Full Information Maximum Likelihood Method Research Articles

Relationship between trade and growth for developing countries at different levels of income

We explore the relationship between trade openness and economic growth for developing countries at different income levels using a simultaneous equations model from 2004-2019. The model is estimated using a full information maximum likelihood method for the developing high-income (HI), upper-middle-income (UMI), lower-middle-income (LMI), and low-income (LI) countries. We find that for developing HI, UMI, and LI countries, trade positively impacts growth which varies significantly in magnitude. Trade contributes significantly higher growth for developing HI countries when compared to developing UMI and LI countries. The results suggest that trade is positively related to growth, but it works as a handmaiden rather than a growth engine. On the other hand, for developing LMI countries, we find that trade negatively impacts growth. For developing LMI countries, trade contributes significantly to capital formation for these countries, leading to growth. Further, we also perform dynamic policy simulations based on the most advocated policies like increasing growth of exports, foreign capital inflows, curbing domestic inflation, etc. We find that these policies are not very effective in increasing the growth rate of the real per capita income.

Dealing with missing data in multi-informant studies: A comparison of approaches.

Multi-informant studies are popular in social and behavioral science. However, their data analyses are challenging because data from different informants carry both shared and unique information and are often incomplete. Using Monte Carlo Simulation, the current study compares three approaches that can be used to analyze incomplete multi-informant data when there is a distinction between reference and nonreference informants. These approaches include a two-method measurement model for planned missing data (2MM-PMD), treating nonreference informants' reports as auxiliary variables with the full-information maximum likelihood method or multiple imputation, and listwise deletion. The result suggests that 2MM-PMD, when correctly specified and data are missing at random, has the best overall performance among the examined approaches regarding point estimates, type I error rates, and statistical power. In addition, it is also more robust to data that are not missing at random.

Evaluating the Effect of Planned Missing Designs in Structural Equation Model Fit Measures

In a planned missing design, the nonresponses occur according to the researcher’s will, with the goal of increasing data quality and avoiding overly extensive questionnaires. When adjusting a structural equation model to the data, there are different criteria to evaluate how the theoretical model fits the observed data, with the root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), comparative fit index (CFI) and Tucker–Lewis index (TLI) being the most common. Here, I explore the effect of the nonresponses due to a specific planned missing design—the three-form design—on the mentioned fit indices when adjusting a structural equation model. A simulation study was conducted with correctly specified model and one model with misspecified correlation between factors. The CFI, TLI and SRMR indices are affected by the nonresponses, particularly with small samples, low factor loadings and numerous observed variables. The existence of nonresponses when considering misspecified models causes unacceptable values for all the four fit indexes under analysis, namely when a strong correlation between factors is considered. The results shown here were performed with the simsem package in R and the full information maximum-likelihood method was used for handling missing data during model fitting.

Area under the Curve as an Alternative to Latent Growth Curve Modeling When Assessing the Effects of Predictor Variables on Repeated Measures of a Continuous Dependent Variable

Researchers conducting longitudinal data analysis in psychology and the behavioral sciences have several statistical methods to choose from, most of which either require specialized software to conduct or advanced knowledge of statistical methods to inform the selection of the correct model options (e.g., correlation structure). One simple alternative to conventional longitudinal data analysis methods is to calculate the area under the curve (AUC) from repeated measures and then use this new variable in one’s model. The present study assessed the relative efficacy of two AUC measures: the AUC with respect to the ground (AUC-g) and the AUC with respect to the increase (AUC-i) in comparison to latent growth curve modeling (LGCM), a popular repeated measures data analysis method. Using data from the ongoing Panel Study of Income Dynamics (PSID), we assessed the effects of four predictor variables on repeated measures of social anxiety, using both the AUC and LGCM. We used the full information maximum likelihood (FIML) method to account for missing data in LGCM and multiple imputation to account for missing data in the calculation of both AUC measures. Extracting parameter estimates from these models, we next conducted Monte Carlo simulations to assess the parameter bias and power (two estimates of performance) of both methods in the same models, with sample sizes ranging from 741 to 50. The results using both AUC measures in the initial models paralleled those of LGCM, particularly with respect to the LGCM baseline. With respect to the simulations, both AUC measures preformed as well or even better than LGCM in all sample sizes assessed. These results suggest that the AUC may be a viable alternative to LGCM, especially for researchers with less access to the specialized software necessary to conduct LGCM.

The Political Economy of American Exchange Rate Bill Voting: From the Perspective of RMB Appreciation

Abstract Under the theoretical framework of international trade political economy, this paper examines whether interest groups can dominate the voting of the US Exchange Rate Manipulation Act in 2015 from the perspective of RMB appreciation. Taking 100 senators as samples, this paper makes model estimation by using Full Information Maximum Likelihood Method (FIML), and quantitatively investigates the deep-seated driving factors behind the voting results of the US Exchange Rate Manipulation Act. The empirical results show that the influence of political donations from different interest groups on the voting behavior of parliamentarians is different. The political donations from American labor organization significantly affect the voting behavior of parliamentarians, but business groups and ideological organizations do not play a significant role in the voting results. In addition, senators’ personal characteristics affect the political donation of the bill to a certain extent, but do not affect the voting results. In the future, China needs to continuously pay attention to the latest progress of US exchange rate policy, correctly evaluate its impact on China’s economy, and maintain communication and coordination with important US interest groups to reduce the negative impact on related industries in China. JEL classification numbers: F14, F50, F31. Keywords: RMB Appreciation, Trade Protection, Bills Voting, Political Donation, Interest Group.

Designing a Pharmacy Accreditation Programme to Improve the Quality of Service Delivery in Pharmacies

Introduction: Improving service delivery has become a major goal in all healthcare settings. Accreditation can play an important role in improving service delivery, but few studies have been done on the development of pharmacy accreditation models. Aim: To design a pharmacy accreditation programme, develop accreditation models and provide a suitable tool for improving the quality of service delivery in the pharmacy. Materials and Methods: This study was a mixed-method study conducted from March 2019 to December 2020. A scoping review was undertaken for this study. PubMed, Google Scholar, World Health Organisation (WHO) and other related databases (like Web of Pharmacy Accreditation Organisations) were used as sources of databases, used following keywords to search documents according to MeSH terms i.e, health services administration, delivery of healthcare, accreditation and pharmacy. A structural equation modelling method was used. Based on the data extracted from the databases, a questionnaire was designed and was administered of stakeholders. The collected data was analysed using confirmatory factor analysis. Model parameters were estimated using the Full Information Maximum Likelihood (FIML) method. The model was revised on inspection of modification indices and fit statistics and experimented for construct validity, construct reliability and measurement invariance. Results: The findings of this study were the design of a pharmacy accreditation programme that includes five dimensions of quality and safety, management and performance, training and development of human resources, procedures and environmental and equipment factors. The quality and safety dimension with a correlation coefficient of 0.92 had the greatest impact on the accreditation programme, the environmental and equipment factors had the least effect with a correlation coefficient of 0.73. Conclusion: Simultaneous use of valid global models, the views of experts and stakeholders in this model, has provided a powerful and novel tool to improve the performance of pharmacies.

Bias in Observed Validity Estimates When Using Multiple Valid Predictors

ABSTRACT Simulated data, validity reports and a firefighter predictive validation study are used to examine validity bias created by three common selection problems-range restriction, applicant and incumbent attrition, and nonlinearity created by compression of high selection test scores. Top 20% selection samples drawn from an applicant pool with known validity coefficients demonstrate that the sample validity estimates of the three predictors are differentially biased in both magnitude and direction, depending on the selection strategy used. Concurrent validity designs generally favor novel predictors. Corrections for direct range restriction across situations were mostly ineffectual. With proper scaling, corrections for indirect range restriction are accurate, but cross-variable biasing effects can occur when score distributions of the individual predictors differ. Many of the biases found in the simulation results are demonstrated in a firefighter predictive validation study where variations of Pearson-Thorndike range corrected validities and a full information maximum likelihood (FIML), approaches are all compared as validity assessments. With normalized predictors, both Pearson and FIML methods show that a test of general mental ability and physically demanding job tasks predicted firefighter performance throughout the 30-year study, with no evidence of interactions or a leveling of performance at high test scores.

Modelling taper and stem volume considering stand density in Eucalyptus grandis and Eucalyptus dunnii

Eucalyptus grandis and Eucalyptus dunnii are the most planted tree species in Uruguay. Anticipating information about the quantity and quality of wood is important for managing intensive forest plantation. The estimate of merchantable and total wood volume is an essential tool in forest planning and management. The aim of this study was to evaluate four systems of taper and merchantable volume that consisted in a taper, a merchantable volume and a total tree volume function. A modified second-order continuous autoregressive error structure corrected the inherent serial autocorrelation of different observations in one tree. Taper and volume equations were fitted simultaneously after autocorrelation correction by full information maximum likelihood method. The segmented system proposed by Fang et al. (2000) produced the best fit as it explained more than 98% of the taper, merchantable volume and total volume variability for both species. In addition, precision of the segmented system was compared with and without incorporating stand density as a variable. Results of this analysis showed that for E. grandis, the predictive accuracy of the model was improved by including the stand density variable, whereas for E. dunnii this variable was not statistically significant. This modelling framework provides an improvement in taper and tree volume predictions for E. dunnii and E. grandis in Uruguay. The possibilities offered by this methodology could be of interest for its application in countries where fast growing plantations are managed.

Determination of Strategic Spreads in East Asia

ABSTRACT This study develops a simple spread model to explain whether market dealers behave strategically when using electronic broking services. Our spread model stresses the role of an unexpected liquidity imbalance and its volatility for inventory risk, which contrasts sharply with previous studies that emphasised price volatility as the inventory risk. To capture a dealer’s strategic behaviour, we introduce a new concept of a strategic weighted spread and test this new spread using the full information maximum likelihood method with the GARCH (1,1) process. Daily spread data from 1 January 2006 to 20 December 2016 is used to explore strategic spreading at the end of a trading day in East Asia. Different effects on strategic spreads of liquidity depth in Asian financial markets are also investigated by comparing strategic spreads between the thin and deep markets. The evidence provides support for our hypothesis that a dealer behaves strategically to avoid the unexpected inventory risk, and that the magnitude of this influence depends on the depth of the financial market.

Impact of Violation of the Missing-at-Random Assumption on Full-Information Maximum Likelihood Method in Multidimensional Adaptive Testing.

The full-information maximum likelihood (FIML) method makes it possible to estimate and analyze structural equation models (SEM) even when data are partially missing, enabling incomplete data to contribute to model estimation. The cornerstone of FIML is the missing-at-random (MAR) assumption. In (unidimensional) computerized adaptive testing (CAT), unselected items (i.e., responses that are not observed) remain at random even though selected items (i.e., responses that are observed) have been associated with a test taker’s latent trait that is being measured. In multidimensional adaptive testing (MAT), however, the missingness in the response data partially depends on the unobserved data because items are selected based on various types of information including the covariance among latent traits. This eventually may lead to violations of MAR. This study aimed to evaluate the potential impact such a violation of MAR in MAT could have on FIML estimation performance. The results showed an increase in estimation errors in item parameter estimation when the MAT response data were used, and differences in the level of the impact depending on how items loaded on multiple latent traits.

Analysis of Activity Location and Trip Mode Choice – A Study on Hierarchical Ordering

This paper focusses on preliminary analyses conducted to investigate the hierarchical decision-making structure of location and mode choice problems for non-work/school activities. The travel behaviour of two streams of individuals are studied separately. Nested logit models are tested using full information maximum likelihood method after estimating independent models for location and mode choice. The results show that individuals are more inclined to change their destination rather than their mode. The architectural framework of TASHA, the operational agent-based activity-based travel demand model of the Greater Toronto and Hamilton Area, will be redesigned in the light of these results and further analyses.

Imputation Methods to Deal With Missing Responses in Computerized Adaptive Multistage Testing.

Routing examinees to modules based on their ability level is a very important aspect in computerized adaptive multistage testing. However, the presence of missing responses may complicate estimation of examinee ability, which may result in misrouting of individuals. Therefore, missing responses should be handled carefully. This study investigated multiple missing data methods in computerized adaptive multistage testing, including two imputation techniques, the use of full information maximum likelihood and the use of scoring missing data as incorrect. These methods were examined under the missing completely at random, missing at random, and missing not at random frameworks, as well as other testing conditions. Comparisons were made to baseline conditions where no missing data were present. The results showed that imputation and the full information maximum likelihood methods outperformed incorrect scoring methods in terms of average bias, average root mean square error, and correlation between estimated and true thetas.

Simulation Study to Evaluate Full Information Maximum Likelihood as Parameter Estimation Methods for Spatial Vector Autoregressive Model with Calendar Variation

Spatial Vector Autoregressive models with calendar variation can be used to analyze the interrelationships between variables, the relationship of variables with their past and the relationship of variables in a location with those variables in other locations. It also can accommodate the effects of calendar variations. The parameter estimation of this model can be done using Full Information Maximum Likelihood (FIML). The purpose of this study is to evaluate the performance of FIML and it is compared to Ordinary Least Square (OLS) through simulation. The simulation is done using generated data which is designed following Spatial Vector Autoregressive model with calendar variation. There are three aspects studied in this simulation, namely how the effect of error correlation between equations, the variance of error and length of period on the performance of FIML Method and OLS Method. The result of the simulation is the variance of FIML parameter estimator is smaller than OLS, especially when the error correlation between equation are high. While the variance of errors and length of periods have no effect on performance of the estimator. The simulations also show that the mean of parameter estimators both FIML and OLS are very close to the parameters specified.

Validation of a questionnaire for consumers' perception of service quality in community pharmacy.

Consumer insights are vital to gain an understanding of service quality and enable its accurate measurement. Our team has previously developed a theory-based, qualitatively and quantitatively-informed service quality questionnaire for Australian community pharmacies with a price-focused marketing strategy (PFMS). The questionnaire assess 6 domains of service quality: Health and Medicines Advice, Non-Prescription Service, Relationship Quality, Technical Quality, Environmental Quality, and Health Outcomes. The aim of this study was to validate the questionnaire through confirmatory factor analysis (CFA) using data from customers attending pharmacies with a service-focused marketing strategy (SFMS). A 31 item questionnaire was administered to convenience sample of customers from 3 SFMS pharmacies. The collected data was analysed using CFA. Model parameters were estimated using the full information maximum likelihood (FIML) method. The model was revised on inspection of modification indices and fit statistics and tested for construct validity, construct reliability and measurement invariance. A total of 319 responses were collected for CFA. On examination of the modification indices, 4 items were deleted. The fit statistics for the final model were as follows: Satorra-Bentler scaled χ2 = 211.4, df = 155, CFI = 0.96, TLI = 0.95, RMSEA = 0.031 (90% confidence interval = 0.016-0.042). Construct validity of all constructs was established from positive convergent and discriminant validity tests. The composite reliability values for all constructs were ≥0.7, thus confirming construct reliability. A positive test for strong invariance confirmed that the model constructs are comparable across regional and metropolitan settings, gender, age and education level. This study has established the validity of the questionnaire as a measure of consumer perceptions of community pharmacy service quality. The tool can be applied in Australian community pharmacies in both metropolitan and regional settings to guide improvements in aspects of service provision.

Protest response and contingent valuation of an urban forest park in Fuzhou City, China

Urban forest parks, such as Fuzhou National Forest Park (FNFP), provide residents with landscape amenities, recreational opportunities, and interaction with nature. Understanding the recreational quality of green spaces and visitors’ behaviors and preferences is essential for effective forest park conservation strategy. We aimed to assess the recreational use and amenities of FNFP and calculate the monetary value of provided services using a contingent valuation method (CVM). A total of 249 park visitors were interviewed face-to-face based on a questionnaire using entrance fee as the payment vehicle, in October 2015 and January 2016. A considerable number of protest responses may cause selectivity bias; consequently, we used models excluding protest zeros. Sample selection models were estimated using Heckman’s two-step and full information maximum likelihood methods The average willingness-to-pay (WTP) was estimated to be 13.79 RMB (equivalent to 2.00 USD) for the unconditional model, and the lower mean values were estimated for two selectivity bias corrected models. In this case, park visitors would be willing to pay an average 11.6 RMB (equivalent to 1.69 USD) per person according to the full information maximum likelihood estimate and an average 10.96 RMB (equivalent to 1.60 USD) per person by the two-step method. Respondents’ satisfaction of forest park facilities and service significantly influenced their WTP value for forest park improvements. Socio-demographic features could not effectively discriminate the protest bidders and the other non-protest bidders. Our results suggest that protesters may value the resource less than positive bidders. The high percentage of protest respondents may be due to the current free use of FNFP and payment vehicle of entrance fee. The CVM approach takes residents’ preferences into consideration and allows the study of heterogenous socio-demographic groups; thus, our data may help to develop effective management plans for improving urban forest parks in China.

Overcommitment as a predictor of effort-reward imbalance: evidence from an 8-year follow-up study.

The effort-reward imbalance (ERI) model includes the personal characteristic of overcommitment (OC) and the job-related characteristics of effort, reward, and ERI, all of which are assumed to play a role in an employee's health and well-being at work. The aim of the present longitudinal study was to shed more light on the dynamics of the ERI model by investigating the basic hypotheses related to the role of OC in the model, ie, to establish whether an employee's OC could be a risk factor for an increased experience of high effort, low reward, and high ERI at work. The study was based on 5-wave, 8-year follow-up data collected among Finnish professionals in 2006 (T1, N=747), 2008 (T2, N=422), 2010 (T3, N=368), 2012 (T4, N=325), and 2014 (T5, N=273). The participants were mostly male (85% at T1) and the majority of them worked in technical fields. OC, effort, reward, and ERI were measured at each time point with the 23-item ERI scale. Three cross-lagged structural equation models (SEM) were estimated and compared by using full information maximum likelihood method: (i) OC predicted later experiences of effort, reward, and ERI (normal causation model), (ii) effort, reward, and ERI predicted later OC (reversed causation model), and (iii) associations in normal causal and reversed causal models were simultaneously valid (reciprocal causation model). The results supported the normal causation model: strong OC predicted later experiences of high effort, low reward and high ERI. High OC is a risk factor for an increased experience of job strain factors; that is, high effort, low reward, and high ERI. Thus, OC is a risk factor not only for an employee's well-being and health but also for an increasing risk for perceiving adverse job strain factors in the working environment.

Exploring the influential factors in incident clearance time: Disentangling causation from self-selection bias

Understanding the relationships between influential factors and incident clearance time is crucial to make effective countermeasures for incident management agencies. Although there have been a certain number of achievements on incident clearance time modeling, limited effort is made to investigate the relative role of incident response time and its self-selection in influencing the clearance time. To fill this gap, this study uses the endogenous switching model to explore the influential factors in incident clearance time, and aims to disentangle causation from self-selection bias caused by response process. Under the joint two-stage model framework, the binary probit model and switching regression model are formulated for both incident response time and clearance time, respectively. Based on the freeway incident data collected in Washington State, full information maximum likelihood (FIML) method is utilized to estimate the endogenous switching model parameters. Significant factors affecting incident response time and clearance time can be identified, including incident, temporal, geographical, environmental, traffic and operational attributes. The estimate results reveal the influential effects of incident, temporal, geographical, environmental, traffic and operational factors on incident response time and clearance time. In addition, the causality of incident response time itself and its self-selection correction on incident clearance time are found to be indispensable. These findings suggest that the causal effect of response time on incident clearance time will be overestimated if the self-selection bias is not considered.

(The Leverage Effects of Bank Lending on House Prices Under the Chonsei System)

Korean Abstract: 본 연구는 우리나라 전세제도 하에서 주택가격의 결정모형을 제시하고, 전세가 존재할 때 담보대출이 주택가격에 미치는 효과를 살펴보았다. 본 연구에서 담보대출은 전세가 존재할 때 주택수요를 보다 탄력적으로 변화시켜서 주택가격의 변동을 심화시키는 효과를 보인다. 따라서 담보대출비율(LTV)과 금리정책 등과 더불어 주택정책수단으로서 전세가비율의 통제에 대한 이론적 근거를 제시하고 있다. 2006년 1월~2012년 9월까지 우리나라 아파트 주택과 전세가격에 대한 월별자료와 최우도(Full Information Maximum Likelihood) 기법을 사용한 추정한 결과는 본 연구의 가설을 강하게 지지하고 있다. 본 연구의 결과는 전세가비율이 높을수록 담보대출은 주택시장을 불안정적으로 만든다는 흥미로운 결과를 보여주고 있다. 본 연구의 시사점은 주택가격의 변동성이 증가하면 부동산경기변동으로 인한 실질경기변동을 유발할 수 있으므로 통화정책은 거시적 물가안정 뿐 아니라 미시적으로 주택가격의 안정을 동시에 추구해야 한다. 이때 전세가비율에 대한 직접통제보다는, 금리정책과 더불어 주택보유의 감가상각비 상계허용, 주택 거래세와 보유세 감소, 미래 주택가격의 기대를 증가시키는 정책 등 간접적인 통제정책이 효율적이다. English Abstract: This paper aims to explore a theoretical model of determination of Chonsei price and its ratio to house prices, and to examine the effect of bank lending on Korean house prices in the Chonsei system. In theory, the existence of Chonsei house demand magnifies the effects of bank lending on house prices more elastically than these effects without Chonsei. The appropriate control of the Chonsei ratio as well as traditional house market policies such as LTV ratio and interest rate policy could be alternative policy to manage house prices during the economic recession periods. Using the monthly data of house sales and Chonsei trades during January 2006 through September 2012 and Full Information Maximum Likelihood(FIML) method, we test our hypothesis that the effect of income change and change in bank lending on house price depends on the Chonsei ratio to house price. Given theoretical results about the leverage effects of income and bank lending with Chonsei ratio, the evidence strongly supports our hypothesis on he leverage effect of bank lending on Korean house market with a Chonsei system.

Heritability of Autism Spectrum Disorder in a UK Population-Based Twin Sample.

Most evidence to date highlights the importance of genetic influences on the liability to autism and related traits. However, most of these findings are derived from clinically ascertained samples, possibly missing individuals with subtler manifestations, and obtained estimates may not be representative of the population. To establish the relative contributions of genetic and environmental factors in liability to autism spectrum disorder (ASD) and a broader autism phenotype in a large population-based twin sample and to ascertain the genetic/environmental relationship between dimensional trait measures and categorical diagnostic constructs of ASD. We used data from the population-based cohort Twins Early Development Study, which included all twin pairs born in England and Wales from January 1, 1994, through December 31, 1996. We performed joint continuous-ordinal liability threshold model fitting using the full information maximum likelihood method to estimate genetic and environmental parameters of covariance. Twin pairs underwent the following assessments: the Childhood Autism Spectrum Test (CAST) (6423 pairs; mean age, 7.9 years), the Development and Well-being Assessment (DAWBA) (359 pairs; mean age, 10.3 years), the Autism Diagnostic Observation Schedule (ADOS) (203 pairs; mean age, 13.2 years), the Autism Diagnostic Interview-Revised (ADI-R) (205 pairs; mean age, 13.2 years), and a best-estimate diagnosis (207 pairs). Participants underwent screening using a population-based measure of autistic traits (CAST assessment), structured diagnostic assessments (DAWBA, ADI-R, and ADOS), and a best-estimate diagnosis. On all ASD measures, correlations among monozygotic twins (range, 0.77-0.99) were significantly higher than those for dizygotic twins (range, 0.22-0.65), giving heritability estimates of 56% to 95%. The covariance of CAST and ASD diagnostic status (DAWBA, ADOS and best-estimate diagnosis) was largely explained by additive genetic factors (76%-95%). For the ADI-R only, shared environmental influences were significant (30% [95% CI, 8%-47%]) but smaller than genetic influences (56% [95% CI, 37%-82%]). The liability to ASD and a more broadly defined high-level autism trait phenotype in this large population-based twin sample derives primarily from additive genetic and, to a lesser extent, nonshared environmental effects. The largely consistent results across different diagnostic tools suggest that the results are generalizable across multiple measures and assessment methods. Genetic factors underpinning individual differences in autismlike traits show considerable overlap with genetic influences on diagnosed ASD.

Growth patterns of white British and Pakistani children in the Born in Bradford cohort: a latent growth modelling approach

BackgroundChildhood growth patterns have been proposed as a key predictor of health during childhood and adult life. In earlier studies, however, the statistical methodologies employed failed to uncover the more...