Pseudo Maximum Likelihood Method Research Articles

Impact of Anti-Dumping on Chinese Exports to Countries in ASEAN Plus 6

ABSTRACT Concerns about China’s excessive production capacity prompt a closer examination of Anti-dumping Duties (ADs) as a tool for managing trade imbalances. Within the ASEAN region, ASEAN plus 6 countries, especially India, are emerging as alternatives to China in global value chains. These countries import a broad range of products from China, including intermediate goods, making it vital to analyze the effects of ADs on these imports. Using the Poisson Pseudo-Maximum Likelihood (PPML) method, which effectively handles zero trade flows, heteroskedasticity, and endogeneity, this study explores the use of ADs beyond traditional major economies like the US and EU, focusing on their adoption by less economically developed countries. The findings show that ADs significantly curtail Chinese exports, potentially hindering stronger cooperation within ASEAN plus 6 and slowing regional trade dynamics. This research serves as a critical benchmark for assessing China’s strategic role in global economic leadership, viewed with reservations by several countries, including the US. It emphasizes the need to further explore China’s interaction with ASEAN plus 6 partner countries, particularly concerning trade remedy such as ADs and their impact on regional integration.

Institutional quality and emigration nexus: Empirical evidence from Türkiye

AbstractThis study attempts to fill an important gap in the empirical literature by investigating the impact of institutional quality in destination countries on emigrants from Türkiye using bilateral migration data from 2010 through 2020. For this purpose, the study builds an augmented gravity model, including economic, geographic, and cultural variables in an exponential form and estimates it by the Poisson Pseudo Maximum Likelihood (PPML) method. Empirical results significantly confirm the pulling role of strong institutions in destination countries for emigrants from Türkiye since all institutional quality indicators are positively associated with emigrant movements, albeit just one of them, regulatory quality, is statistically insignificant. Among indicators of institutional quality, the most pulling ones for emigrants are voice and accountability and the rule of law, followed by government effectiveness, control of corruption, and political stability and absence of violence, respectively. Overall results indicate that institutions are front‐line players in the emigrants' migration decision and destination choice process. In this context, policy‐makers in both Türkiye and the destination country may implement an institutional policy considering the outcomes stemming from migration movements.

Effects of logistics performance on Vietnam’s exports: a quantitative analysis using the PPML method

This study aims to analyze the effects of logistics performance on Vietnam’s exports at both the overall level and in specific aspects. We investigate the linkage between logistics performance and Vietnam’s exports based on a panel of bilateral trade from Vietnam to 158 trading partners from 2007 to 2018. Using the Poisson pseudo-maximum likelihood method, the positive and significant effects of Vietnam’s and importing countries’ logistics performance on exports are identified. Our estimates become highly consistent after controlling for both time-specific effects and time-invariant country-fixed effects and remain robust to changes in the model specification, contributing reliable empirical evidence to the literature. Considering the specific aspects of logistics performance, we find the significant roles of the ability to track and trace consignments, the quality of logistics services, the customs clearance efficiency, and the timeliness of shipments in trade. From Vietnam’s perspective, improvements in tracking and tracing ability and timeliness can enhance exports to other countries, with larger effects than those of trading partners. Based on these findings, this study provides evidence for policymakers to effectively enhance trade with other countries by developing the national logistics system alongside further logistics cooperation with trading partners and with a greater focus on tracking and tracing ability and timeliness.

Implementation of Adaptive-Bayesian DStoch technique for obtaining winds from MST radar covering higher altitudes

It is challenging to estimate winds accurately from higher altitudes using VHF-MST radar. The current study introduces the Adaptive-Bayesian Deterministic Stochastics Technique (ADStoch), which implements an Empirical Bayesian 1D prediction method using stochastics to analyze radar signals. A new and robust estimator for empirical wavelet shrinkage with Gaussian prior of the nonzero mean for wavelet coefficients is presented, which makes the current prior different from other priors. The mean parameters and the prior covariance hyperparameters follow a pseudo maximum likelihood method for computation. Details on the implemented algorithm developed from scratch using C# are also presented. This technique outperforms contemporary techniques discussed in this context that can recover signals buried in noise established based on the analysis of moment and quality. The estimated Wind is cross-validated for accuracy with the observed wind from the GPS radiosonde operated simultaneously. This technique can consistently extract 3D wind that can reach the range of 25.5 km–28.2 km, improving the conventional maximum altitude of 21.2 km in real time for the MST radar. It is concluded that the ADStoch analysis technique can effectively obtain VHF-MST radar signals at significantly higher altitudes, which is helpful in various scientific investigations.

Maximum Pseudo-Likelihood Estimation of Copula Models and Moments of Order Statistics

It has been shown that, despite being consistent and in some cases efficient, maximum pseudo-likelihood (MPL) estimation for copula models overestimates the level of dependence, especially for small samples with a low level of dependence. This is especially relevant in finance and insurance applications when data are scarce. We show that the canonical MPL method uses the mean of order statistics, and we propose to use the median or the mode instead. We show that the MPL estimators proposed are consistent and asymptotically normal. In a simulation study, we compare the finite sample performance of the proposed estimators with that of the original MPL and the inversion method estimators based on Kendall’s tau and Spearman’s rho. In our results, the modified MPL estimators, especially the one based on the mode of the order statistics, have a better finite sample performance both in terms of bias and mean square error. An application to general insurance data shows that the level of dependence estimated between different products can vary substantially with the estimation method used.

Revisiting India’s Export Potential: Modern Gravity Model Approach

The high contribution of India’s exports to its gross domestic product underscores the vulnerability of macroeconomic shocks like the COVID-19 in terms of losses in export revenues, increased cost of import and slowdown in growth. The scale of impact also depends on the degree of trade concentration, geographical expansion, export potential and strategic relationship of a country with its trading partners. This article aims to examine these factors using the modern gravity model approach. It also intends to analyse the export intensity and export potential of India with its 25 major trading partners. The results are estimated by using the Poisson pseudo-maximum likelihood method for the period 1991–2020. The results show that the exports between two nations are remarkably impacted by the trade cost and combined economic size. Furthermore, variables like population and islands are introduced in the extended gravity model, which positively impacts India’s exports. The direction of India’s exports witnessed a reversal in trend towards the traditional export destinations in 2020 compared to emerging countries from 2010 onwards. The export potential index also explains that India has the scope to enhance the export potential with developed nations as advocated by the Heckscher–Ohlin model. JEL Codes: F14, F49

Trade Destruction and Trade Diversion of Indian Anti-dumping Duties Against Bangladesh

In this study, we investigate Indian anti-dumping (AD) duties imposed on eight products (Harmonized System classification) from Bangladesh over the period 1998–2020. Using the Poisson pseudo-maximum likelihood method, panel regression analysis is applied to examine the relationship between Indian AD duties and goods imported from Bangladesh. Our results provide weakly suggestive significant evidence of trade destruction in the full sample, though statistical significance is enhanced for all product groups other than lead-acid batteries at the product-level investigation. We also provide suggestive evidence for trade diversion from AD duties. Overall, our results show Indian AD duties to be correlated with a decrease in imports from Bangladesh, and an increase in imports of goods from other, unnamed countries. This suggests that India’s protectionist measures may have been ineffective in protecting local producers, as any adverse effects of protectionism on Bangladesh may be offset by import diversion to other foreign suppliers. We also discuss the policy implications of these findings.

A Statistical Dependence Framework Based on a Multivariate Normal Copula Function and Stochastic Differential Equations for Multivariate Data in Forestry

Stochastic differential equations and Copula theories are important topics that have many advantages for applications in almost every discipline. Many studies in forestry collect longitudinal, multi-dimensional, and discrete data for which the amount of measurement of individual variables does not match. For example, during sampling experiments, the diameters of all trees, the heights of approximately 10% of the trees, and the tree crown base height and crown width for a significantly smaller number of trees are measured. In this study, for estimating five-dimensional dependencies, we used a normal copula approach, where the dynamics of individual tree variables (diameter, potentially available area, height, crown base height, and crown width) are described by a stochastic differential equation with mixed-effect parameters. The approximate maximum likelihood method was used to obtain parameter estimates of the presented stochastic differential equations, and the normal copula dependence parameters were estimated using the pseudo-maximum likelihood method. This study introduced the normalized multi-dimensional interaction information index based on differential entropy to capture dependencies between state variables. Using conditional copula-type probability density functions, the exact form equations defining the links among the diameter, potentially available area, height, crown base height, and crown width were derived. All results were implemented in the symbolic algebra system MAPLE.

Three-way gravity models with multiplicative unobserved effects

Summary This paper investigates three-way gravity models with multiplicative unobserved effects, which are popular in bilateral trade analysis and many other contexts. Such models are usually estimated by the fixed effects Poisson pseudo maximum likelihood method. As an alternative, we extend the estimation strategy proposed by Jochmans (2017) to our new settings by constructing moment conditions that are independent of the unobserved effects. Our method entails estimation of the conditional mean of a variety of functional forms. The generalized method of moments (GMM) estimator based on these moment conditions is N-consistent and asymptotically normally distributed. We also discuss the estimation of dynamic models by extending the linear feedback models to our three-way settings. Through various simulation designs, we show that our GMM estimator outperforms the competing Poisson pseudo maximum likelihood estimator. As an empirical application, we estimate the effects of the currency unions on exports.

Asymptotically Consistent Estimation of Preferential Attachments in Growing Networks

Preferential attachment, also referred to as the “rich-get-richer” mechanism, characterizes the ability of already existing nodes in an evolutionary network to acquire new connections from newly-coming nodes throughout the growing process of the network. This mechanism is responsible for the emergence of some critical structures in many real networks, such as the Saccharomyces cerevisiae protein-protein interaction network, scientific collaboration network, and bitcoin transaction network. Clearly, it is very important to accurately estimate the preferential attachment mechanism for a growing network in interest. Although it has long been aware that the existing widely-used methods yield significantly biased estimates of preferential attachments, the underlying mechanism is not completely understood and not well explained. By rewriting preferential attachment from a deterministic formulation to a stochastic one, this paper reveals two major problems existing in the current methodologies, thereby well explaining why traditional methods introduce biases to the estimates of preferential attachments. To avoid the negative effects of these two problems, a new method is proposed based on the Poisson Pseudo Maximum Likelihood method for estimating preferential attachments, which provides asymptotically consistent estimation to preferential attachments. More importantly, even with insufficient information, the new method can still provide an estimate with high precision, showing a significant advantage in measuring preferential attachments based on small-sized samples. The new method is also robust against network parameter changes. The findings may shed some lights onto deeper understanding of the evolutionary processes of real networks.

The effect of airport efficiency on air traffic, using DEA and multilateral resistance terms gravity models

This study evaluates the bias-corrected airport efficiency by employing a slacks-based measurement-data envelopment analysis model with a bootstrapping technique. The impacts of airport efficiency on bilateral passenger traffic and freight volume are then assessed by developing a novel robust gravity model incorporating multilateral resistance (MR) terms and the Poisson pseudo maximum likelihood (PPML) method to address heteroscedasticity. We collected air route data using Incheon airport with a sample of 50 airports in 28 countries for 2006–2015. The results from all the applied models revealed a significant and positive effect of airport efficiency on passenger traffic and freight volume.

Multivariate time series models for mixed data

We introduce a general approach which unifies some previous attempts for modeling the dynamic of multivariate time series or for regression analysis when the data are of mixed type (binary/count/continuous). Our approach is quite flexible since conditionally on past values, each coordinate at time t can have a distribution compatible with a standard univariate time series model such as GARCH, ARMA, INGARCH or logistic models whereas past values of the other coordinates play the role of exogenous covariates in the dynamic. The simultaneous dependence in the multivariate time series can be modeled with a copula. Additional exogenous covariates are also allowed in the dynamic. We first study some usual stability properties of these models and then show that autoregressive parameters can be consistently estimated equation-by-equation using a pseudo-maximum likelihood method, leading to a fast implementation even when the number of time series is large. Moreover, we prove consistency results when a parametric copula model is fitted to the time series and in the case of Gaussian copulas, we show that the likelihood estimator of the correlation matrix is strongly consistent. We carefully check all our assumptions for two prototypical examples: a GARCH/INGARCH model and logistic/log-linear INGARCH model. Our results are illustrated with numerical experiments as well as two real data sets.

On the central limit theorem for a conditional mode estimator in the single functional index modeling for functional time series data under random censorship

The main objective of this paper is to investigate the estimation of conditional density function based on the single-index model in the censorship model when the sample is considered as an dependent random variables. First of all, a kernel type estimator for the conditional density function (cond-df) is introduced. Afterwards, the asymptotic properties are stated when the observations are linked with a single-index structure. The pointwise almost complete convergence and the uniform almost complete convergence (with rate) of the kernel estimate of this model are established. As an application the conditional mode in functional single-index model is presented. Under general conditions, the asymptotic normality of the conditional density estimator is established. Simulation study is also presented to illustrate the validity and finite sample performance of the considered estimator. Finally, the estimation of the functional index via the pseudo-maximum likelihood method is discussed, but not tackled.

Modeling of the Nexus Between Environmental Regulations of Trade Partners and Export Volumes: Analysis of Russian Regions

Export development is one of the priorities of the Russian economy. Exporters and companies planning to enter the international market face a number of limiting factors. Transitional climate risks, mainly represented by trading partner countries' environmental regulation aimed at internalizing national and global negative environmental externalities, are also among these factors. However, the global climate agenda can not only have a negative impact on export performance, but also generate growth opportunities. The purpose of this study is the econometric modeling of the impact of environmental regulation of trading partner countries on the dynamics of export volumes of Russia's regions based on data for the period of 2013-2020. In addition, the study is aimed at identifying regional factors that determine the sign of the influence of climate regulation of importing countries on export volumes. The study assumes that the effect of climate regulation on export performance is different for the regions of Russia and is determined by institutional and environmental characteristics. The estimation of the gravity equation using the Poisson pseudo-maximum likelihood method for different subsamples of Russia's regions demonstrated that the stringency of environmental regulation of the partner countries is negatively related to the volumes of Russian exports, but there is a positive correlation in the case of regions characterized by favorable conditions for innovation activity and active environmental policy. It was also detected that the regional budget spending on environmental protection do not provide a stimulating effect of climate regulations. Thus, in order to minimize the negative impact of transitional climate risks, special attention of the state should be paid to creating an environment conducive to innovation and the formation of the most effective environmental policy at the regional and national levels. The theoretical significance of the study lies in expanding the traditional gravity model by including the environmental determinants of trade, while the practical significance of the work is represented by recommendations to regional authorities on minimizing the negative impact of transitional climate risks.

Quality of Indonesia’s domestic institutions and export performance in the era of global value chains

The focus of this research is on analysing the effects of institutional quality and trade agreements on Indonesia bilateral trade in four commodity groups in the era of global value chain (GVC). Employing a panel data gravity model of Indonesian export of four commodities to Indonesia’s trading partners from 2000 to 2018 and estimating the Feasible Generalized Least Squares (FGLS) and the Poisson Pseudo Maximum Likelihood Method (PPML), the results of this study find that institutional quality plays a different role in explaining Indonesia’s export performance of those four commodities, despite the fact that institutions contribute positively. On two commodities that are prospective to increase Indonesia participation in the global value chain, exported intermediates and consumer goods, we find that although both domestic institutions and trading partner institutions are significant to Indonesian export, but we argue that domestic institutions contribute more. This study also reveals that through tariff reductions, Indonesian trade agreements are significant regarding export of all the four commodities group. However, even though tariffs are significant, we find that the quality of institutions, especially domestic institutions, has a greater effect on Indonesia's exports.

THE ORDINAL LOGISTIC REGRESSION MODEL WITH SAMPLING WEIGHTS ON DATA FROM THE NATIONAL SOCIO-ECONOMIC SURVEY

Ordinal logistic regression is a method describing the relationship between an ordered categorical response variable and one or more explanatory variables. The parameter estimation of this model uses the maximum likelihood estimation having assumption that each sample unit having an equal chance of being selected, or using simple random sampling (SRS) design. This study uses data from the National Socio-Economic Survey (SUSENAS) having two-stage one-phase sampling (not SRS). So, the parameter estimation should consider the sampling weights. This study describes the parameter estimation of the ordinal logistic regression with sampling weight using the pseudo maximum likelihood method, especially in SUSENAS sampling design framework. The variance estimation method uses Taylor linearization. This study also provides numerical examples using ordinal logistic regression with sampling weight. Data used is 121,961 elderly spread over 514 districts/cities. Testing data (20%) is used to obtain the accuracy of the prediction results. The variables used in this study are the health status of the elderly as the response variable, and nine explanatory variables. The results of this study indicate that the ordinal logistic regression model with sampling weights is more representative of the population and more capable to predict minority categories of the response variable (poor and moderate health status) than is without sampling weights.

Impact of ASEAN- India Free Trade Agreement (AIFTA) on Palm Oil Trade

Aims: To analyse the trade creation and trade diversion effect of the ASEAN-India Free Trade Agreement (AIFTA) on palm oil trade among the member countries.
 Data and Methodology: Data on palm oil exports of major Association of South East Asian countries (ASEAN) member countries such as Indonesia (64.10%), Malaysia (31.52%), Thailand (2.18%), Singapore (1.95%) and Philippines (0.25%) were collected for the period of 21 years (2000–2020) and used for the analysis. The Gravity model by Ordinary Least Square (OLS) and Poisson Pseudo-Maximum Likelihood (PPML) methods were estimated with the panel data.
 Results: As expected gravity model variables the distance had a negative impact on the palm oil trade between India and ASEAN member countries. The tariff rates and export prices had a negative impact on the palm oil trade whereas; the population of ASEAN members (exporters), GDP of ASEAN members and exchange rate were insignificant. The AIFTA had a positive and significant effect on the palm oil trade from ASEAN member countries to India which denoted the trade creation effect between ASEAN members and India.
 Conclusion: The analysis showed a pure trade creation effect of AIFTA in the time and country fixed effect model. The results suggested that the palm oil trade could be pushed for further trade liberalisation in the AIFTA.

DETERMINANTS OF WASTE IMPORT IN TURKEY WITHIN THE FRAMEWORK OF THE CIRCULAR ECONOMY: ANALYSIS OF THE GRAVITY MODEL

Sanayileşme ve küreselleşmenin etkisi ile birlikte tüketim ve üretimdeki artış eğilimi, açığa çıkan atık miktarında da artış yaşanmasına neden olmaktadır. Son yıllarda giderek artan ve çevre ve sağlık açısından ciddi olumsuz etkileri bulunan atıkların, ekonomik yarar sağlayacak kanallara yönlendirilmesi gerektiği görüşü ön plana çıkmaktadır. Böylece doğrusal ekonomideki al-yap-kullan-at görüşünün yerini döngüsel ekonomi çerçevesinde al-yap-yeniden yap görüşü almaya başlamıştır. Atıkların azaltılması, geri dönüşümü ve yeniden kullanımı stratejilerinin benimsendiği döngüsel ekonomi çerçevesinde atıkların potansiyel bir ikincil hammadde olması, enerjiye dönüştürülebilmesi ve ikinci el mal ticareti yoluyla ekonomik yararlar sağlayabileceği düşünülmektedir. Küresel boyutta döngüsel ekonominin stratejilerinin sürdürülebilirliği ülkelerin tek başına uyguladıkları politikalarla yeterli olmayacağı için ülkeler arası atık ticaretini gerektirmektedir. Bu kapsamda ülkeler arası atık ticareti ile atıkların, sınıflandırma ve işleme süreçleri açısından karşılaştırmalı üstünlüğe sahip olan ülkelere yönlendirilmesi ve böylece küresel boyutta ekonomik kazanımlar sağlanması amaçlanmaktadır. Çalışmada dünya atık ithalatında önemli rol oynayan Türkiye’nin atık ithalatını etkileyen faktörlerin çekim modelinden hareketle belirlenmesi amaçlanmaktadır. Bu bağlamda Türkiye’nin atık ithalatının belirleyicileri 2006-2020 dönemi verileri kullanılarak, ticaret ortağı olan 63 ülke için çekim modelinden hareketle Poisson Pseudo Maximum Likelihood (PPML) yöntemi ile analiz edilmiştir. Analiz sonucunda ihracatçı ülkenin gelir düzeyinin ve nüfusunun artmasının Türkiye’nin atık ithalatını arttırıcı yönde etkilediği, ülkeler arası uzaklığın artmasının, atık ihracatçısı olan ülkelerin karalarla çevrili olmasının ve atık ihracatçısı ülkenin çevresel performansındaki ve yolsuzluğun kontrolündeki iyileşmelerin Türkiye’nin atık ithalatını sınırlandırıcı etkide bulunduğu tespit edilmiştir.

The institutional determinants of outward foreign direct investment

Abstract This study focuses on the influence of institution quality on foreign direct investment (FDI) outflows. For empirical estimation, we use a dataset covering 102 home and 67 host countries from 2001 to 2016. We use the gravity approach and apply the Poisson pseudo maximum likelihood method to derive unbiased estimates. A set of institutional variables in a country is integrated into a single institutional index using principal component analysis. Our main findings are the following. First, we only identify a positive influence of the level of institutional development on FDI outflows for the institutionally developed countries. Second, we have not found evidence for crowding out national investment in the countries with weak institutions. Third, increases in the level of institutions stimulate horizontal rather than vertical outward FDI in an economy. Finally, institutional distance negatively affects the level of outward FDI only when the institutional distance between the two countries is large. The policy implications of this research are strongly in favour of further developing institutions.

Analyzing the impact of Fukushima nuclear wastewater discharge on seafood trade with gravity model

The immediate influence of Fukushima nuclear wastewater (FNW) discharge is the world seafood trade. The gravity model is introduced to capture the World seafood trade under the effect of FNW discharge with the consideration of the trade communities and public opinions. The trade communities capture the closeness of seafood trade relations among countries and is investigated by the complex network method. The public opinions are related to the citizen's attitudes on FNW discharge, which affects the consumption of seafoods from Japan and thus the trade quantities. The validity of the proposed model is clarified by the Poisson Pseudo Maximum Likelihood (PPML) methods. The proposed model provides the more reasonable estimation of the seafood trade quantities among the main seafood trade countries by incorporating the factors of trade communities and public opinions. With 10-year seafood trade data and public opinions from Google Trends on FNW discharge, we found that the trade community and public opinion have significant impacts on the seafood trade quantities between Japan and the counties in the same community.