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Quantile Regression (quantile + regression)

Distribution by Scientific Domains

Business, Economics, Finance and Accounting	47%
Mathematics and Statistics	16%
Earth and Environmental Science	14%
Medical Sciences	9%
Humanities and Social Sciences	7%
Life Sciences	4%

Distribution within Business, Economics, Finance and Accounting

General & Introductory Economics	48%
General & Introductory Finance & Investments	14%
Agricultural Economics & Resource Management	8%

Terms modified by Quantile Regression

quantile regression analysis

Selected Abstracts

Quantile Regression by R. Koenker

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2007
Andreas Karlsson
No abstract is available for this article. [source]

Characterizing Waiting Room Time, Treatment Time, and Boarding Time in the Emergency Department Using Quantile Regression

ACADEMIC EMERGENCY MEDICINE, Issue 8 2010
Ru Ding MS
ACADEMIC EMERGENCY MEDICINE 2010; 17:813,823 © 2010 by the Society for Academic Emergency Medicine Abstract Objectives:, The objective was to characterize service completion times by patient, clinical, temporal, and crowding factors for different phases of emergency care using quantile regression (QR). Methods:, A retrospective cohort study was conducted on 1-year visit data from four academic emergency departments (EDs; N = 48,896,58,316). From each ED's clinical information system, the authors extracted electronic service information (date and time of registration; bed placement, initial contact with physician, disposition decision, ED discharge, and disposition status; inpatient medicine bed occupancy rate); patient demographics (age, sex, insurance status, and mode of arrival); and clinical characteristics (acuity level and chief complaint) and then used the service information to calculate patients' waiting room time, treatment time, and boarding time, as well as the ED occupancy rate. The 10th, 50th, and 90th percentiles of each phase of care were estimated as a function of patient, clinical, temporal, and crowding factors using multivariate QR. Accuracy of models was assessed by comparing observed and predicted service completion times and the proportion of observations that fell below the predicted 10th, 50th, and 90th percentiles. Results:, At the 90th percentile, patients experienced long waiting room times (105,222 minutes), treatment times (393,616 minutes), and boarding times (381,1,228 minutes) across the EDs. We observed a strong interaction effect between acuity level and temporal factors (i.e., time of day and day of week) on waiting room time at all four sites. Acuity level 3 patients waited the longest across the four sites, and their waiting room times were most influenced by temporal factors compared to other acuity level patients. Acuity level and chief complaint were important predictors of all phases of care, and there was a significant interaction effect between acuity and chief complaint. Patients with a psychiatric problem experienced the longest treatment times, regardless of acuity level. Patients who presented with an injury did not wait as long for an ED or inpatient bed. Temporal factors were strong predictors of service completion time, particularly waiting room time. Mode of arrival was the only patient characteristic that substantially affected waiting room time and treatment time. Patients who arrived by ambulance had shorter wait times but longer treatment times compared to those who did not arrive by ambulance. There was close agreement between observed and predicted service completion times at the 10th, 50th, and 90th percentile distributions across the four EDs. Conclusions:, Service completion times varied significantly across the four academic EDs. QR proved to be a useful method for estimating the service completion experience of not only typical ED patients, but also the experience of those who waited much shorter or longer. Building accurate models of ED service completion times is a critical first step needed to identify barriers to patient flow, begin the process of reengineering the system to reduce variability, and improve the timeliness of care provided. [source]

On the Quantile Regression Based Tests for Asymmetry in Stock Return Volatility

ASIAN ECONOMIC JOURNAL, Issue 2 2002
Beum-Jo Park
This paper attempts to examine whether the asymmetry of stock return volatility varies with the level of volatility. Thus, quantile regression based tests (,-tests) are presupposed. These tests differ from the diagnostic tests introduced by Engle and Ng (1993) insofar as they can provide a complete picture of asymmetries in volatility across quantiles of variance distribution and, in case of non-normal errors, they have improved power due to their robustness against non-normality. A small Monte Carlo evidence suggests that the Wald and likelihood ratio (LR) tests out of ,-tests are reasonable, showing that they outperform the Lagrange multiplier (LM) test based on least squares residuals when the innovations exhibit heavy tail. Using the normalized residuals obtained from AR(1)-GARCH(1, 1) estimation, the test results demonstrated that only the TOPIX out of six stock-return series had asymmetry in volatility at moderate level, while all stock return series except the FAZ and FA100 had more significant asymmetry in volatility at higher levels. Interestingly, it is clear from the empirical findings that, like hypothesis of leverage effects, volatility of the TOPIX, CAC40, and, MIB tends to respond significantly to extremely negative shock at high level, but is not correlated with any positive shock. These might be valuable findings that have not been seriously considered in past research, which has focussed only on mean level of volatility. [source]

Bayesian Quantile Regression for Longitudinal Studies with Nonignorable Missing Data

BIOMETRICS, Issue 1 2010
Ying Yuan
Summary We study quantile regression (QR) for longitudinal measurements with nonignorable intermittent missing data and dropout. Compared to conventional mean regression, quantile regression can characterize the entire conditional distribution of the outcome variable, and is more robust to outliers and misspecification of the error distribution. We account for the within-subject correlation by introducing a,,2,penalty in the usual QR check function to shrink the subject-specific intercepts and slopes toward the common population values. The informative missing data are assumed to be related to the longitudinal outcome process through the shared latent random effects. We assess the performance of the proposed method using simulation studies, and illustrate it with data from a pediatric AIDS clinical trial. [source]

Relating streamflow characteristics to specialized insectivores in the Tennessee River Valley: a regional approach,

ECOHYDROLOGY, Issue 4 2008
Rodney R. Knight
Abstract Analysis of hydrologic time series and fish community data across the Tennessee River Valley identified three hydrologic metrics essential to habitat suitability and food availability for insectivorous fish communities in streams of the Tennessee River Valley: constancy (flow stability or temporal invariance), frequency of moderate flooding (frequency of habitat disturbance), and rate of streamflow recession. Initial datasets included 1100 fish community sites and 300 streamgages. Reduction of these datasets to sites with coexisting data yielded 33 sites with streamflow and fish community data for analysis. Identification of critical hydrologic metrics was completed using a multivariate correlation procedure that maximizes the rank correlation between the hydrologic metrics and fish community resemblance matrices. Quantile regression was used to define thresholds of potential ranges of insectivore scores for given values of the hydrologic metrics. Increased values of constancy and insectivore scores were positively correlated. Constancy of streamflow maintains wetted perimeter, which is important for providing habitat for fish spawning and increased surface area for invertebrate colonization and reproduction. Site scores for insectivorous fish increased as the frequency of moderate flooding (3 times the median annual streamflow) decreased, suggesting that insectivorous fish communities respond positively to less frequent disturbance and a more stable habitat. Increased streamflow recession rates were associated with decreased insectivore scores. Increased streamflow recession can strand fish in pools and other areas that are disconnected from flowing water and remove invertebrates as food sources that were suspended during high-streamflow events. Copyright © 2008 John Wiley & Sons, Ltd. [source]

CEO Pay-For-Performance Heterogeneity Using Quantile Regression

FINANCIAL REVIEW, Issue 1 2010
Kevin F. Hallock
G3; J33; M52 Abstract We provide some examples of how quantile regression can be used to investigate heterogeneity in pay-firm size and pay-performance relationships for U.S. CEOs. For example, do conditionally (predicted) high-wage managers have a stronger relationship between pay and performance than conditionally low-wage managers? Our results using data over a decade show, for some standard specifications, there is considerable heterogeneity in the returns-to-firm performance across the conditional distribution of wages. Quantile regression adds substantially to our understanding of the pay-performance relationship. This heterogeneity is masked when using more standard empirical techniques. [source]

The growth of family farms in Hungary

AGRICULTURAL ECONOMICS, Issue 2009
Lajos Zoltán Bakucs
Gibrat's Law; Family farm; Quantile regression; Transition agriculture Abstract The article investigates the validity of Gibrat's Law for Hungarian family farms using FADN data collected between 2001 and 2007. Gibrat's Law states that the growth rate of firms will be independent of their initial size. Regression results allow us to reject Gibrat's Law for all quantiles. Evidence suggests that smaller farms tend to grow faster than larger ones. Results do not support the hypothesis of "disappearing middle" in Hungarian agriculture. We study a number of socio-economic factors that can help to explain farm growth. We find that total subsidies received by a farm and the farm operator's age are the most significant factors correlated with farm growth. [source]

The Impact of Board Independence and CEO Duality on Firm Performance: A Quantile Regression Analysis for Indonesia, Malaysia, South Korea and Thailand

BRITISH JOURNAL OF MANAGEMENT, Issue 3 2010
Dendi Ramdani
We study the effect of board independence and CEO duality on firm performance for a sample of stock-listed enterprises from Indonesia, Malaysia, South Korea and Thailand, applying quantile regression. Quantile regression is more powerful than classical linear regression since quantile regression can produce estimates for all conditional quantiles of the distribution of a response variable, whereas classical linear regression only estimates the conditional mean effects of a response variable. Moreover, quantile regression is better able to handle violations of the basic assumptions in classical linear regression. Our empirical evidence shows that the effect of board independence and CEO duality on firm performance is different across the conditional quantiles of the distribution of firm performance, something classical linear regression would leave unidentified. This finding suggests that estimating the quantile effect of a response variable can well be more insightful than estimating only the mean effect of the response variable. Additionally, we find a negative moderating effect of board size on the positive relationship between CEO duality and firm performance. [source]

Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings

ECONOMETRICA, Issue 1 2002
Alberto Abadie
This paper reports estimates of the effects of JTPA training programs on the distribution of earnings. The estimation uses a new instrumental variable (IV) method that measures program impacts on quantiles. The quantile treatment effects (QTE) estimator reduces to quantile regression when selection for treatment is exogenously determined. QTE can be computed as the solution to a convex linear programming problem, although this requires first-step estimation of a nuisance function. We develop distribution theory for the case where the first step is estimated nonparametrically. For women, the empirical results show that the JTPA program had the largest proportional impact at low quantiles. Perhaps surprisingly, however, JTPA training raised the quantiles of earnings for men only in the upper half of the trainee earnings distribution. [source]

Is the Impact of Public Investment Neutral Across the Regional Income Distribution?

ECONOMIC GEOGRAPHY, Issue 3 2005
Evidence from Mexico
Abstract: This article investigates the contribution of public investment to the reduction of regional inequalities, with a specific application to Mexico. We examine the impact of public investment according to the position of each region in the conditional distribution of regional income by using quantile regression as an empirical technique. The results confirm the hypothesis that regional inequalities can indeed be attributed to the regional distribution of public investment; the observed pattern shows that public investment mainly helped to reduce regional inequalities among the richest regions. [source]

Emergency Department Patient Volume and Troponin Laboratory Turnaround Time

ACADEMIC EMERGENCY MEDICINE, Issue 5 2010
Ula Hwang MD
Abstract Objectives:, Increases in emergency department (ED) visits may place a substantial burden on both the ED and hospital-based laboratories. Studies have identified laboratory turnaround time (TAT) as a barrier to patient process times and lengths of stay. Prolonged laboratory study results may also result in delayed recognition of critically ill patients and initiation of appropriate therapies. The objective of this study was to determine how ED patient volume itself is associated with laboratory TAT. Methods:, This was a retrospective cohort review of patients at five academic, tertiary care EDs in the United States. Data were collected on all adult patients seen in each ED with troponin laboratory testing during the months of January, April, July, and October 2007. Primary predictor variables were two ED patient volume measures at the time the troponin test was ordered: 1) number of all patients in the ED/number of beds (occupancy) and 2) number of admitted patients waiting for beds/beds (boarder occupancy). The outcome variable was troponin turnaround time (TTAT). Adjusted covariates included patient characteristics, triage severity, season (month of the laboratory test), and site. Multivariable adjusted quantile regression was carried out to assess the association of ED volume measures with TTAT. Results:, At total of 9,492 troponin tests were reviewed. Median TTAT for this cohort was 107 minutes (interquartile range [IQR] = 73,148 minutes). Median occupancy for this cohort was 1.05 patients (IQR = 0.78,1.38 patients) and median boarder occupancy was 0.21 (IQR = 0.11,0.32). Adjusted quantile regression demonstrated a significant association between increased ED patient volume and longer times to TTAT. For every 100% increase in census, or number of boarders over the number of ED beds, respectively, there was a 12 (95% confidence interval [CI] = 9 to 14) or 33 (95% CI = 24 to 42)-minute increase in TTAT. Conclusions:, Increased ED patient volume is associated with longer hospital laboratory processing times. Prolonged laboratory TAT may delay recognition of conditions in the acutely ill, potentially affecting clinician decision-making and the initiation of timely treatment. Use of laboratory TAT as a patient throughput measure and the study of factors associated with its prolonging should be further investigated. ACADEMIC EMERGENCY MEDICINE 2010; 17:501,507 © 2010 by the Society for Academic Emergency Medicine [source]

Private Investment and Political Institutions

ECONOMICS & POLITICS, Issue 1 2002
David Stasavage
Recent research has demonstrated a negative link between macroeconomic and political uncertainty and levels of private investment across countries. This raises the question whether certain types of government institutions might help reduce this uncertainty. North and Weingast (1989) propose that political institutions characterized by checks and balances can have beneficial effects on investment by allowing governments to credibly commit not to engage in ex post opportunism with respect to investors. In this paper I develop and test a modified version of their hypothesis, suggesting that checks and balances, on average, improve possibilities for commitment, but that they are not a necessary condition for doing so. Results of heteroskedastic regression and quantile regression estimates strongly support this proposition. [source]

Potentialities of quantile regression to predict ozone concentrations

ENVIRONMETRICS, Issue 2 2009
S. I. V. Sousa
Abstract This paper aims: (i) to analyse the influence of ozone precursors (both meteorological variables and pollutant concentrations) on ozone concentrations at different ozone levels; and (ii) to predict next day hourly ozone concentrations using a new approach based on quantile regression (QR). The performance of this model was compared with multiple linear regressions (MLR) for the three following periods: daylight, night time and all day. QR as proven to be an useful mathematical tool to evidence the heterogeneity of ozone predictor influences at different ozone levels. Such heterogeneity is generally hidden when an ordinary least square regression model is applied. The influence of previous concentrations of ozone and nitrogen monoxide on next day ozone concentrations was higher for lower quantiles. When QR was applied, the wind direction (WD) was found to be significant in the medium quantiles and the relative humidity (RH) in the higher quantiles. On the contrary, using the MLR models, both variables were not statistically significant. Moreover, QR allowed more efficient previsions of extreme values which are very useful once the forecasting of higher concentrations is fundamental to develop strategies for protecting the public health. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Semiparametric M -quantile regression for estimating the proportion of acidic lakes in 8-digit HUCs of the Northeastern US

ENVIRONMETRICS, Issue 7 2008
Monica Pratesi
Abstract Between 1991 and 1995, the Environmental Monitoring and Assessment Program of the US Environmental Protection Agency conducted a survey of lakes in the Northeastern states of the US to determine the ecological condition of these waters. Here, to this end, we want to obtain estimates of the proportion of lakes at (high) risk of acidification or acidified already for each 8-digit hydrologic unit code (HUC) within the region of interest. Sample sizes for the 113 HUCs are very small and 27 HUCs are not even observed. Therefore, small area estimation techniques should be invoked for the estimation of the distribution function of acid neutralizing capacity (ANC) for each HUC. The procedure is based on a semiparametric M -quantile regression model in which ANC depends on elevation and the year of the survey linearly, and on the geographical position of the lake through an unknown smooth bivariate function estimated by low-rank thin plate splines. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Statistical analysis of temperature impact on daily hospital admissions: analysis of data from Udine, Italy

ENVIRONMETRICS, Issue 1 2006
Francesco Pauli
Abstract This article is devoted to the analysis of the relationship between the health status of an urban population and meteorological variables. The analysis considers daily number of hospital admissions, not due to surgery, regarding the population resident in the Municipality of Udine, aged 75 and over. Hourly records on temperature, humidity, rain, atmospheric pressure, solar radiation, wind velocity and direction recorded at an observation site located near the center of Udine are considered. The study also considers hourly measures of pollutant concentrations collected by six monitoring stations. All data are relative to the summer periods of years 1995,2003. Generalized additive models (GAM) are used in which the response variable is the number of hospital admissions and is assumed to be distributed as a Poisson whose rate varies as a possibly non-linear function of the meteorological variables and variables allowing for calendar effects and pollutant concentrations. The subsequent part of the analysis explores the distribution of temperature conditional on the number of daily admissions through quantile regression. A non-linear (N-shaped) relationship between hospital admissions and temperature is estimated; temperature at 07:00 is selected as a covariate, revealing that nighttime temperature is more relevant than daytime. The quantile regression analysis points out, as expected, that the distribution of temperature on days with more admissions has higher q -quantiles with q near unity, while a clear-cut conclusion is not reached for q quantiles with q near 0. Copyright © 2005 John Wiley & Sons, Ltd. [source]

CEO Pay-For-Performance Heterogeneity Using Quantile Regression

Does Work Always Pay in Germany?

GERMAN ECONOMIC REVIEW, Issue 3 2010
Christoph Scheicher
Equity; redistribution; social insurance; taxes Abstract. Income redistribution in Germany is the result of a combination of several redistribution instruments: there is a complex income tax law, different obligatory social insurances and supplementary benefits. This paper estimates income redistribution by quantile regression, using German EVS data. Two results are obtained: income after redistribution does not always increase in line with income before redistribution, i.e. for people with a low income before redistribution, it does not make sense to increase their efforts, since more work means less earnings. Further, an increasing redistribution rate for higher incomes is not always observable from the data. [source]

Expenditure dispersion and dietary quality: evidence from Canada

HEALTH ECONOMICS, Issue 9 2008
Timothy K. M. BeattyArticle first published online: 13 AUG 200
Abstract This paper examines links between the way in which a household spreads their food expenditure over time and the dietary quality of the food they purchase. I find that households who make more frequent, smaller food purchases buy healthier foods than households who make fewer, larger purchases. These households are more likely to purchase foods with a lower share of total calories from fats, saturated fats and a larger share of calories from fruits and vegetables. The analysis is extended using quantile regression. The effect of expenditure dispersion is found to be largest among households with poor diets i.e. those households with diets high in saturated fats and low in fruits and vegetables. Copyright © 2008 John Wiley & Sons, Ltd. [source]

Modeling tropical cyclone intensity with quantile regression

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 10 2009
Thomas H. Jagger
Abstract Wind speeds from tropical cyclones (TCs) occurring near the USA are modeled with climate variables (covariates) using quantile regression. The influences of Atlantic sea-surface temperature (SST), the Pacific El Niño, and the North Atlantic oscillation (NAO) on near-coastal TC intensity are in the direction anticipated from previous studies using Poisson regression on cyclone counts and are, in general, strongest for higher intensity quantiles. The influence of solar activity, a new covariate, peaks near the median intensity level, but the relationship switches sign for the highest quantiles. An advantage of the quantile regression approach over a traditional parametric extreme value model is that it allows easier interpretation of model coefficients (parameters) with respect to changes to the covariates since coefficients vary as a function of quantile. It is proven mathematically that parameters of the Generalized Pareto Distribution (GPD) for extreme events can be used to estimate regression coefficients for the extreme quantiles. The mathematical relationship is demonstrated empirically using the subset of TC intensities exceeding 96 kt (49 m/s). Copyright © 2008 Royal Meteorological Society [source]

Patterns of change in timing of spring migration in North European songbird populations

JOURNAL OF AVIAN BIOLOGY, Issue 1 2006
Anders P. Tøttrup
From 1976 to 1997 passerines were mist-netted and ringed on the island of Christiansø, in the Baltic Sea. Here we present analyses of phenological changes (i.e. time of arrival) for 25 species based on the entire populations of mist-netted songbirds during spring migration. We used two approaches (least square and quantile regression) to test for changes in arrival time of first individuals and three different parts of the songbird populations (i.e. first 5%, 50% and 95% of the total number of trapped individuals corrected for trapping effort). Our results generally confirm earlier spring arrival of migratory passerines with an overall earlier arrival of 0.26 days per year. Changes in the arrival time of first individuals are often the only data available. They are typically analysed on the assumption that they are representative of their respective population. We found a unidirectional, significant change towards earlier arrival for all four measures of arrival timing which seem to support this. However, the four measures of arrival are changing at different rates. First individuals changed arrival time more rapidly than the first 5%, 50% and 95% of the spring total. Such differences are likely to be important for our understanding of population-dynamic changes in relation to climate change. These differences may also have long-term evolutionary consequences. Migration distance seems to affect the degree of change in arrival time, but we found no difference between species wintering in different regions of Africa. [source]

Evaluating predictive performance of value-at-risk models in emerging markets: a reality check

JOURNAL OF FORECASTING, Issue 2 2006
Yong Bao
Abstract We investigate the predictive performance of various classes of value-at-risk (VaR) models in several dimensions,unfiltered versus filtered VaR models, parametric versus nonparametric distributions, conventional versus extreme value distributions, and quantile regression versus inverting the conditional distribution function. By using the reality check test of White (2000), we compare the predictive power of alternative VaR models in terms of the empirical coverage probability and the predictive quantile loss for the stock markets of five Asian economies that suffered from the 1997,1998 financial crisis. The results based on these two criteria are largely compatible and indicate some empirical regularities of risk forecasts. The Riskmetrics model behaves reasonably well in tranquil periods, while some extreme value theory (EVT)-based models do better in the crisis period. Filtering often appears to be useful for some models, particularly for the EVT models, though it could be harmful for some other models. The CaViaR quantile regression models of Engle and Manganelli (2004) have shown some success in predicting the VaR risk measure for various periods, generally more stable than those that invert a distribution function. Overall, the forecasting performance of the VaR models considered varies over the three periods before, during and after the crisis. Copyright © 2006 John Wiley & Sons, Ltd. [source]

Rejecting the mean: Estimating the response of fen plant species to environmental factors by non-linear quantile regression

JOURNAL OF VEGETATION SCIENCE, Issue 4 2005
Henning K. Schröder
Abstract Question: Is quantile regression an appropriate statistical approach to estimate the response of fen species to single environmental factors? Background: Data sets in vegetation field studies are often characterized by a large number of zeros and they are generally incomplete in respect to the factors which possibly influence plant species distribution. Thus, it is problematic to relate plant species abundance to single environmental factors by the ordinary least squares regression technique of the conditional mean. Location: Riparian herbaceous fen in central Jutland (Denmark). Methods: Semi-parametric quantile regression was used to estimate the response of 18 plant species to six environmental factors, 95% regression quantiles were chosen to reduce the impact of multiple unmeasured factors on the regression analyses. Results of 95% quantile regression and ordinary least squares regression were compared. Results: The standard regression of the conditional mean underestimated the rates of change of species cover due to the selected factor in comparison to 95% regression quantiles. The fitted response curves indicated a general broad tolerance of the studied fen species to different flooding durations but a narrower range concerning groundwater amplitude. The cover of all species was related to soil exchangeable phosphate and base-richness. A relationship between soil exchangeable potassium and species cover was only found for 11 species. Conclusion: Considering the characteristics of data sets in vegetation science, non-linear quantile regression is a useful method for gradient analyses. [source]

The Cost of Flexibility at the Margin.

LABOUR, Issue 4-5 2007
Comparing the Wage Penalty for Fixed-term Contracts in Germany, Spain using Quantile Regression
Using quantile regression we find that in West Germany the earnings of permanent and fixed-term workers are most similar among high earners and most dissimilar among low earners. In Spain, the wage penalty shows little variation across the distribution of wages. This pattern was also found for different occupational groups, although there are clear differences in the absolute wage penalty across occupations. In conclusion we caution against generalizing findings from Spain to other ,rigid' European labour markets. [source]

Under Performers and Over Achievers: A Quantile Regression Analysis of Growth

THE ECONOMIC RECORD, Issue 248 2004
Raul A. Barreto
Numerous papers have searched for empirical linkages between long run economic growth and a myriad of economic, socio-political and environmental factors. Most of these studies use ordinary least-squares regression or panel regression analysis on a sample of countries and therefore consider the behaviour of growth around the mean of the conditional distribution. We extend the literature by using quantile regression to analyse long-term growth at a variety of points in the conditional distribution. By using this approach, we identify the determinants of growth for under performing countries relative to those for over achieving countries. [source]

The Distributional Heterogeneity of Growth Effects: Some Evidence

THE MANCHESTER SCHOOL, Issue 4 2003
Brendan M. Cunningham
This paper applies quantile regression and non-parametric density estimation techniques to international data on long-run economic growth. The approach reveals that previously identified drivers of growth vary in their impact across the conditional distribution of international growth. Specifically, these factors display disparate effects in conditional low-growth and high-growth contexts. The results suggest that there is a general bias underlying prior research. The incumbent drivers of growth exhibit relatively larger coefficients, in absolute value, on the upper tail of the conditional growth distribution. This set of stylized facts identifies factors that might alter the international distribution of growth. [source]

Characterizing Waiting Room Time, Treatment Time, and Boarding Time in the Emergency Department Using Quantile Regression

Monetary Policy Impulses and Retail Interest Rate Pass-Through in Asian Banking Markets

ASIAN ECONOMIC JOURNAL, Issue 3 2010
Kuan-Min Wang
C23; E43; E52; E58; F36 This paper considers the integration of financial markets and mutual influences of monetary policies in the USA and Asia based on monthly data from 1994 to 2007. We used panel-type and time-series and quantile panel-type error correction models to test the influences of expected and unexpected monetary policy impulses on the interest rate pass-through mechanism in the financial markets of 9 Asian countries and the USA. The empirics show that if interest rate integration exists in the financial markets, the following effects are observed: (i) positive impulses of unexpected monetary policy will lead to an increase in the long-run multiplier of the retail interest rate; (ii) the adjustment of retail interest rates with short-run disequilibrium will lead to an increase in the long-run markup; and (iii) the empirical results of quantile regression prove that when the interest variation is greater than the 0.5th quantile and unexpected monetary policy impulses are greater than the expected monetary policy impulses, the short-run interest rate pass-through mechanism becomes more unstable. [source]