Pseudo Maximum Likelihood Estimation Research Articles

Two-stage pseudo maximum likelihood estimation of semiparametric copula-based regression models for semi-competing risks data.

We propose a two-stage estimation procedure for a copula-based model with semi-competing risks data, where the non-terminal event is subject to dependent censoring by the terminal event, and both events are subject to independent censoring. With a copula-based model, the marginal survival functions of individual event times are specified by semiparametric transformation models, and the dependence between the bivariate event times is specified by a parametric copula function. For the estimation procedure, in the first stage, the parameters associated with the marginal of the terminal event are estimated using only the corresponding observed outcomes, and in the second stage, the marginal parameters for the non-terminal event time and the copula parameter are estimated together via maximizing a pseudo-likelihood function based on the joint distribution of the bivariate event times. We derived the asymptotic properties of the proposed estimator and provided an analytic variance estimator for inference. Through simulation studies, we showed that our approach leads to consistent estimates with less computational cost and more robustness than the one-stage procedure developed in Chen YH (Lifetime Data Anal 18:36-57, 2012), where all parameters were estimated simultaneously. In addition, our approach demonstrates more desirable finite-sample performances over another existing two-stage estimation method proposed in Zhu H et al., (Commu Statistics-Theory Methods 51(22):7830-7845, 2021) . An R package PMLE4SCR is developed to implement our proposed method.

Evaluating the influence of information and communication technology and the BRICS in South African trade dynamics: a gravity model approach

PurposeThis study aims to assess how South African membership to the Brazil, Russia, India, China and South Africa (BRICS) has influenced its traded sector. It also evaluated information and communication technology (ICT) as an instrument for enhancing bilateral trade.Design/methodology/approachICT was captured from three perspectives: mobile technology, internet usage and fixed telephones. It was integrated into an augmented gravity equation as a trade cost. The study covered 27 years and 181 South African trading partners. Estimation was done using the Poisson pseudo maximum likelihood estimation technique and the implementation of exporter-year and importer-year fixed effects.FindingsIt was revealed that mobile phone and fixed telephone subscriptions improve import and export by 4.66% and 1.79%, respectively, while the internet penetration rate negates export and import by 13.4% and 5.89%, respectively. However, it further demonstrates that the internet penetration rate and fixed telephone subscription reduced the negative impact of distance by 7.26% and 1.15% for export. ICT performed better when South Africa only traded with the BRICS countries. The report also shows an encouraging BRICS effect on South African bilateral trade with the bloc.Practical implicationsThe study highlights that BRICS membership significantly boosts South Africa’s bilateral trade, which encourages new African memberships, while also emphasising the role of ICT in mitigating the negative impact of distance. Policymakers should enhance mobile phone and fixed telephone infrastructures to improve imports and exports, respectively, and strategically manage internet penetration to maximise the benefits of BRICS economic cooperation.Originality/valueThe study evaluated the influence of South Africa’s BRICS membership and how ICT variables interact with distance to mitigate its effects on trade. This provides a nuanced understanding of ICT’s unique impact on South Africa’s bilateral trade, offering valuable insights for policymakers to enhance trade performance and leverage BRICS economic cooperation effectively.

Impact of Ebola Virus Disease and ECOWAS Membership on Intra-Regional Trade in West Africa

The West African sub-region bore the brunt of the world's largest Ebola outbreak, significantly impacting the economic activities and trade shares of the affected countries. This study seeks to examine the repercussions of the Ebola Virus Disease (EVD) on the trade shares of affected countries and to explore the potential influence of ECOWAS membership on intra-regional trade in West Africa. Using the Poison Pseudo Maximum Likelihood (PPML) estimation technique, an analysis of the augmented gravity model of international trade was conducted. The findings indicate a two-fold reduction in the trade shares of affected countries with their intra-regional partners due to the Ebola Virus Disease. Additionally, with Mauritania expressing its desire to join the ECOWAS sub-region, there is a need to explore the impact of the Regional Economic Community on intra-regional trade. Furthermore, the study reveals that ECOWAS membership has the potential to double trade levels in West Africa. The findings also suggest that Mauritania stands to gain significant benefits from becoming a member of the ECOWAS. In conclusion, this study highlights the necessity for ECOWAS to proactively respond to disease outbreaks and underscores the importance of increased research investment. Moreover, it emphasizes the need for the ECOWAS to further improve infrastructure to facilitate intra-regional trade, especially in transportation.

The gravity of distance: evidence from a trade embargo

Abstract On 5 June 2017, an airspace blockade was imposed on the State of Qatar by Bahrain, Saudi Arabia, United Arab Emirates (neighboring countries), and Egypt. We exploit this exogenous increase in air transportation costs toward non-blockading countries to examine the effect of increased travel distance, due to re-routing, on bilateral trade. Based on a gravity model estimated using a Poisson pseudo-maximum likelihood estimator, we find a distance elasticity of imports between −0.3 and −0.5. Overcoming the limitations of cross-sectional studies and taking advantage of this quasi-natural experiment, our findings are robust and revise downward previous estimates of the distance elasticity.

A Generalized Finite Mixture Model for Asymmetric Probability Distribution Observations

Finite mixture model is a useful probabilistic model for representing the probability distributions of observations. Gaussian mixture model (GMM) is a widely used one whose parameters are always estimated by the famous EM algorithm. But when probability distributions contain asymmetric modes, GMM requires much more constituent components to achieve a satisfied accuracy. Therefore, a generalized mixture finite model is proposed. First, it adopts the derivative [Formula: see text]-PDF which can well represent the asymmetric probability distributions as the probability density. However, the differential operation and solving the likelihood equations are scarcely possible in the M step of EM algorithm for the complex expression of the derivative [Formula: see text]-PDF. Second, a pseudo EM method is proposed to avoid the abovementioned difficulty which finds the pseudo maximum likelihood estimates of the parameters by utilizing the moment matching principle. It more easily estimates the parameters by solving a series of moment matching equations. Finally, four examples are presented to verify that the proposed generalized finite mixture model has advantage on representing the asymmetric probability distributions observations.

How Product Standards Influence Tea Exports? Empirical Evidence from India

India has witnessed a declining trend in tea exports in the recent period, which can partly be attributed to competition from exporters (e.g., Kenya and Sri Lanka) enjoying price advantages over the Indian varieties. However, although after the inception of the World Trade Organization (WTO) in 1995, the tariff barriers on Indian tea exports have come down across developed partners, the incidence of non-tariff barriers (NTBs) has emerged as a challenge in many destinations, with a possible dampening effect on exports. The present analysis attempts to analyse whether the imposition of product standard related measures, particularly sanitary and phytosanitary (SPS) and Technical Barriers to Trade (TBT) interventions, may have negatively influenced Indian tea exports over the period 1996–2021. Rather than considering the mere presence of SPS–TBT standards in major partner countries, their potential impacts on Indian tea exports have been determined by analysing the stringency levels of the corresponding notifications submitted to the WTO. The constructed stringency patterns are then incorporated into the Poisson pseudo maximum likelihood estimation models. The observed negative influence of standard-related measures in partner countries on Indian tea exports calls for policy interventions to enable the sector to better ensure compliance in the future.

Environmental, Geographical, and Economic Impacts of Inbound Tourism in China: A Mixed-Effects Gravity Model Approach

This study examines the dynamics of inbound tourism in China, utilizing a mixed-effects gravity model to analyze data from urban clusters around China’s three major airports. The research methodology of the study includes applying advanced econometric techniques, such as the Poisson pseudo-maximum likelihood estimation, to ensure robust and accurate results. The study focuses on international tourist arrivals and foreign exchange earnings, identifying key drivers such as tourism resources, transportation safety, and service quality. Our findings indicate a 10% increase in per capita GDP correlates with a 0.88% rise in inbound tourist numbers. Additionally, proximity to major urban centers like Beijing, Shanghai, and Guangzhou significantly influences tourist arrivals, with every 100 km increase in distance resulting in a 5.56% decrease in tourist numbers. The study also explores the impact of environmental factors on tourism, suggesting that improvements in green coverage and reductions in industrial waste and traffic fatalities could enhance tourist arrivals. Conversely, environmental protection measures can both positively and negatively impact tourism. This research provides a strategic blueprint for policymakers and professionals in tourism and environmental sustainability, emphasizing the importance of integrated environmental sustainability in tourism development strategies. The model accounts for up to 79% of the variability in inbound tourism flows, offering robust evidence that economic and policy dimensions impact tourism.

Kinerja Ekspor Produk Teh Indonesia di Pasar Internasional: Asesmen Perjanjian Perdagangan Indonesia

The decline in Indonesian tea products export performance does not correspond to the Government initiatives to reduce barriers through trade agreements. This research aims to analyse the export performance and the role of trade agreements towards Indonesian tea products export. This study employs panel data regression with a 15-year time series (2007-2021) and a cross-section of 40 destination countries. The data are analysed using a modified export approach gravity model with Poisson Pseudo-Maximum Likelihood (PPML) estimation. The research results show that the Indonesian tea products export destinations are dominated by Asia (19 countries) and Europe (12 countries). The regression results suggest that the implementation of trade agreements with Asian and European region countries has not effectively increased Indonesian tea products export. On the other hand, the implementation of trade agreements with other region countries, i.e., Australia and New Zealand, has successfully promoted export towards Indonesian tea products. Those can be attributed to trade agreements underutilization and trade barriers imposed by the trade agreement members. Hence, inward and outward-looking strategies are necessary to promote the export creation effect of implemented trade agreements towards Indonesian tea product exports.

The Role of Time Zone Differences on Bilateral Service Flows from Kenya

ABSTRACT This study investigates the effect of time zone differences on service exports (total and nine categories—travel, transport, computer and information, construction, financial, insurance, government, other business services, and personal, cultural, and recreational services) from Kenya. Estimates from the Poisson Pseudo-Maximum Likelihood (PPML) estimator of the gravity model on bilateral service exports data from Kenya to 176 countries, for the period 2005–2019, show that the effects of time zone differences between Kenya and partner countries is positive and statistically significant in technology-sensitive service exports (computer and information) while negative for construction and government service exports. Further analysis shows that these results are robust when an alternative model is employed and overlapping workday hours are used. The authors also find that the effect of time zone differences on services is nonlinear and sensitive to regulations and physical infrastructure (particularly mobile subscriptions). Attracting investment in technology-sensitive sectors should be encouraged through appropriate policy, given the positive impact of time zone differences on their exports. Traders in these sectors can also create networks with foreign firms. Deregulating and developing information and communication infrastructure, to expand mobile subscriptions, should also be encouraged.

Econometric Determinants of Chinese Developmental Finance in Sub-Saharan Africa; Further Evidence using a Flow-Type Decomposed Poisson Pseudo-Maximum Likelihood Model

Chinese “aid” has long engendered criticism. Western pundits and policymakers posit it as a political tool employed by Beijing to asway foreign policy concessions and secure natural resources access for its domestic industries, often atthe expense of the recipient country. However, we argue that large amounts of these claims lack empirical scrutiny or have been hindered by enduring conceptual, methodological, and data-centric constraints. This paper thus aims to refine and integrate prevailing methodological practices to produce a more nuanced understanding of Chinese motivations. Using empirical regression analysis and a newly released granular dataset spanning 48 Sub-Saharan African countries from 2000-2021, we: (1) decompose Chinese “aid” into flow types of relative concessionality; (2) utilize a Poisson pseudo-maximum likelihood (PPML) estimator to mitigate log-transformation and heteroskedasticity concerns present within traditional modeling of zero-inflated datasets; (3) retain a subset of contemporary values to deal with issues related to simultaneity in our regressors. Our findings suggest that while developmental goals primarily drive concessional aid, commercial lending is moreso influenced by economic self-interest, which is often incorrectly conflated with “aid” in the traditional sense. Further, Chinese “aid” does not flow disproportionately to corrupt or authoritarian regimes. This challenges the dominant “rogue donor” narrative and contributes to a more comprehensive perspective on China's emerging role in global developmental finance.

Do institutions matter for agri-food imports and exports between China and Africa? A gravity model analysis

This paper aims to study the role of institutional quality on agri-food trade (import and export) between China and 48 African countries during the 2001-2021 period. The gravity model of trade was employed by estimating the Generalised Least Square (GLS) and the Poisson Pseudo-Maximum Likelihood (PPML) estimators, which include zero trade flows for panel data. The main results show that voice and accountability, control of corruption, and the rule of law all enhance agri-food trade flows. While political instability encourages agri-food exports to China. In addition, the economic size of China, the trade cost (distance), and the population of African countries also encourage agri-food trade between China and African economies. Moreover, access to the sea, exchange rate policy, and the economic size of African nations deplete China’s agri-food imports from Africa. We showed the mechanism through which each institutional variable affects China’s agri-food trade flows to African nations.

Tác động của biến động tỷ giá hối đoái đến xuất khẩu nông nghiệp của Việt Nam và vai trò của tự do hóa tài chính tại nước đối tác

This study assesses the impact of real exchange rate volatility on Vietnam’s agricultural exports, considering the role financial liberalization in its trading partners. The author employs Poisson pseudomaximum likelihood estimators to estimate a gravity model covering bilateral agricultural exports from Vietnam to 187 its trading partners during 1996-2021. The empirical results of this study indicate that exchange rate volatility hampers agricultural exports of Vietnam and financial liberalization in its trading partners is crucial for alleviating this adverse nexus. Therefore, maintaining stable exchange rate is necessary to boost agricultural exports of Vietnam. In addition, under great turmoil in global economy with strong fluctuations in exchange rates, Vietnam still has a chance to increase its agricultural exports to countries that have high levels of financial liberalization. This finding has an important implication for selecting trading partners to improve export performance of agricultural sector in Vietnam.

Coupling and decoupling of ancestral linkages and current cross-border economic activities: Genetics and policy

This paper studies the potential link between the biological evolution of populations and present-day economic interactions by estimating the correlation of shared ancestry among populations with cross-border capital and human flows. To this end, we employ the new concept of genetic distance, based on (dis)similarity of neutral gene alleles, to quantify shared ancestry. We then incorporate the genetic distance measure into an augmented gravity model, traditionally used to analyze the effect of geographical distance on bilateral exchange. Our analysis focuses on bilateral foreign direct investment (FDI) and migration across 135 countries and we use both linear regression techniques as well as the Poisson Pseudo-Maximum Likelihood Estimator to account for any non-linearities in the model. Our results show that a 1% increase in genetic distance reduces FDI flows by 0.08% while controlling for other distance constructs and factors associated with global capital and human movement. Genetic distance also has a negative effect on migration, where a 1% increase in genetic distance reduces migration flows by 0.22%, with all other things remaining constant. Our study, therefore, links shared ancestry with economic behavior, showing how historical connections are associated with current economic exchanges among nations. Additionally, recognizing that ancestral ties are outside human control, we examine policy measures that help nations overcome such distance barriers. Our findings show that strengthening a nation’s institutional quality and adherence to the rule of law can effectively mitigate any negative correlation of distance constructs with economic exchanges. These insights suggest that prudent policies to foster a stable business environment are essential for any nation to attract FDI and human capital, even from geographically or genetically distant nations.

Indirect channel of trade agreements in deepening global value chains

AbstractPreferential trade agreements (PTAs) between trading partners are known to have positive impact on the expanding and deepening of global value chain integration due to lower trade and investment barriers. Less is known, however, is if other PTAs of PTA partners have any impact on the value‐added trade linkages of the direct PTA partners concerned. Based on compositive indicators measuring the breadth and depth of PTAs from 2007 to 2018 and using Multiregional Input–Output Table database covering 62 countries and 35 sectors and the Content of Deep Trade Agreements database, this paper estimates the multiplicative form of the constant‐elasticity augmented gravity equation model by a Poisson Pseudo Maximum Likelihood (PPML) estimator. This study shows that indirect impact of the deepening of trade agreements of a country's PTA partner exerts positive, significant impact on value‐added exports of a country. Both backward and forward linkages of a country measured by various decomposition of valued added exports indicate this indirect channel of value chain deepening effect even outweighs the direct impact from the deepening of PTAs between direct trading partners with significant policy implications in formulating PTA strategies.

The more the better? How degree programs’ variety affects university students’ churn risk

Students’ intra- and, especially, inter-degree relocation poses significant challenges that tertiary education systems and universities must address to identify the best possible solutions for its reduction. Indeed, this phenomenon has a major impact on several aspects, from the reallocation of financial resources to factors related to the organization of spaces and activities, as well as in terms of human and social capital. Moreover, it is much more challenging when involving different universities in different geographical areas. This paper addresses the issue of churn risk among students and its relationship with the institutions’ educational offerings, in terms of degree programs, by accounting for the impact of the characteristics of both universities and hosting areas. In particular, the analysis relies on Italian administrative data regarding students who decide to change university or field of study when enrolling in their second year of career. The results obtained performing the Poisson Pseudo Maximum Likelihood estimation show that the variety and the dimension of universities’ services supply contribute to reducing students’ churn risks and are positively associated with universities’ retention rates.

Assessing the impact of non-tariff measures on Sri Lankan mango exports: insights, challenges, and recommendations

IntroductionWith the increase in non-tariff measures (NTMs) in recent years, understanding their impact on trade policies and agricultural exports is of utmost importance for countries like Sri Lanka, where the proliferation of NTMs has posed substantial impediments to its mango export industry. However, studying NTMs can be challenging due to their complex nature, diverse range, and limited information availability.MethodsThis research aims to investigate the significant effect of NTMs on Sri Lankan mango exports, identify the pivotal drivers influencing mango exports, and analyze the performance of mango exports in Sri Lanka. This study utilized a panel dataset of 16 importing countries spanning the period from 2000 to 2021. The primary focus was on examining NTM-related challenges and obstacles faced by the mango export industry in Sri Lanka. The identification and quantification of these obstacles were achieved through a multifaceted approach in which assessment of NTM-related rejections, both domestic and overseas, evaluation of NTM incidences using frequency and coverage ratio measures, and the deployment of various gravity model equations to quantify the effect of NTMs were taken into consideration. Probit, Ordinary Least Square, Random Effects, and Poisson Pseudo Maximum Likelihood estimation techniques were utilized for data analysis.ResultsThe findings indicate a significant inverse correlation between the increase in the total count of NTMs and Sri Lankan mango exports if the country aims to export more mangoes to relevant markets, while Sanitary and Phytosanitary Measures (SPS), Technical Barriers to Trade (TBT), and Pre-shipment Inspection (PSI) contributed to increased mango exports. Among other explanatory variables, the GDP of both importing and exporting countries had a positive influence on Sri Lankan mango exports. Surprisingly, the distance to the importing country had no significant effect, though it shows a negative sign.DiscussionThis study provides valuable insights into the impact of NTMs on Sri Lankan mango exports. It highlights the considerable influence of NTMs on the overall fresh fruit export sector. We recommend proactively aligning internal quality testing regulations with the standards required by destination countries to promote future expansion.

Economic geography of talent migration and agglomeration in China: A dual-driver framework

The long-standing debate on the role of economic factors and amenities on talent migration and redistribution foreshadows the importance of spatial economic theoretical thinking in resolving the effects of duality, a point yet to be conclusively addressed in the existing literature. Situated within the framework of new economic geography (NEG), this article introduces an innovative theoretical model – the Dual-Driver (DD) framework– aiming at comprehending the nuanced dual impact of economic and amenity factors on talent movements between regions. While the DD framework retains the mechanisms of increasing returns to scale, iceberg transportation cost, and talent agglomeration presented in the NEG models, it depicts, for the first time, the self-reinforcing mechanisms between non-traded goods (i.e., amenities) and talent agglomeration. The model describes a logic of talent movements influenced by the dual drivers of regional economic factors (nominal wage, the diversity of local manufacturing products) and amenities (the diversity and quality of amenities, transfer payment for talent), among which the economic effects play a predominant role versus the amenity effects. Empirical evidence has been given by the geographical analysis of China's internal talent migration between 2000 and 2015 and the corresponding econometric analysis using the Poisson pseudo-maximum likelihood (PPML) estimation. Our findings furnish a theoretical perspective for comprehending talent geography and offer policy insights into China's logic of talent migration.

Semiparametric Probit Regression Model with General Interval-Censored Failure Time Data

Interval-censored data frequently arise in various biomedical areas involving periodical follow-ups where the failure or event time of interest cannot be observed exactly but is only known to fall into a time interval. This article considers a semiparametric probit regression model, a valuable alternative to other commonly used semiparametric models in survival analysis, to investigate potential risk factors for the interval-censored failure time of interest. We develop an expectation-maximization (EM) algorithm to conduct the pseudo maximum likelihood estimation (MLE) using the working independence strategy for general or mixed-case interval-censored data. The resulting estimators of regression parameters are shown to be consistent and asymptotically normal with the empirical process techniques. In addition, we propose a novel penalized EM algorithm for simultaneously achieving variable selection and parameter estimation in the case of high-dimensional covariates. The proposed variable selection method can be readily implemented with some existing software and considerably reduces the estimation error of the proposed pseudo-MLE approach. Simulation studies demonstrate the satisfactory performance of the proposed methods. An application to a set of interval-censored data on prostate cancer further confirms the utility of the methodology. Supplementary materials for this article are available online.

Unravelling open innovation determinants and firm performance relationships in CEE and SEE countries

Purpose Open innovation (OI), by now one of the major concepts for the analysis of innovation, is seen as a methodology for collaboratively designing and implementing solutions by engaging stakeholders in an iterative and inclusive service design process. This paper aims to empirically investigate OI capacities, defined as a cooperative, knowledge-sharing innovation ecosystem, and to explore how it can lead to improved performance of firms in Central and Eastern European (CEE) and Southeastern European (SEE) countries.Design/methodology/approach The study builds on the World Bank/European Bank for Reconstruction and Development (EBRD’s) Business Environment Enterprise Performance Survey (BEEPS) dataset for 2009, 2013 and 2019. Primarily, the research model was estimated using log-transformed ordinary least squares (OLS). Taking into consideration that this method might produce substantial bias, yielding misleading inferences, this study is fitting Poisson pseudo maximum likelihood estimators with robust standard errors and instrumental variable/generalized method of moments estimation (IV/GMM) approach for comparative results. Secondarily, the research model was tested using structural equation modelling (SEM) to investigate the relationship between five OI capacities and firm performance.Findings The findings indicate that there is a significant positive relationship between most OI capacities and firm performance, except for innovation, which did not show a statistically significant relationship with firm performance. Specifically, research and development (R&D), knowledge and coopetition are statistically significant and positively associated with firm performance, whereas transformation is statistically significant but negatively associated with firm performance. The IV/GMM estimations’ findings support the view that the firm performance is significantly affected by OI capacities, together with some control variables such as size, age, foreign ownership and year dummy to have a significant impact on firm performance.Originality/value This paper fills an identified gap in the literature by investigating the impact of OI on firm performance executed in the specific CEE and SEE country context.

How Social Networks Shape Refugee Movements in Wartime: Evidence from the Russian Attack on Ukraine

This article analyzes the key factors guiding the destination choice of Ukrainian refugees in the EU between March and December 2022 in the framework of the activation of the Temporary Protection Directive. To this end, it specifies a migration model, computed for the whole period and two subperiods (March–May and June–December), that captures the influence of social networks. Furthermore, our migration model makes a distinction for the first time between prewar migrant communities and the new social networks that are emerging in wartime circumstances. The estimation of the model, using the Poisson Pseudo-Maximum Likelihood estimator by Santos Silva and Tenreyro (2006), confirmed the importance of both types of social networks in explaining refugees’ choice of destination. The results also revealed that some economic variables (expected earnings and the size of the informal sector) influenced the location choice of refugees since the outbreak of the war, while other socioeconomic and political factors (risk of social exclusion, anti-immigration sentiment, and rule of law) only did so after a few months, when it became increasingly self-evident that the war was going on for a long time.