Pseudo Maximum Likelihood Research Articles

TURKISH ECONOMY IN THE BELT ROAD INITIATIVE: A GRAVITY MODEL FOR INTERNATIONAL TRADE

In this paper, we attempt to analyze the determinants of international trade flows between Turkey and countries in the One Belt One Road (OBOR) project and their implications for the Turkish economy through the estimation of panel data using a gravity model. In particular, we employ the Poisson Pseudo Maximum Likelihood (PPML) estimation procedure for the nine OBOR nations from the period 2000-2021. This estimator is more robust than other methods such as pooled, fixed, and random models and it is free from the problems of autocorrelation, asymptotic bias, multicollinearity, and heteroscedasticty in the residuals for estimation and inference. First, panel unit root tests show that conducted variables are stationary at the conventional level. Second, the gravity estimations revealed that remoteness, scale, land endowment variables and, dummies (Preferential Trade Agreement and Border) in the gravity model have a positive impact, while GDP Distance and Capital Endowment have an inverse effect on trade flows. Only the language dummy is statistically insignificant. For policy purposes, we concluded that it would be better to promote trade flows with the OBOR countries.

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.

Predicting Multi-Period Corporate Default Based on Bayesian Estimation of Forward Intensity—Evidence from China

We employed a forward intensity approach to predict the multi-period defaults of Chinese-listed firms during the period 2001–2019 on a monthly basis. We introduced the firm’s default heterogeneity into the model, and each firm’s actual past default situation was considered for Bayesian estimation. Maximum pseudo-likelihood estimation was conducted on 3513 firms to calculate the parameters of the Bayesian model to adjust the default intensity of all 4216 firms. Finally, we re-calculated the default probabilities and compared them with the original default probabilities of the out-of-sample 703 firms for all prediction horizons. We found that the Bayesian model, considering the firm’s default heterogeneity, improved the prediction accuracy ratio of the out-of-sample firm’s default probabilities both for short and long horizons. As compared with the original model, the prediction accuracy ratio of the out-of-sample’s default probabilities, which were computed by our model, increased by almost 15% for horizons from 1 month to 6 months. When the horizon was extended from 1 year to 3 years, the prediction accuracy ratio increased by more than 10%. We found that the Bayesian model improved the predictive performance of the forward intensity model, which is helpful to improve the credit risk measurement system of Chinese-listed firms.

DO ICT AND TECHNOLOGICAL DEVELOPMENT FACILITATE SUPPLY CHAIN TRADE?

This paper aims to estimate the impact of ICT and technological development of countries on supply chain trade. As proxy measures of supply chain trade, the author applies domestic value added (DVAFX) included in gross-foreign exports, and foreign-value added included in domestic exports (FVA) from the Eora MRIO database while constructing the ICT variable by using confirmatory factor analysis. Furthermore, as a proxy variable for the country’s technological development, the author applies the economic complexity index developed by Harvard’s Growth Lab, conducting her analysis based on the structural gravity model estimated with the Poisson Pseudo-Maximum Likelihood Estimator (PPML). Together with the standard gravity regressors, the model includes the bilateral position of countries in the supply chain. Our panel dataset covers the 2000-2019 period, including 130 world countries. To control for the potential source of endogeneity, the model includes reporter and partner country fixed effects, yearly fixed effects, and country-pair fixed effects. The results confirm a statistically significant relationship between the country’s technological development and supply-chain trade. Technological development enables a country to produce more complex, higher value-added products and thus to be more upstream positioned in the supply chain. The more upstream the country is in the supply chain, the higher the domestic value added is included in its exports.

Facilitating trade in intermediate goods

A side effect of economic globalisation and new information and communication technologies is the increasing fragmentation of the production process across different countries and continents, contributing to the rise of trade in intermediate goods, which has increased to almost 2/3 of total world trade. Most of this trade, i.e., intermediate goods, are transported by sea. Maritime trade is one of the most economical but also complex ways of trading and transporting goods, requiring good coordination, various stops and controls, transhipments, storage, ICT technology to track the cargo, etc. Therefore, the objective of this paper is to quantify the impact of trade facilitation on trade in intermediate goods on the sample of EU28 countries, using biennial data for the period 2010-2018. We estimate augmented gravity model on bilateral trade data using a Poisson Pseudo Maximum Likelihood (PPML) estimator. Our results suggest that, as expected, logistics, and in particular ICT development, has a significant and positive effect on trade in intermediate inputs, when controlling for other variables in the gravity model such as GDP of trading partners, distance, contiguity, existence of a free trade agreement, exchange rate, and common cultural proxies. Our results support the global trend of development and investment in logistics, and, in particular, new ICT technologies, which can not only contribute to the continued growth of trade in intermediates, but also help mitigate the negative effects of recent global economic shocks.

Empirical Analysis of Sustainable Trade Effects of FTAs Based on Augmented Gravity Model: A Case Study of China

China has developed many strategies to promote sustainable trade, including accelerating the signing of free-trade agreements (FTAs). However, there is a lack of studies examining the impact of FTAs on the sustainable growth of foreign trade from a holistic perspective. This paper applies an augmented gravity model to verify the trade creation and diversion effects of China’s FTAs that are currently in effect. A panel data analysis over the period of 1995–2019 is conducted on 34 countries and regions, including China, and Poisson pseudo-maximum likelihood (PPML) is employed to test three different fixed-effects models. The results show that the trade effects differ across FTAs, with nine FTAs generally making positive contributions to China’s trade and social welfare growth, and the remaining five FTAs exhibiting varying degrees of trade diversion or contraction effects. Empirically, it is clear that participation in FTAs has contributed to the sustainable trade of China, but China still needs to take prudent action to pursue further trade liberalization in order to counter the potential threat of trade diversion.

Assessing the Impacts of Non-Tariff Measures on the Export of Vietnam’s Main Agricultural and Seafood Products

With the development of free trade agreements, tariff barriers are gradually removed, and non-tariff measures are playing an important role in the trade policies of countries. However, studies on the effects of non-tariff measures on trade show a great disparity in results and both negative and positive influences can be found when non-tariff measures are imposed in different contexts. The heterogeneity in the result is often hypothesized to be the consequence of intrinsic characteristics related to products and countries; therefore, arguably, unbiased and reliable estimation of non-tariff measure effect on trade should be analyzed in product level data for a specific country. Hence, the study attempts to use the gravity model and Poisson Pseudo-Maximum Likelihood estimation (PPML) to quantify the effects of non-tariff measures on the export of some agricultural and seafood products of Vietnam to certain main markets including the United States (US), European Union (EU), China, and Japan. The research findings show that from a national perspective, non-tariff measures mostly have a negative impact on agricultural exports, especially sanitary and phytosanitary measures (SPM), and countervailing measures (CM). In terms of commodities, coffee and seafood are particularly sensitive to non-tariff measures, while other key agricultural product groups are mostly affected by tariff measures.

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.

The Impact of Competition Policy Provisions in Regional Trade Agreements on Intermediate Product Trade

The deepening of the content of regional trade agreements has promoted the deep integration of global value chains. As an important part of the behind-the-border measures of regional trade agreements, competition policy provisions have an important impact on intermediate product trade. This paper constructs the depth index of competition policy provisions, uses the trade gravity model combined with Poisson's pseudo maximum likelihood estimation (PPML) and fixed effects to empirically analyze the impact of competition policy depth on the scale of intermediate product trade, and finally proposes policy recommendations for China to improve the construction level of regional trade agreements according to the conclusions.

Impact of regional trade agreements on bilateral trade flows: A Systemic Literature Review

This paper reviews the theoretical and empirical literature on the impact of regional trade agreements on trade. The empirical literature is arranged based on the econometricmethods used to estimate the gravity model. Advantages and disadvantages of each method were highlighted. Papers covering RTAs from Africa, Asia, the Americas, and Europe were reviewed to gain a more representative understanding. The covered empirical literature suggests that the Poisson Pseudo Maximum Likelihood (PPML) estimator is more reliable than OLS in estimating gravity models, as it can deal with zero trade flows. The Fixed Effects (FE) approach produces more consistent estimates than the Random Effects (RE) approach when quantifying the effects of RTAs. This is because it allows one to control for the unobserved time-invariant variables. Surprisingly, the covered literature suggests that a great majority of African RTAs generated trade, regardless of the method of estimation used. Journal of Economic Literature (JEL) codes: F13, F15

How do exchange rates affect the Big One? An empirical analysis of the effect of exchange rates on RCEP exports using the gravity model

The often disparate and conflicting effects of exchange rate on bilateral exports reported by previous literature necessitate a further study of the relationship between monetary and trade variables. This study contributes to the stream of literature by analyzing monetary variables such as exchange rate volatility, exchange rate misalignment, exchange rate regimes, and real effective exchange rates with bilateral aggregate exports through a sample of 15 nations comprising the Regional Comprehensive Economic Partnership (RCEP) region for the years 1996 to 2017 using Ordinary Least Squares and Poisson Pseudo-Maximum Likelihood panel fixed effects regression. Results indicate that a country’s real effective exchange rate ratio and the exchange rate volatility for countries under a floating exchange rate regime reduce aggregate exports.

Scalable Semiparametric Spatio-temporal Regression for Large Data Analysis

With the rapid advances of data acquisition techniques, spatio-temporal data are becoming increasingly abundant in a diverse array of disciplines. Here, we develop spatio-temporal regression methodology for analyzing large amounts of spatially referenced data collected over time, motivated by environmental studies utilizing remotely sensed satellite data. In particular, we specify a semiparametric autoregressive model without the usual Gaussian assumption and devise a computationally scalable procedure that enables the regression analysis of large datasets. We estimate the model parameters by maximum pseudolikelihood and show that the computational complexity can be reduced from cubic to linear of the sample size. Asymptotic properties under suitable regularity conditions are further established that inform the computational procedure to be efficient and scalable. A simulation study is conducted to evaluate the finite-sample properties of the parameter estimation and statistical inference. We illustrate our methodology by a dataset with over 2.96 million observations of annual land surface temperature, and comparison with an existing state-of-the-art approach to spatio-temporal regression highlights the advantages of our method.Supplementary materials accompanying this paper appear online.

Marginal proportional hazards models for multivariate interval-censored data.

Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption that all event times are independent, and we provide a simple and stable EM-type algorithm. The resulting nonparametric maximum pseudolikelihood estimators for the regression parameters are shown to be consistent and asymptotically normal, with a limiting covariance matrix that can be consistently estimated by a sandwich estimator under arbitrary dependence structures for the related event times. We evaluate the performance of the proposed methods through extensive simulation studies and present an application to data from the Atherosclerosis Risk in Communities Study.

Estimation method for mixture copula models in hydrological context

Hydrological extreme events are characterized by several correlated variables. For a better associated risk assessment, the dependence structure between these variables must be taken into account by considering copulas. On the other hand, extreme events are generated from different phenomena. In such cases, the margins and/or copula may be affected. Hence, mixture copula should be considered. Recently, there have been an increasing number of studies dealing with the parameter estimation of mixture copula. However, existing methods have several drawbacks. To overcome these drawbacks, we propose a new parameter estimation approach for the mixture copula models, based on the maximum pseudo-likelihood using a metaheuristic algorithm. A simulation study is conducted to evaluate the performance of the proposed method and to compare it with those of the widely used existing method. Results indicate that the proposed method estimates more accurately the parameters even with small sample sizes compared to the existing ones. An application to a real data set is also provided and validated with the available data.

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.

Institutional Quality and Intra-Regional Trade Flows: Evidence from ECOWAS

Since institutional quality can either create or destroy incentives for individuals to engage in trade, it has become a source of worry to policymakers, as it can limit both intra- and extra-regional trade. Based on this, we empirically analyzed the extent to which national institutional quality affects bilateral trade flows in ECOWAS based on a gravity model for the period from 2000 to 2018. Specifically, the study employs the negative binomial pseudo-maximum likelihood estimator (NBPML). The results reveal that institutional variables with both aggregated and disaggregated measures of the quality of institutions have a significant and positive impact on trade flows in ECOWAS and on its sub-samples, WAEMU and WAMZ. The results further indicate that for both importing and exporting countries, reduced corruption, effective rule of law, and effective government coincide with more trade among member countries. The degree of regional integration is an important determinant of intra-ECOWAS trade, as are GDP, GDP per capita, common language, and landlockedness.

The Determinants of China’s Services Trade

This paper examines the determinants of services trade in China with a panel of 42 trading partners during the period of 2000–2014. Using augmented Gravity model, the estimated results from Poisson Pseudo Maximum Likelihood (PPML) show that the sectoral output of China and its trading partners, sharing of common borders, fixed telephone subscription in China, exchange rate, perception of lower corruption in partner countries contribute positively to services trade between China and its trading partners. On the contrary but not surprisingly, trade restrictiveness and distance between capitals of China and its trading partners are negatively associated with services trade between China and its counterparts. Greater availability and adoption of information and communication technology in partner countries, proxied by fixed telephone subscription, appear to have a negative (positive) effect on China’s services exports (imports). On the policy front, this paper suggests that the Chinese government should devise policies to deal with non-tariff trade restrictions and improve the country’s telecommunication infrastructure to address the services trade gaps with its trading partners.

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.