Coefficient Estimates Research Articles

The Predictive Power of Monetary Policy on International Stock Market Returns—Evidence From TV-ARMAX Model

In this paper, we apply the time-varying ARMA model with exogenous variable (TV-ARMAX) to examine the predictive power of monetary policy on international stock returns. This method allows time-varying coefficient estimates and uses time-dependent cumulated variation penalty to filter noisy outlier data points. Based on a wide range of 31 countries, our method robustly outperforms other popular methods including the simple linear-regression model (SLM), the vector autoregression and its variants (VAR, TV-VAR, and VARX) and the ARMA model with exogenous variable (ARMAX).

Validation of a fall rate prediction model for community-dwelling older adults: a combined analysis of three cohorts with 1850 participants

BackgroundFragility fractures in older adults are often caused by fall events. The estimation of an expected fall rate might improve the identification of individuals at risk of fragility fractures and improve fracture prediction.MethodsA combined analysis of three previously developed fall rate models using individual participant data (n = 1850) was conducted using the methodology of a two-stage meta-analysis to derive an overall model. These previously developed models included the fall history as a predictor recorded as the number of experienced falls within 12 months, treated as a factor variable with the levels 0, 1, 2, 3, 4 and ≥ 5 falls. In the first stage, negative binomial regression models for every cohort were fit. In the second stage, the coefficients were compared and used to derive overall coefficients with a random effect meta-analysis. Additionally, external validation was performed by applying the three data sets to the models derived in the first stage.ResultsThe coefficient estimates for the prior number of falls were consistent among the three studies. Higgin’s I2 as heterogeneity measure ranged from 0 to 55.39%. The overall coefficient estimates indicated that the expected fall rate increases with an increasing number of previous falls. External model validation revealed that the prediction errors for the data sets were independent of the model to which they were applied.ConclusionThis analysis suggests that the fall history treated as a factor variable is a robust predictor of estimating future falls among different cohorts.

“Comparing merit order effects of wind penetration across wholesale electricity markets”

Renewable energy (RE) deployment is growing rapidly in electricity industries worldwide due to climate change and energy security concerns. Increased penetration into wholesale electricity markets poses new challenges for the electric utility industry. Among the topics receiving increased attention is the decrease of the equilibrium electricity price resulting from RE penetration, commonly known as the Merit Order Effect (MOE) in wholesale electricity markets. The pricing mechanism in most wholesale electricity markets is based on marginal cost. Using data from two wholesale electricity market regions (Pennsylvania – New Jersey – Maryland (PJM) and Electric Reliability Council of Texas (ERCOT)) and two different markets (day-ahead market (DAM) and real-time market (RTM)), we examine whether the MOE exists across both the DAM and RTM and how do different market conditions as represented by quantiles of wholesale electricity prices impact the MOE. Quantile regression is used to obtain coefficient estimates that indicate wind penetration demonstrates the MOE on a relatively consistent basis across market conditions in the DAM for both the PJM and ERCOT regions. However, results from the RTM show vastly different results with the PJM region demonstrating the MOE across all quantiles with declining impacts at higher quantiles and in the ERCOT region, the lower quantiles show a positive impact from wind penetration, failing to support the MOE price-dampening hypothesis. One possible interpretation of this unusual result is linked to connections between ancillary services markets in ERCOT and DAM prices, as described and estimated by Zarnikau et al. (2019) [23].

On the effect of confounding in linear regression models: an approach based on the theory of quadratic forms

In the last two decades, significant research efforts have been dedicated to addressing the issue of spatial confounding in linear regression models. Confounding occurs when the relationship between the covariate and the response variable is influenced by an unmeasured confounder associated with both. This results in biased estimators for the regression coefficients reduced efficiency, and misleading interpretations. This article aims to understand how confounding relates to the parameters of the data generating process. The sampling properties of the regression coefficient estimator are derived as ratios of dependent quadratic forms in Gaussian random variables: this allows us to obtain exact expressions for the marginal bias and variance of the estimator, that were not obtained in previous studies. Moreover, we provide an approximate measure of the marginal bias that gives insights of the main determinants of bias. Applications in the framework of geostatistical and areal data modeling are presented. Particular attention is devoted to the difference between smoothness and variability of random vectors involved in the data generating process. Results indicate that marginal covariance between the covariate and the confounder, along with marginal variability of the covariate, play the most relevant role in determining the magnitude of confounding, as measured by the bias.

An Empirical Assessment of IAS 40 Investment Property

This study examines the value relevance of the fair value model versus the cost model for evaluating investment properties under IAS 40 Investment Property. Contrary to the popular belief that fair value is the most relevant measurement attribute, we find that the coefficient estimate of investment properties for Chinese companies that adopted IAS 40’s fair value model is significantly smaller than its theoretical value, and is not significantly different from zero, suggesting that reported fair values are not value relevant as perceived by investors for the sample firms. Furthermore, investors tend to adjust the valuation of fair value companies’ non-investment property assets downward. The findings do not support the claim that fair value is superior to historical cost for the investment property valuation. Our findings highlight the need for more implementation guidelines from the IASB to enhance the value relevance of fair value estimates under IAS 40.

Unraveling Potential Confounding Variables in the Association Between Maternal Malaria and Child Stunting in Papua: A Case-Control Study with Mantel-Haenszel Analysis

Background:Papua is a malaria-endemic region with Indonesia's highest annual parasite incidence. At the same time, stunting is a common child health problem in Papua. Malaria incidence in pregnant women is considered a risk factor for stunting in children. Thus, the identification of confounding factors in this relationship is necessary. Objective:The study aimed to determine significant confounding factors in maternal malaria and child stunting and clarify the true association between these two conditions. Methods: The study with a case-control design was conducted at 14 Puskesmas from May to September 2023. The sample size was 681 children, consisting of toddlers who were stunted and not stunted and were selected by probability sampling technique. Data collection used questionnaires and anthropometric measurements. Statistical analysis used Mantel-Haenszel chi-square with a cut-off point for changes in the estimated coefficient for determining confounding> 10%. Results: The results showed that the crude Odds Ratio of the effect of malaria incidence during pregnancy on the incidence of stunting in children was 1.746 (95% CI 1,062-2,872). Stratification analysis showed the adjusted Odds Ratio value of child sex (1.78), low birth weight (1.652), basic immunization status (1.771), breastfeeding status (1.753), maternal age (1.732), occupation (1.828), ethnicity (1.722) and family income (1.764). There were no potential confounding variables in the association between malaria incidence in pregnant women and childhood stunting (change in coefficient estimate <10%). Conclusions: Public health interventions aimed at preventing malaria during pregnancy can be used as a strategy to reduce the risk of childhood stunting. Keywords:Malaria; pregnancy; stunting; confounding; Indonesia

Investigating weight constraint methods for causal-formative indicator modeling.

Causal-formative indicators are often used in social science research. To achieve identification in causal-formative indicator modeling, constraints need to be applied. A conventional method is to constrain the weight of a formative indicator to be 1. The selection of which indicator to have the fixed weight, however, may influence statistical inferences of the structural path coefficients from the causal-formative construct to outcomes. Another conventional method is to use equal weights (e.g., 1) and assumes that all indicators equally contribute to the latent construct, which can be a strong assumption. To address the limitations of the conventional methods, we proposed an alternative constraint method, in which the sum of the weights is constrained to be a constant. We analytically studied the relations and interpretations of structural path coefficients from the constraint methods, and the results showed that the proposed method yields better interpretations of path coefficients. Simulation studies were conducted to compare the performance of the weight constraint methods in causal-formative indicator modeling with one or two outcomes. Results showed that higher biases in the path coefficient estimates were observed from the conventional methods compared to the proposed method. The proposed method had ignorable bias and satisfactory coverage rates in the studied conditions. This study emphasizes the importance of using an appropriate weight constraint method in causal-formative indicator modeling.

Abstract P522: Racial Differences in Sleep Health in Middle-Aged Adults

Introduction: Sleep health is comprised of many dimensions, including duration, quality and sleep disorders. Prior work found that Black adults have worse sleep health than White adults, but the underlying reasons are unknown. Our goal was to examine possible determinants of racial disparities in sleep health. We hypothesized that sleep health would be worse in Black participants, and this would be partly explained by clinical, psychosocial and lifestyle risk factors. Methods: We used cross-sectional data from the sleep ancillary study to the Coronary Artery Risk Development in Young Adults (CARDIA) study. Several objective sleep measures were collected, including 7 days of wrist actigraphy and a home sleep test (Nox T3s). Sleep disordered breathing was classified by oxygen desaturation index (ODI)>=10 or apnea-hypopnea index (AHI) >=15. We used a series of regression models to calculate the percent reduction in the coefficient estimate for race in models for each sleep health metric after adjusting for clinical factors, psychosocial factors or lifestyle factors. Results: There were 565 Black and 741 White adults in this analysis (mean (SD) age 61 (3.6) years). Black adults had significantly worse sleep health based on all measures, including shorter sleep duration, lower sleep percentage, greater sleep fragmentation and higher prevalence of sleep disordered breathing compared to White adults. Clinical factors, psychosocial factors and lifestyle factors all explained a proportion of the racial differences in sleep fragmentation and sleep percentage and explained away the racial differences in sleep disordered breathing (Table). Conclusions: Racial disparities in sleep health may be partly explained by clinical factors, such as blood pressure and BMI, psychosocial factors, such as education and depression, and lifestyle factors, such as smoking, alcohol intake, and physical activity. These findings provide several potential intervention targets to improve sleep health, particularly in Black adults.

Calibration Indonesian-Numerical Weather Prediction using Geostatistical Output Perturbation

Indonesian-Numerical Weather Prediction (INA-NWP) is a numerical-based weather forecast method that has been developed by the Meteorology, Climatology and Geophysics Agency. However, the forecast is still unable to produce accurate weather forecasts. Geostatistical Output Perturbation (GOP) is a weather forecast method derived from only one deterministic output. GOP takes into consideration the spatial correlation among multiple locations simultaneously. GOP is capable to identify spatial dependency patterns that are associated with error models. This study aims to obtain calibrated forecasts for daily maximum and minimum temperature variables using GOP at 10 meteorological stations in Surabaya and surrounding areas. The stages in performing temperature forecasts using GOP are obtaining regression coefficient estimators, then calculating empirical semivariograms and estimating spatial parameters. Based on several weather forecast indicators, such as RMSE and CRPS, GOP is better than INA-NWP in terms of precision and accuracy.

Certain class of analytic functions defined by q-analogue of Ruscheweyh differential operator

In this paper, we obtain coefficient estimates, distortion theorems, radii of close-to-convexity, starlikeness and convexity for functions belonging to the class of analytic starlike and convex functions defined by q−analogue of Ruscheweyh differential operator. Also we find closure theorems, Nk,q,δ (e, g) neighborhood and partial sums for functions in this class. Mathematics Subject Classification (2010): 30C45. Received 07 June 2020; Accepted 06 August 2020

Estimators of various kappa coefficients based on the unbiased estimator of the expected index of agreements

Estimators of various kappa coefficients based on the unbiased estimator of the expected index of agreements

Poisson-modification of the Quasi Lindley optimal generalized regression estimator

In this paper, we propose an optimal generalized regression estimator based on the sample information for the Poisson-Modification of the Quasi Lindley (PMQL) regression model namely the PMQL optimal generalized regression estimator. Asymptotic properties of the proposed estimator are obtained. Further, the conditions of superiority over some other regression coefficient estimators for the PMQL regression model are also derived based on the mean square error criterion. In order to compare the performance of the proposed estimator over some other PMQL regression coefficient estimators in the scalar mean square error sense, a Monte Carlo simulation study is designed and results are discussed. Finally, a simulated data example and a real-world example are taken to show the potentiality of the new regression model based on the proposed estimator and show the consistency of the theoretical and simulation results, respectively.

Utilizing Linear Logistic Test Models to Explore Item Characteristics of Medical Subspecialty Certification Examinations

ABSTRACT Linear logistic test models (LLTMs), leveraging item response theory and linear regression, offer an elegant method for learning about item characteristics in complex content areas. This study used LLTMs to model single-best-answer, multiple-choice-question response data from two medical subspecialty certification examinations in multiple years and found that word count, proportion of complex words, number of options (3- vs. 4-option), whether including an image, nature of the question task (identifying risks, diagnostic test, management), and whether including application context significantly predicted item difficulty in one or both of the Critical Care Medicine and Pediatric Anesthesiology exams. The differences in the item characteristics that were significant predictors of item difficulty and their associated coefficient estimates between the two exams suggest possible domain differences. This study highlights the possibilities and challenges of using LLTMs to identify item characteristics for complex assessments. The results may help inform or expedite item writing and reviewing processes.

Multivariate assumptions and effect of model parameters in path analysis in oat crop

Context Path analysis (PA) is a widely used multivariate statistical technique. When performing PA, the effects of the parameters of the mathematical model relating to the experimental design are disregarded, working only with the average effects of the treatments. Aims We aimed to analyse the implications of statistical assumptions, and of removing mathematical model parameters, on the PA results in oat. Methods A field study was conducted in southern Brazil in five crop years. The experimental design employed was a two-factor 22 × 5 randomised complete block design, characterised by 22 cultivars and five fungicide applications, with three repetitions. Six explanatory variables were measured, panicle length, panicle dry mass, panicle spikelet number, panicle grain number, panicle grain dry mass, and harvest index, and the primary variable yield. Initially, normality and multicollinearity diagnoses were carried out and correlation coefficients were calculated. The PA was performed in three ways: traditional, with measures to address multicollinearity (ridge), and traditional with eliminating variables. Key results and conclusions The occurrence of multicollinearity resulted in obtaining path coefficients without biological application. Removing the model’s parameters modifies the path coefficients, with average changes of 10.5% and 13.3% in the direction, and 24.7% and 23.0% in the magnitude, of the direct and indirect effects, respectively. Implications This new approach makes it possible to remove the influences of treatments and experimental design from observations and, consequently, from path coefficients and their interpretations. Therefore, the researcher will reduce possible bias in the coefficient estimates, highlighting the real relationship between the variables, and making the results and interpretations more reliable.

Stein-Like Shrinkage Estimators for Coefficients of a Single-Equation in Simultaneous Equation Systems

Two stein-like shrinkage estimators are introduced to modify the 2SLS and the LIML estimators for coefficients of a single equation in a simultaneous system of equations. The proposed estimators are weighted averages of the 2SLS/LIML estimators and the OLS estimator. The shrinkage weight depends on the Wu-Hausman misspecification test statistic which evaluates the null of exogeneity against the alternative hypothesis of endogeneity. The approximate finite sample bias, mean squared errors, and density functions of the Stein-like shrinkage estimators are obtained using small-disturbance approximations. The dominance conditions of the Stein-like shrinkage estimators over the 2SLS/LIML estimator under the mean squared error and the concentration probability are obtained. The proposed method is further illustrated by simulation studies which demonstrate the good finite sample performance of the method, and is also applied to an empirical application of returns to education.

Electronic Trading and Financial Crisis Effects in the e-MID Interbank Market: A Multivariate Multiplicative Error Model Analysis

We employ a multivariate multiplicative error model (MMEM) to explore the interplay between high-frequency return volatilities, trading volume, and trading intensities within the Italian Electronic Interbank Credit Market (e-MID). Our research question also aims at analysing the e-MID and, more specifically, the behaviour of traders in relation to the financial crisis. For this purpose, we consider four time periods: the first period before the first intervention by the ECB, the second period before the collapse of Lehman Brothers, and finally, the last intervention by the ECB. Utilising five-minute intervals, our analysis reveals a robust causal relationship among volatilities, volumes, and trading intensities in this electronic market, yielding highly significant coefficient estimates of the multivariate multiplicative error model (MMEM). However, these relationships change qualitatively, showing a shift in e-MID market behaviour before, during, and after the outbreak of the financial crisis. Notably, we find evidence that trading was in a Pareto optimum in the first period, but as soon as uncertainties hit the market, this Pareto optimum becomes unstable and breaks down completely in the last period of an extremely illiquid market state. To the best of our knowledge, this paper represents the first and inaugural empirical application of MMEM to an interbank credit market, contributing valuable insights into its intricate dynamics.

Nearly collinear robust procedures for 2SLS estimation

Stata’s two-stage least-squares (2SLS) computation procedures are sensitive to near collinearity among regressors, allowing situations in which reported results depend upon factors as irrelevant as the order of the data and variables. This article illustrates this claim with the public-use data of Oreopoulos (2006, American Economic Review 96: 152–175), where the instrumented coefficient estimate can be made to vary between 0.012 and 30.0 in one specification by permuting the order of the variables. Different methods for improving the accuracy of 2SLS estimates are reviewed, and a Stata command for collinearity-robust 2SLS estimation is provided.

Comparative evaluation of Borrelia burgdorferi antibody detection between the VetScan Flex4 and SNAP 4Dx Plus

Two studies were developed to compare Borrelia burgdorferi antibody detection between the VetScan Flex4 and SNAP 4Dx Plus tests. The objective of the first study was to evaluate the diagnostic sensitivity (Se) and specificity (Sp) of VetScan Flex4 and SNAP 4Dx Plus B. burgdorferi results using field sourced samples compared to a Western Blot reference method. The sensitivity and specificity of VetScan Flex4 were 81.9 % (95 % CI: 71.9 %-89.5 %) and 89.3 % (95 % CI: 85.2 %-92.9 %) respectively, and SNAP 4Dx Plus's sensitivity and specificity were 80.7 % (95 % CI: 70.6 %-88.6 %) and 92.8 % (95 % CI: 89.1 %-95.5 %) respectively. When comparing VetScan Flex4 and Snap 4Dx Plus, the Simple Kappa Coefficient estimate was 0.76 (95 % CI: 0.69-0.84) indicating substantial agreement between the two methods. McNemar's Test revealed concordance between the two methods was not statistically significant (P = 0.05). The objective of the second study was to evaluate whether VetScan Flex4 differentiates between B. burgdorferi antibodies derived from infection versus vaccination with commonly used canine Lyme vaccines. The sensitivity and specificity of the VetScan Flex4 in differentiating canine Lyme vaccination from infection with Borrelia burgdorferi were 100 % (Se 95 % CI: 78.2 %-100 %; Sp 95 % CI: 91.2 %-100 %). In conclusion, the VetScan Flex4 is a reliably sensitive and specific point-of-care test that is similar to Snap 4Dx Plus, can differentiate between infection and Lyme vaccination, and can be utilized by veterinarians for Lyme disease diagnosis and surveillance of B. burgdorferi exposure.

Notes on some classes of spirallike functions associated with the $q$-integral operator

The object of the present paper is to find the essential properties for certain subfamilies of analytic and spirallike functions which are generated by $q$-integral operator. Further, we derive membership relations for functions belong to these subfamilies, and also we determine coefficient estimates.

Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts

Abstract. This study investigates parameter estimation (PE) to enhance climate forecasts of a coupled general circulation model by adjusting the background vertical diffusivity coefficients in its ocean component. These parameters were initially identified through sensitivity experiments and subsequently estimated by assimilating the sea surface temperature and temperature–salinity profiles. This study expands the coupled data assimilation system of the Community Earth System Model (CESM) and the ensemble adjustment Kalman filter (EAKF) to enable parameter estimation. PE experiments were performed to establish balanced initial states and adjusted parameters for forecasting the El Niño–Southern Oscillation (ENSO). Comparing the model states between the PE experiment and a state estimation (SE) experiment revealed that PE can significantly reduce the uncertainty of these parameters and improve the quality of analysis. The forecasts obtained from PE and SE experiments further validate that PE has the potential to improve the forecast skill for ENSO.