Estimation Procedure Research Articles

Economic policy uncertainty and financial stress revisited: a MIDAS-CQR approach

ABSTRACT In this note, we implement mixed data sampling (MIDAS) quantile regression to investigate the impact of economic policy uncertainty (EPU) on financial stress, as measured by the Financial Stress Index (FSI). By combining data of varying frequencies, the MIDAS framework, integrated with quantile regression, provides an in-depth understanding of the relationship between EPU and FSI. Our analysis reveals that EPU has positive and asymmetric effects on FSI, with its influence being more pronounced at higher quantiles than at lower ones. This indicates that financial markets are more sensitive to rising EPU during periods of elevated stress than when stress levels are lower. To improve the predictive performance of EPU on FSI, we construct a MIDAS composite quantile regression (MIDAS-CQR) model and propose innovative parametric and nonparametric estimation procedures. The empirical results demonstrate that MIDAS-CQR significantly improves the prediction accuracy of FSI, offering an efficient and robust alternative to traditional MIDAS regression models for analyzing mixed-frequency data. In addition, the nonparametric estimation approach slightly outperforms its parametric counterpart in prediction precision. These findings provide a deeper understanding of how EPU affects FSI, offering valuable insights for policymakers and financial analysts seeking effective strategies to stabilize markets during the periods of heightened financial stress.

The Spatial Distribution of Globular Cluster Systems in Early-type Galaxies: Estimation Procedure and Catalog of Properties for Globular Cluster Systems Observed with Deep Imaging Surveys

Abstract We present an analysis of the spatial distribution of globular cluster (GC) systems of 118 nearby early-type galaxies in the Next Generation Virgo Cluster Survey and Mass Assembly of early-Type GaLAxies with their fine Structures survey programs, which both used MegaCam on the Canada–France–Hawaii Telescope. We describe the procedure used to select GC candidates and fit the spatial distributions of GCs to a two-dimensional Sérsic function, which provides effective radii (half number radii) and Sérsic indices, and estimate background contamination by adding a constant term to the Sérsic function. In cases where a neighboring galaxy affects the estimation of the GC spatial distribution in the target galaxy, we fit two two-dimensional Sérsic functions, simultaneously. We also investigate the color distributions of GCs in our sample by using Gaussian mixture modeling. For GC systems with bimodal color distributions, we divide the GCs into blue and red subgroups and fit their respective spatial distributions with Sérsic functions. Finally, we measure the total number of GCs based on our fitted Sérsic function, and calculate the GC specific frequency.

Jump-Preserving Estimation and Jump Detection for Nonparametric Model with Missing Covariate

Nonparametric regression analysis has broad applications. In some cases, the regression function with jumps (i.e., the regression curve is discontinuous) seems to be more appropriate to describe the related phenomena. Existing a number of methods for estimating discontinuous curve, most of which are based on the data is complete, which is unrealistic in many practical situations. In this paper, we consider estimating discontinuous nonparametric model with missing covariate data. Based on inverse selection probability weighted and jump-preserving techniques, a jump-preserving estimation procedure is proposed. The proposed method is capable of automatically accommodating possible jumps in the nonparametric function, without the requirement of prior knowledge regarding the number and locations of jump points. The proposed estimator for the discontinuous regression function is shown to be oracally efficient in the sense that it is uniformly indistinguishable from that when the selection probabilities are known. Furthermore, it is proved that the fitted curve by this procedure is consistent in the entire design space. Numerical simulation also indicates the performance of finite sample of this method is efficient and reliable.

An Omega-Hierarchical Extension Index for Second-Order Constructs With Hierarchical Measuring Instruments

An index extending the widely used omega-hierarchical coefficient is discussed, which can be used for evaluating the influence of a second-order factor on the interrelationships among the components of a hierarchical measuring instrument. The index represents a useful and informative complement to the traditional omega-hierarchical measure of explained overall scale score variance by that underlying construct. A point and interval estimation procedure is outlined for the described index, which is based on model reparameterization and is developed within the latent variable modeling framework. The method is readily applicable with popular software and is illustrated with examples.

Cauchy–Logistic Unit Distribution: Properties and Application in Modeling Data Extremes

This manuscript deals with a novel two-parameter stochastic distribution, obtained by transforming the Cauchy distribution, using generalized logistic mapping, into a unit interval. In this way, according to the well-known properties of the Cauchy distribution, a unit random variable with significantly accentuated values at the ends of the unit interval is obtained. Therefore, the proposed stochastic distribution, named the Cauchy–logistic unit distribution, represents a stochastic model that may be suitable for modeling phenomena and processes with emphasized extreme values. Key stochastic properties of the CLU distribution are examined, such as moments, entropy, modality, and symmetry conditions. In addition, a quantile-based parameter estimation procedure, an asymptotic analysis of the thus obtained estimators, and their Monte Carlo simulation study are conducted. Finally, the application of the proposed distribution in stochastic modeling of some real-world data with emphasized extreme values is provided.

US Monetary Policy and Indeterminacy

ABSTRACTIn this paper, I investigate the stance of the US monetary policy in the postwar period. To this end, I show that two features are key: a rich structural model and a parameterization of the indeterminate solution that ensures a sufficient exploration of the indeterminacy region during the estimation procedure. Therefore, I estimate a medium‐scale model on the US macroeconomic data using a novel solution method to allow for indeterminacy. The evidence of a passive monetary policy in the pre‐1979 period is pervasive and robust to the use of alternative model specifications and data. By contrast, the evidence of an active stance after 1979 is overturned if the period of the Volcker disinflation is excluded or if the model is estimated including a time‐varying inflation target and data on inflation expectations.

Overview of reduced-rank regression with dense and sparse coefficients, and a new estimation procedure

Overview of reduced-rank regression with dense and sparse coefficients, and a new estimation procedure

Bayesian Gene Set Benchmark Dose Estimation for "omic" responses.

Estimating a toxic reference point using tools like the benchmark dose (BMD) is a critical step in setting policy to regulate pollution and ensure safe environments. Toxicity can be measured for different endpoints, including changes in gene expression and histopathology for various tissues, and is typically explored one gene or tissue at a time in a univariate setting that ignores correlation. In this work, we develop a multivariate estimation procedure to estimate the BMD for specified gene sets. Our approach extends the foundational univariate approach by accounting for correlation in a statistically principled way. We illustrate the method using data from a 5-day rat study and Hallmark gene sets and compare to existing BMD results computed by the EPA for both gene sets and apical histopathology endpoints. In contrast to previous ad-hoc methods, our principled approach provides the needed extension to bring the foundational univariate method into the multivariate world of transcriptomics. In addition to use in a regulatory setting, our method can provide hypothesis generation when gene sets correspond to mechanistic pathways. BS-BMD is implemented in R and C ++ and available at https://github.com/NIEHS/BS-BMD. Supplementary data are available at Journal Name.

Sequential Confidence Intervals for Comparing Two Proportions with Applications in A/B Testing

This article addresses the use of fixed-width confidence intervals (FWCIs) for comparing two independent Bernoulli populations in A/B testing scenarios. Two sequential estimation procedures are proposed: one for estimating the difference in log probabilities of success and the other for log odds ratios. Both methods showcase great efficiency, as established via theoretical analysis and Monte Carlo simulations. The practical utility of these methods is demonstrated through two real-world applications: analyzing retention rates in mobile game Cookie Cats and evaluating the effectiveness of online advertising.

Target an arbitrary probability of response using weighted staircase procedures.

Threshold estimation procedures are widely used to measure the stimulus level corresponding to a specified probability of response. The weighted up-and-down procedure, familiar to many due to its use in standard pure-tone audiometry, allows the experimenter to target any probability of response by using different ascending and descending step sizes. Unfortunately, thresholds have a signed mean error that made using weighted staircases inadvisable. The current study evaluated a correction to eliminate the error. Monte Carlo simulations of weighted staircases were used to test the effectiveness of the proposed correction for yes-no and forced-choice tasks with Gaussian and log-Weibull psychometric functions. Results showed that the proposed correction was effective over a wide range of step size magnitudes and ratios with a symmetric psychometric function and less effective when there was asymmetry due to the shape of the function or a high guess or lapse rate. The proposed correction facilitates the use of weighted staircases to target an arbitrary probability of response.

The effectiveness of vaccines against COVID-19 in Mexico: A time series approach

ObjectiveTo estimate the effectiveness of several vaccine brands—Pfizer, Astra, and Sinovac—against symptomatic COVID-19 without information on vaccination at the individual level. MethodsWe use data of mass vaccination programs—specifically, for sexagenarians and quinquagenarians—in three large municipalities of Mexico (Monterrey, Guadalupe, and San Nicolás) to conduct a two-step time series estimation procedure involving a synthetic control group. The data covers the period between the first week of March 2020 and the first week of October 2021. ResultsVaccine effectiveness is a concave function of time. At the peak, Pfizer reaches 92.6 % effectiveness, Astra 83.6 % and Sinovac 65.6 %. This occurs 9 to 12 weeks after the first shot. ConclusionThe results indicate that the three vaccines protect against symptomatic COVID-19. Nevertheless, they offer different levels of protection. The results also suggest that VE—under a two-shot scheme—reaches its peak 9 to 16 weeks after the first shot. Moreover, there seems to be a trade-off between achieving higher efficiency by administering the 2nd shot earlier or extending the protection period by administering it later.

A novel framework for increasing research transparency: Exploring the connection between diversity and innovation.

A split sample/dual method research protocol is demonstrated to increase transparency while reducing the probability of false discovery. We apply the protocol to examine whether diversity in ownership teams increases or decreases the likelihood of a firm reporting a novel innovation using data from the 2018 United States Census Bureau's Annual Business Survey. Transparency is increased in three ways: 1) all specification testing and identifying potentially productive models is done in an exploratory subsample that 2) preserves the validity of hypothesis test statistics from de novo estimation in the holdout confirmatory sample with 3) all findings publicly documented in an earlier registered report and in this journal publication. Bayesian estimation procedures that leverage information from the exploratory stage included in the confirmatory stage estimation replace traditional frequentist null hypothesis significance testing. In addition to increasing statistical power by using information from the full sample, Bayesian methods directly estimate a probability distribution for the magnitude of an effect, allowing much richer inference. Estimated magnitudes of diversity along academic discipline, race, ethnicity, and foreign-born status dimensions are positively associated with innovation. A maximally diverse ownership team on these dimensions would be roughly six times more likely to report new-to-market innovation than a homophilic team.

Industrial and service sectors’ growths: an empirical investigation of the effects on income inequality in Nigeria

This study empirically investigated the implications of industrial and service sectors’ growths on income inequality in Nigeria from 1985 to 2022, using data sourced from the Central Bank Nigeria Statistical Bulletin and the World Bank’s Development Indicators. The causal variables which include growth rates in the construction, manufacturing, mining and service sectors were regressed on Gini index as proxy for income inequality. The Autoregressive Distributed Lag (ARDL) estimation procedures were employed. The pre-diagnoses of Kwiatkowski-Phillips-Schmidt-Shin (KPSS) unit root revealed I(1) and I(0) mixed orders of integrations and the ARDL bounds cointegration established a long-run equilibrium relationship among the variables. The estimated current period short-run ARDL results indicated that construction and service sub-sectors’ growth rates had positively significant influences on the level of income inequality in Nigeria, growth rate in the manufacturing sub-sector exerted negatively significant effect and that of mining sub-sector positively but insignificantly impressed on income inequality while the one year lagged short-run results established that all the regressors negatively and significantly influenced income inequality level in Nigeria. The long-run results revealed that while all the explanatory variables appeared positive, construction and manufacturing sub-sectors’ growth rates had insignificant effects on income inequality in Nigeria. These findings were validated by the applied post-diagnostic tests. The study concluded that the immediate past year’s growth rates of the industrial and service sectors significantly moderated income inequality in Nigeria during the sampled period. It is consequently recommended among others that government should encourage reinvestment of economic growth gains from the referred sectors to ensure employment driven growth performances for sustained curtailing of income inequality in Nigeria. Keywords: Industrial Sub-sector, Service Sub-sector, Sub-sectors’ Growths, Income Inequality, Autoregressive Distributed Lag (ARDL) Procedures, Nigeria.

Minimum reduced-order models via causal inference

AbstractConstructing sparse, effective reduced-order models (ROMs) for high-dimensional dynamical data is an active area of research in applied sciences. In this work, we study an efficient approach to identifying such sparse ROMs using an information-theoretic indicator called causation entropy. Given a feature library of possible building block terms for the sought ROMs, the causation entropy ranks the importance of each term to the dynamics conveyed by the training data before a parameter estimation procedure is performed. It thus allows for an efficient construction of a hierarchy of ROMs with varying degrees of sparsity to effectively handle different tasks. This article examines the ability of the causation entropy to identify skillful sparse ROMs when a relatively high-dimensional ROM is required to emulate the dynamics conveyed by the training dataset. We demonstrate that a Gaussian approximation of the causation entropy still performs exceptionally well even in presence of highly non-Gaussian statistics. Such approximations provide an efficient way to access the otherwise hard to compute causation entropies when the selected feature library contains a large number of candidate functions. Besides recovering long-term statistics, we also demonstrate good performance of the obtained ROMs in recovering unobserved dynamics via data assimilation with partial observations, a test that has not been done before for causation-based ROMs of partial differential equations. The paradigmatic Kuramoto–Sivashinsky equation placed in a chaotic regime with highly skewed, multimodal statistics is utilized for these purposes.

Bayesian Inference for Zero-Modified Power Series Regression Models

Count data often exhibit discrepancies in the frequencies of zeros, which commonly occur across various application domains. These data may include excess zeros (zero inflation) or, less frequently, a scarcity of zeros (zero deflation). In regression models, both situations can arise at different levels of covariates. The zero-modified power series regression model provides an effective framework for modeling such count data, as it does not require prior knowledge of the type of zero modification, whether zero inflation or zero deflation, and can accommodate overdispersion, equidispersion, or underdispersion present in the data. This paper proposes a Bayesian estimation procedure based on the stochastic gradient Hamiltonian Monte Carlo algorithm, effectively addressing many challenges associated with estimating the model parameters. Additionally, we introduce a measure of Bayesian efficiency to evaluate the impact of prior information on parameter estimation. The practical utility of the proposed method is demonstrated through both simulated and real data across different types of zero modification.

A Nonparametric Model for High-Frequency Energy Prices

Abstract This paper proposes an efficient approach for modelling a high frequency continuous time diffusion process for the dynamics of crude oil. While various applications of continuous time models are considered in the literature, the results on choosing the right model are mixed. We employ a very general non-parametric approach to capture the dynamics of the crude oil market proxied by United States Oil (USO) exchange traded fund. This approach is purely data driven and does not require specification of the drift or the diffusion coefficient function. The proposed nonparametric kernel-based estimation procedure relies on the local polynomial kernel regression, where the choice of a bandwidth parameter plays a significant role. We demonstrate that besides offering a convenient way of estimating the continuous-time models for energy prices, our estimation procedure performs well when dealing with predicting USO prices out-of-sample. The analysis is extended by incorporating possible jump diffusion, where the assumption of continuity of the stochastic process is relaxed and a jump component is added to the diffusion process. In addition, we extend our model by adding possible seasonalities in the underlying dynamics, which requires decomposing the price by means of the Maximum Overlap Discrete Wavelet Transform (MODWT) algorithm and applying nonparametric kernel-based estimation procedure to modelling of the deseasonalized prices.

Effects of Maize Lethal Necrosis (MLN) Disease on Maize Output and Food Security Among Smallholder Farmers in Bomet County, Kenya

Maize is one of the leading cereals produced and consumed in Kenya and is very vital to food security. In the recent past, maize production has encountered many problems including poor input use, drought, pests, and diseases. Maize production in Kenya has been particularly devastated by Maize Lethal Necrosis (MLN), a viral disease which unexpectedly infested the maize crops in 2011. The disease has caused major economic and social losses to maize farmers and has spread to other neighbouring countries. Data on 3368 households was collected using a two-stage stratified cluster sampling method. The estimation procedure involves the use of propensity score matching to determine the effect of MLN on household food security and maize output. The results of this study indicate that MLN disease reduces household food security by about 16.6 percent, while it reduces maize output by about 10 (90 kgs.) bags per acre. The study therefore recommends that the government and stakeholders design appropriate policies for restoring plant health which include maize-resistant varieties, training of smallholder farmers on good agronomic practices and growing of alternative crops in order to break the disease cycle thus ensuring food security

The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study.

Thus far, considerable research has been focused on classifying a lesion as benign or malignant. However, there is a requirement for quick depth estimation of a lesion for the accurate clinical staging of the lesion. The lesion could be malignant and quickly grow beneath the skin. While biopsy slides provide clear information on lesion depth, it is an emerging domain to find quick and noninvasive methods to estimate depth, particularly based on 2D images. This study proposes a novel methodology for the depth estimation and visualization of skin lesions. Current diagnostic methods are approximate in determining how much a lesion may have proliferated within the skin. Using color gradients and depth maps, this method will give us a definite estimate and visualization procedure for lesions and other skin issues. We aim to generate 3D holograms of the lesion depth such that dermatologists can better diagnose melanoma. We started by performing classification using a convolutional neural network (CNN), followed by using explainable artificial intelligence to localize the image features responsible for the CNN output. We used the gradient class activation map approach to perform localization of the lesion from the rest of the image. We applied computer graphics for depth estimation and developing the 3D structure of the lesion. We used the depth from defocus method for depth estimation from single images and Gabor filters for volumetric representation of the depth map. Our novel method, called red spot analysis, measures the degree of infection based on how a conical hologram is constructed. We collaborated with a dermatologist to analyze the 3D hologram output and received feedback on how this method can be introduced to clinical implementation. The neural model plus the explainable artificial intelligence algorithm achieved an accuracy of 86% in classifying the lesions correctly as benign or malignant. For the entire pipeline, we mapped the benign and malignant cases to their conical representations. We received exceedingly positive feedback while pitching this idea at the King Edward Memorial Institute in India. Dermatologists considered this a potentially useful tool in the depth estimation of lesions. We received a number of ideas for evaluating the technique before it can be introduced to the clinical scene. When we map the CNN outputs (benign or malignant) to the corresponding hologram, we observe that a malignant lesion has a higher concentration of red spots (infection) in the upper and deeper portions of the skin, and that the malignant cases have deeper conical sections when compared with the benign cases. This proves that the qualitative results map with the initial classification performed by the neural model. The positive feedback provided by the dermatologist suggests that the qualitative conclusion of the method is sufficient.

Stochastic modeling with time-inhomogeneous Jacobi diffusions: applications to bounded and seasonal environmental processes

AbstractIn this paper we explore stochastic modeling of bounded processes in continuous time using time-inhomogeneous Jacobi diffusions. We present some basic general results and introduce a subclass of models with seasonal time variation. In the seasonal models we derive the conditional mean and variance in closed form and propose a strategy for estimation based on quasi maximum likelihood. An empirical application is carried out to daily time series data on relative humidity. Simulation methods are used to investigate properties of the resulting parameter estimators. The results show that the proposed seasonal Jacobi model gives a very satisfactory fit to data and that the estimation procedure works well.

Proportional rates model for recurrent event data with intermittent gaps and a terminal event.

Recurrent events are common in medical practice or epidemiologic studies when each subject experiences a particular event repeatedly over time. In some long-term observations of recurrent events, a terminal event such as death may exist in recurrent event data. Meanwhile, some inspected subjects will withdraw from a study for some time for various reasons and then resume, which may happen more than once. The period between the subject leaving and returning to the study is called an intermittent gap. One naive method typically ignores gaps and treats the events as usual recurrent events, which could result in misleading estimation results. In this article, we consider a semiparametric proportional rates model for recurrent event data with intermittent gaps and a terminal event. An estimation procedure is developed for the model parameters, and the asymptotic properties of the resulting estimators are established. Simulation studies demonstrate that the proposed estimators perform satisfactorily compared to the naive method that ignores gaps. A diabetes study further shows the utility of the proposed method.