identify financial and macroeconomic factors influencing volatility and to assess the specific nature of their influence. Componentwise boosting is capable of producing parsimonious models from a, possibly, large number of predictors and—in contrast to other related techniques—allows a straightforward interpretation of the parameter estimates.Considering a wide range of potential risk drivers, we apply boosting to derive monthly volatility predictions for the equity market represented by S&P 500 index. Comparisons with commonly-used GARCH and EGARCH benchmark models show that our approach substantially improves out-of-sample volatility forecasts for short- and longer-run horizons. The results indicate that risk drivers affect future volatility in a nonlinear fashion.     

The impact of child labor on children's educational performance: Evidence from rural Vietnam

In 1998, nearly one-third of Vietnamese children engaged in non-housework labor supply, 95% of these working children residing in rural areas. This paper investigates the impact of child labor on children's educational outcomes in rural Vietnam using the 1998-Vietnam Living Standard Survey. The paper finds that child labor lowers children's academic performance and the negative impact is bigger for girls.     

Different from or similar to neighbors? An investigation of hotels' strategic distances

This paper examines how conformity and differentiation impacts hotel performance. It evaluates strategic outcomes of hotel entrepreneurs' choice of geographic location, capacity, and quality. Using instrumental variable (IV) estimation, we find that the distribution of similar competitors affects hotel performance and performance risk. Specifically, results show that choosing a similar capacity (conformity) to competitors may lead to higher and more stable performance. The results also reveal that locating geographically close to competitors or choosing a similar quality of product to competitors (both conformity strategies) can increase hotel performance but also increases performance risk. Additionally, when mixing conformity and differentiation strategies in terms of location, capacity, and quality selection, a higher dispersion of differentiation (i.e., highly differentiated in one product dimension while conforming to others in all remaining product dimensions) increases both performance and performance risk. Findings of this study can aid entrepreneurs in improving their strategic decisions and product positioning.     

Cross-hedging minimum return guarantees: Basis and liquidity risks

We reveal pitfalls in the hedging of insurance contracts with a minimum return guarantee on the underlying investment, e.g. an external mutual fund. We analyze basis risk entailed by hedging the guarantee with a dynamic portfolio of proxy assets for the funds. We also take account of liquidity risk which arises since the insurer may need to advance funds for performing the hedge. Based on a least-squares Monte Carlo simulation, we study the economic implications of basis and liquidity risks. We demonstrate that both risks may be surprisingly high and show how the design of the contract and the hedging strategy may help to alleviate them.     

Predicting the long-term stock market volatility: A GARCH-MIDAS model with variable selection

We consider a GARCH-MIDAS model with short-term and long-term volatility components, in which the long-term volatility component depends on many macroeconomic and financial variables. We select the variables that exhibit the strongest effects on the long-term stock market volatility via maximizing the penalized log-likelihood function with an Adaptive-Lasso penalty. The GARCH-MIDAS model with variable selection enables us to incorporate many variables in a single model without estimating a large number of parameters. In the empirical analysis, three variables (namely, housing starts, default spread and realized volatility) are selected from a large set of macroeconomic and financial variables. The recursive out-of-sample forecasting evaluation shows that variable selection significantly improves the predictive ability of the GARCH-MIDAS model for the long-term stock market volatility.