Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data

Considerable effort has been exercised in estimating mean returns to education while carefully considering biases arising from unmeasured ability and measurement error. Recent work has investigated whether there are variations from the “mean” return to education across the population with mixed results. We use an instrumental variables estimator for quantile regression on a sample of twins to estimate an entire family of returns to education at different quantiles of the conditional distribution of wages while addressing simultaneity and measurement error biases. We test whether there is individual heterogeneity in returns to education and find that: more able individuals obtain more schooling perhaps due to lower marginal costs and/or higher marginal benefits of schooling and that higher ability individuals (those further to the right in the conditional distribution of wages) have higher returns to schooling consistent with a non-trivial interaction between schooling and unobserved abilities in the generation of earnings. The estimated returns are never lower than 9 percent and can be as high as 13 percent at the top of the conditional distribution of wages but they vary significantly only along the lower to middle quantiles. Our findings may have meaningful implications for the design of educational policies.     

Integrated Conditional Moment testing of quantile regression models

In this paper we propose a consistent test of the linearity of quantile regression models, similar to the Integrated Conditional Moment (ICM) test of Bierens (1982) and Bierens and Ploberger (1997). This test requires re-estimation of the quantile regression model by minimizing the ICM test statistic with respect to the parameters. We apply this ICM test to examine the correctness of the functional form of three median regression wage equations.     

Asymmetric inflation dynamics: Evidence from quantile regression analysis

This paper applies the regression quantile approach developed by Koenker and Xiao (2004) to investigate the dynamic behavior of inflation in 12 OECD countries. By analyzing the behavior in a wide range of quantiles, this method allows us to quantify the influence of various sizes of shocks that hit the inflation, and is able to capture possible asymmetric adjustment of the inflation towards to its long-run equilibrium. It therefore sheds new lights on the inflation dynamics compared with the conventional unit root methodologies. Our results suggest that generally, the inflation rates are not only mean-reverting but also exhibit asymmetries in their dynamic adjustments, in which large negative shocks tend to induce strong mean reversion, and on the contrary, large positive shocks do not. Policy implications related to the empirical findings are also provided.     

Modelling the effect of the geographical environment on Islamic banking performance: A panel quantile regression analysis

While studies have focused on Islamic banking, research on the effect of the geographical environment on Islamic banks is scarce. We investigate this issue by using daily data on 12 Islamic banks in four regions (Africa, Asia, Europe, and the United States) from July 2007 to April 2016. We apply different methodological approaches (principal component analysis, panel data tests, and quantile regression). First, the principal component analysis shows that the performance of Islamic banks varies among regions. Second, the linear panel regression highlights that the geographical environment positively and significantly affects Islamic banking, suggesting the importance of externality effects. Finally, the environmental effect seems to vary with quantiles (positive effect for the lowest quantile versus negative effect for the highest quantile). This quantile specification points to nonlinearity in the environment–Islamic bank performance relationship, reflecting a time-varying discipline imposed by the Sharia board (Islamic Law). This finding helps better explain the main difference between Islamic banks in the East (Africa and Asia) and those in the West (Europe and the United States) and also enables investors to adjust their portfolio choices when considering the products of Islamic banks according to regional specificities.     

The public-private sector wage gap in Zambia in the 1990s: A quantile regression approach

We investigate the determinants of wages in Zambia and based on the quantile regression approach, we analyze how their effects differ at different points in the wage distribution and over time. We use three cross-sections of Zambian household data from the early nineties, which was a period of economic transition, because items as privatization and deregulation were on the political agenda. The focus is placed on the public-private sector wage gap, and the results show that this gap was relatively favorable for the low-skilled and less favorable for the high-skilled. This picture was further strengthened during the period 1991–1996.     

Nonparametric quantile regression analysis of R&D-sales relationship for Korean firms

This paper applies the nonparametric quantile regression estimation procedure to the analysis of the innovation-firm size relationship using Korean manufacturing firms data. Due to the high asymmetric distribution of R&D expenditure, the mean regression does not capture properly the stylized facts of R&D behavior; hence it underestimates the sales elasticity. Comparing the parametric estimates and nonparametric estimates allows us to see that there exists a nonlinear relationship in innovative activity and sales. Dividing the data into three groups according to the sales volume, the elasticity in the medium-sized firms is the biggest for scientific firms. This result conforms that the findings of Scherer (1965) coincide with findings from Korean manufacturing firms data in the sense that R&D expenditure tends to increase faster than firm size with size up to a point and then more slowly among larger firms. For the non-scientific firms, it steadily increases showing increasing returns to scale in innovative activity in large firms.     

Investor sentiment and the prediction of stock returns: a quantile regression approach

We employ quantile regression to provide a detailed picture of the stock return forecasting ability of investor sentiment. We find that investor sentiment predicts aggregate stock returns at lower quantiles. However, the forecasting power is lost at upper quantiles. The results are robust after controlling for a comprehensive set of macroeconomic and financial predictors and for characteristic portfolios. We also show that investor sentiment consists mainly of cash flow news and contains little information about discount rate news. The ability to forecast cash flows increases gradually from the lower quantiles to upper quantiles. Our results do not support that the ability of investor sentiment to predict stock returns comes from a rational forecast of future cash flows.     

Public-private sector wage differentials in Germany: Evidence from quantile regression

This paper measures and decomposes the differences in earnings distributions between public sector and private sector employees in Germany for the years 1984–2001. Oaxaca decomposition results suggest that conditional wages are higher in the public sector for women but lower for men. Using the quantile regression decomposition technique proposed by Machado and Mata (2004), we find that the conditional distribution of wages is more compressed in the public sector. At the low end of wages, differences in characteristics explain less than the raw wage gap when it is the opposite at high wages. Separate analyses by work experience and educational groups reveal that the most experienced employees and those with basic schooling do best in the public sector. All these results are stable over the 80s and 90s.I thank Michael Lechner and the Centre for European Economic Research (ZEW), Mannheim, for letting me work with the full sample of the German Socio-Economic Panel (GSOEP). I am grateful to an anonymous referee and particularly to the editor, Bernd Fitzenberger, for providing me detail comments and suggestions that have significantly improved the paper. I have also benefited from discussions with Michael Lechner and Ruth Miquel.First version received: April 2002/Final version received: June 2004     

Portfolio style: Return-based attribution using quantile regression

Return-based classification identifies a portfolio's style signature in the time series of its returns. Detection is based on a regression of portfolio returns on returns of factor mimicking indices. The method is easy to apply and does not require information about portfolio composition. Classification using least squares means that style is determined by the way factor exposure influences expected returns. We introduce regression quantiles as a complement to the standard analysis. The regression quantiles extract additional information from the time series of returns by identifying the way style affects returns at places other than the expected value. This allows discrimination among portfolios that would be otherwise judged equivalent based on conditional expectations. It also provides direct information about the impact of style on the tails of the conditional return distribution. Simple examples are presented to illustrate regression quantile classification.     

Exploring the effect of diversification strategy on R&D intensity using quantile regression: evidence from France

This study examines the relationship between firm diversification strategy and R&D intensity for a sample of large French companies between 2008 and 2012. Applying quantile regression, we provide evidence that the choice of diversification affects R&D intensity in a differentiated way. The results indicate that a low level of diversification (below the twentieth quantile) has no significant impact on R&D intensity. Conversely, a moderate or high level of diversification has a negative and significant impact on R&D intensity. These findings suggest that R&D intensity seems to be significantly higher in related-business firms than in unrelated-business firms.     

Equity market information and credit risk signaling: A quantile cointegrating regression approach

Investigating linkages between credit and equity markets, we consider daily aggregate U.S. CDS spreads as well as well-chosen equity market and implied volatility indexes over ten years. We describe such robust (to spurious correlation) relationship with the quantile cointegrating regression approach. Such approach handles extreme quantiles/CDS values and their behavior with respect to the equity market's influence. Heteroskedastic patterns such as time-varying variance, but also autocorrelation, skewness and leptokurtosis are captured. Thus, the sensitivity of aggregate CDS spreads to equity market price and volatility channels is accurately measured across quantiles and spreads. Such quantile-dependent sensitivity exhibits asymmetric responses to equity market shocks. A sub-period analysis investigates potential regime shifts in estimated quantile cointegrating regressions. Quantile cointegrating coefficients vary over time and quantiles, and exhibit different magnitudes across sub-periods and spreads. Therefore, the relationship is unstable over time. We also propose a scenario analysis and risk signaling application for credit risk management prospects. Under specific risk levels, credit risky situations are described conditional on the equity market's information over time, and related expected aggregate CDS spreads are computed. Estimated conditional quantiles/CDS spreads act as credit alert triggers.     

Revisiting growth empirics based on IV panel quantile regression

We analyse the well-known issue of economic growth convergence using quantile regression. Most previous studies have used a least squares (LS) method or variation, which focuses on the issue only at the mean of the growth rate. Therefore, such results cannot provide a satisfactory answer to what can happen if the growth rate is far from the conditional mean level. For example, we consider the following question: do we still have economic growth convergence or is the convergence speed changed in a low growth period such as the ‘Great Recession,’ that started in 2008? We propose using instrumental variable panel quantile regression to answer this question. Our empirical findings demonstrate that economic growth convergence occurs at all quantiles over the entire conditional distribution, but that the convergence speed does depend on quantiles; the convergence speed is much higher when the GDP growth rate is at either high or low quantiles.     

A selective review of the first 20 years of instrumental variables models in health-services research and medicine

Abstract

Background:

The method of instrumental variables (IV) is useful for estimating causal effects. Intuitively, it exploits exogenous variation in the treatment, sometimes called natural experiments or instruments. This study reviews the literature in health-services research and medical research that applies the method of instrumental variables, documents trends in its use, and offers examples of various types of instruments.     

Political stability and growth: An application of dynamic GMM and quantile regression

This paper studies the effect of political stability on economic growth by taking 120 developing countries over the period of 1996–2014. We apply relatively advanced dynamic two step system-GMM and quantile regression. Political stability is found to be a key determinant of economic growth. More importantly, political instability (or risk) is found to be higher in the OIC countries and is a deterrent to economic growth. Also, for the lower and middle income OIC countries, political instability appears to affect economic growth more severely perhaps due to the absence of strong economic and political institutions. Moreover, political instability is also found to be significantly higher in the oil-dependent OIC countries. Notably, political instability is likely to affect growth through the channels of investment and human capital accumulation in the developing countries. Finally, the impact of political stability and political instability on growth is found to be equally distributed across the OIC countries with higher or lower growth level. Therefore, the development of political and economic institutions along with human capital development is recommended for all the developing countries in general and the OIC countries in particular.     

Trade credit in SMEs: a quantile regression approach

This article analyses the determinants of the trade credit in Spanish manufacturing small and medium-sized enterprises (SMEs) from a new perspective. Specifically, we focus on the relationship between trade credit and other relevance financial resources: bank credit and self-financing. For the first time in the literature, a quantile regression approach is used, which takes into account the heterogeneity of firms in different quantiles of trade credit distribution. Our results show that the relationship between trade credit and other financial sources presents dissimilarities, including differences in sign, on SMEs with different degrees of trade credit. Our findings help to clarify the confusing results achieved in previous research on this topic.     

Engineering robust instruments for GMM estimation of panel data regression models with errors in variables: a note

Econometricians have long recognized the need to account in some way for measurement errors, specification errors and endogeneity to ensure that the ordinary least squares estimator is consistent. This article introduces a new generalized method of moments estimator that relies on robust instruments to estimate panel data regression models containing errors in variables. We show how this GMM approach can be generalized for the panel data framework using higher moments and cumulants as instruments. The new instruments, engineered for greater robustness, are proposed to tackle the pervasive problem of weak instruments.     

How credible is inflation targeting in Asia? A quantile unit root perspective

This article examines the dynamic behavior of the inflation rate for eight Asian countries using a quantile unit root test. We advocate a three-way definition of inflation targeting based on perfect, imperfect and zero credibility and advance the analysis by incorporating a fully-fledged adoption of inflation targeting. In doing so, we offer new insights by showing that the credibility of inflation targeting and the alternative monetary policy frameworks in Asia are imperfect, except for Malaysia and South Korea under a fully-fledged adoption of inflation targeting. In contrast to past studies that focus on the mean-reversion in inflation rates, we also consider trend-reversion and find that Asian inflation targeting countries have been building up their monetary policy credibility more than the non- inflation targeting countries in terms of a faster rate of decline in inflation rate changes. Our results generally indicate the presence of mean reversion at the lower quantiles only. Where stationarity is present, we find evidence of a varied speed of adjustment process across the quantiles. Finally, we determine the threshold levels whereby inflation becomes stationary and demonstrate that Asian inflation rates generally display stationary behavior during periods of inflation declining or slowing down.     

The absolute health income hypothesis revisited: a semiparametric quantile regression approach

This paper uses the 1998–1999 Canadian National Population Health Survey data to examine the health–income relationship that underlies the absolute income hypothesis. To allow for nonlinearity and data heterogeneity, we use a partially linear semiparametric quantile regression model. The “absolute income hypothesis” is partially true; the negative aging effects appear more pronounced for the ill-healthy population than for the healthy population and when annual income is below 40,000 Canadian dollars. We would like to thank two anonymous referees and Baldev Raj, the editor, for useful and constructive comments and suggestions. The views expressed in this article are those of the authors and do not necessarily reflect the views of Statistics Canada. Both authors would also like to acknowledge financial support from SSHRC of Canada.     

Spillovers from the USA to stock markets in Asia: a quantile regression approach

This article analyses return spillovers from the USA to stock markets in Asia by means of quantile regressions. Traditional studies consider spillovers as effects of the conditional means of foreign returns onto the conditional means of chronologically succeeding domestic markets’ returns. We, by contrast, study the full range of quantiles of the conditional distribution of the domestic markets’ returns. This enables us to document the detailed structure of spillovers across return quantiles. Generally, we find spillovers from the USA to Asia to be negative. Specifically, however, we reveal an asymmetric structure of spillovers with an increasing negative magnitude from lower to upper return quantiles. Theoretically, this pattern is consistent with an asymmetric overreaction of traders in Asia to news from the US market. Extensions from the baseline model further suggest the presence of contagion throughout the financial crisis of 2007–2008 as well as of calm-down effects over weekends.     

Understanding the gender wage-gap differential between the public and private sectors in Italy: A quantile approach

This paper analyzes the gender-based wage gaps across the wage distribution in the private and public sectors in Italy for the years 2005–2010. We use quantile regression methods to estimate and decompose the wage gap at all wage levels and propose a two-step procedure that relies on a novel approach to estimating fixed effects quantile regressions. The method's main advantage is that it allows the employment sector's marginal effect on wages at various points of the distribution to be estimated, while accounting for both observable and time-invariant unobservable factors. The new method stresses important differences with respect to standard decomposition analyses and amplifies the differences in the two sectors' wage-setting mechanisms. When the estimation is net of individual heterogeneity, the gender-based wage gap decreases in both sectors and there is evidence of a glass ceiling effect, but only in the public sector. Economic grounds are provided.