Trade characteristics of foreign direct investment inflows in China

2016 ◽  
Vol 6 (2) ◽  
pp. 177-207 ◽  
Author(s):  
Xinzhong Li ◽  
Seung-Rok Park
Keyword(s):  
International Trade ◽  
Probability Distribution ◽  
Count Data ◽  
Negative Binomial ◽  
Distribution Model ◽  
Continuous Data ◽  
Fdi Inflows ◽  
Content Type ◽  
The Past ◽  
Model Based

Purpose – The purpose of this paper is to indicate trade characteristics of Foreign direct investment (FDI) inflows in China and examine the dynamic interaction between FDI inflows and China’s international trade through empirical analysis. Design/methodology/approach – At first, this paper builds the probability distribution model (Poisson and negative binomial (NB)) to capture the characteristics of spatial distribution of all kinds of FDI firms in Chinese cities and provinces based on count data, so as to indicate the potentials for further introducing FDI inflows in China; Second, this paper investigates the effects of trade on FDI firms inflows based on probability regress model (Binary Logit, Tobit, NB, Poisson, zero inflated negative binomial) and shows how international trade accelerates the different kinds of FDI firms to agglomerate in Eastern, Middle and Western region by the endowments of factors; third, this paper empirically examines the magnitude and characteristics of trade effects generated by FDI inflows by building dynamic panel model based on continuous data. Findings – First, statistical tests of probability distribution model based on count data show that there are characteristics of spatial agglomeration of FDI firms such as manufacture firm, R & D firm, managing and marketing firm and total sectors, which obey NB distribution as whole; Second, this study indicate that FDI inflows have strong positive effects on the international trade in China’s provinces and on China’s regional trade, and that most of foreign firms in China are export oriented being strongly characterized as labor-intensive industries, especially, contributions of FDI to imports are greater than the contributions of FDI to exports in China’s Middle and Western trade, and the growth of FDI trade in China’s trade volume has been strong over the past years; third, the empirical results of models based on count data and continuous data indicate that FDI inflows have significantly positive relationship with international trade, that is, the relationship between FDI and international trade in the case of China is the characteristics with complement and imports substituting relationship. Research limitations/implications – Because of mixed data set for FDI inflows of processing and assembling trade and production-oriented FDI, efficiency-seeking and knowledge or technology – intensive FDI inflows in the past 36 years, the paper only investigate characteristics of FDI inflows in China before the turning point of financial crisis, but it is important for capturing the whole picture of trade characteristics of FDI inflows in China. Practical implications – The derived quantitative results imply that there are still greater potentials for further introducing FDI inflows in China, and decision-maker should make policy of introducing FDI inflows which are favorable to supporting innovative activities and economic agglomeration, and preferably encourage efficiency-seeking and export-oriented FDI inflows so as enhance quality and efficiency of economic growth, which are also helpful to accelerate upgrade of Chinese industry and gradually shorten gap of growth among Eastern, Middle and Western region. Social implications – FDI inflows in China not only stimulate the remarkable growth of bilateral trade between host country and home country, but also promote the growth of international trade between China and the rest of the world. Thus, policies of bilateral or multilateral free-trade and investment area should be encouraged, which will be also favorable to promote the growth and welfare in all the regions. Originality/value – This paper demonstrates that spatial distributions of FDI firms in Chinese cities and provinces obey NB probability distribution pattern, and puts forward the methodology of model based on count data and continuous data. Besides, this paper quantitatively indicates trade characteristics of FDI inflows in China as well as the dynamic interaction between FDI inflows and China’s international trade.

Endogenous thresholds for the determinants of FDI inflows: evidence from the MENA countries

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatma Taşdemir
Keyword(s):  
International Trade ◽  
Financial Development ◽  
Natural Resource ◽  
Developing Economies ◽  
Emerging Market ◽  
Push Factors ◽  
Fdi Inflows ◽  
Content Type ◽  
Financial Depth ◽  
Determinants Of Fdi

PurposeThis paper investigates the main drivers of foreign direct investment (FDI) inflows for a balanced panel of 11 Middle East and North Africa (MENA) economies over the 1995–2017 annual period. The author postulates that the impacts of the main pull (growth) and push (global financial conditions, GFC) factors may not be invariant to endogenously estimated thresholds for structural domestic conditions (SDCs) including trade and capital account openness, financial development, human capital (HC) and natural resource endowments.Design/methodology/approachThe author investigates whether the main SDC provide endogenous thresholds for the impacts of basic pull and push factors on FDI inflows for the MENA sample by employing panel fixed effects threshold procedure of Hansen (1999). As a robustness check, the author also present the results of the dynamic panel data two-step system generalized method of moments (GMM) estimation, which explicitly consider the potential endogeneity of SDC along with main pull factor for the evolution of FDI inflows.FindingsGrowth, GFC and SDC are important drivers of FDI inflows. The impacts of SDC tend to be higher in countries with higher financial depth, openness to international trade and finance and lower natural resource and HC endowments. The sensitivities of FDI inflows to GFC are substantially higher in the countries which are more open to international trade and capital flows and higher levels of financial depth. FDI inflows are found to be pro-cyclical and this pro-cyclicality tends to be much higher for the episodes exceeding the SDC thresholds.Practical implicationsImproving SDC including higher openness to international trade and finance and financial development may be effective in encouraging FDI inflows. The findings support an argument that, better SDC are crucially important not only for attracting FDI but also achieving the growth benefits of FDI inflows. Therefore, improving SDC appears to be an important growth-oriented policy agenda for emerging market and developing economies (EMDEs) including MENA.Originality/valueThe impacts of the main push and pull factors on FDI (and capital) inflows may be nonlinear. The literature often tackles the nonlinearity issue either by some interaction specifications or imposing exogenous thresholds. The literature, however, is yet to comprehensively investigate whether the main SDC provide endogenous thresholds for the impacts of basic pull and push factors. The author aims to contribute to the literature by estimating endogenous SDC threshold levels for the impacts of the main determinants of FDI flows for MENA.

Modelling of wear debris in planetary gear drive

2019 ◽  
Vol 71 (2) ◽  
pp. 199-204
Author(s):  
Rakesh Ranjan ◽  
Subrata Kumar Ghosh ◽  
Manoj Kumar
Keyword(s):  
Probability Distribution ◽  
Aspect Ratio ◽  
Wear Debris ◽  
Planetary Gear ◽  
Wear Particle ◽  
Distribution Model ◽  
Wear Particles ◽  
Slab Caster ◽  
Content Type ◽  
Exponential Probability

Purpose The probability distribution of major length and aspect ratio (major length/minor length) of wear debris collected from gear oil used in planetary gear drive were analysed and modelled. The paper aims to find an appropriate probability distribution model to forecast the kind of wear particles at different running hour of the machine. Design/methodology/approach Used gear oil of the planetary gear box of a slab caster was drained out and charged with a fresh oil of grade (EP-460). Six chronological oil samples were collected at different time interval between 480 and 1,992 h of machine running. The oil samples were filtered to separate wear particles, and microscopic study of wear debris was carried out at 100X magnification. Statistical modelling of wear debris distribution was done using Weibull and exponential probability distribution model. A comparison was studied among actual, Weibull and exponential probability distribution of major length and aspect ratio of wear particles. Findings Distribution of major length of wear particle was found to be closer to the exponential probability density function, whereas Weibull probability density function fitted better to distribution of aspect ratio of wear particle. Originality/value The potential of the developed model can be used to analyse the distribution of major length and aspect ratio of wear debris present in planetary gear box of slab caster machine.

scholarly journals Bayesian Models for Zero Truncated Count Data

2019 ◽  
pp. 1-12
Author(s):  
Olumide S. Adesina ◽  
Dawud A. Agunbiade ◽  
Pelumi E. Oguntunde ◽  
Tolulope F. Adesina
Keyword(s):  
Count Data ◽  
Mixed Models ◽  
Poisson Regression ◽  
Selection Criteria ◽  
Negative Binomial ◽  
Geometric Model ◽  
Suitable Model ◽  
The Past ◽  
Bayesian Monte Carlo ◽  
Multi Level

It is important to fit count data with suitable model(s), models such as Poisson Regression, Quassi Poisson, Negative Binomial, to mention but a few have been adopted by researchers to fit zero truncated count data in the past. In recent times, dedicated models for fitting zero truncated count data have been developed, and they are considered sufficient. This study proposed Bayesian multi-level Poisson and Bayesian multi-level Geometric model, Bayesian Monte Carlo Markov Chain Generalized linear Mixed Models (MCMCglmms) of zero truncated Poisson and MCMCglmms Poisson regression model to fit health count data that is truncated at zero. Suitable model selection criteria were used to determine preferred models for fitting zero truncated data. Results obtained showed that Bayesian multi-level Poisson outperformed Bayesian multi-level Poisson Geometric model; also MCMCglmms of zero truncated Poisson outperformed MCMCglmms Poisson.

scholarly journals Multifactorial Effects of Ambient Temperature, Precipitation, Farm Management, and Environmental Factors Determine the Level of Generic Escherichia coli Contamination on Preharvested Spinach

2015 ◽  
Vol 81 (7) ◽  
pp. 2635-2650 ◽  
Author(s):  
Sangshin Park ◽  
Sarah Navratil ◽  
Ashley Gregory ◽  
Arin Bauer ◽  
Indumathi Srinath ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Negative Binomial ◽  
Precipitation Amount ◽  
Farm Management ◽  
Content Type ◽  
E Coli ◽  
The Past ◽  
Average Maximum ◽  
Average Precipitation ◽  
Contamination Event

ABSTRACTA repeated cross-sectional study was conducted to identify farm management, environment, weather, and landscape factors that predict the count of genericEscherichia colion spinach at the preharvest level.E. coliwas enumerated for 955 spinach samples collected on 12 farms in Texas and Colorado between 2010 and 2012. Farm management and environmental characteristics were surveyed using a questionnaire. Weather and landscape data were obtained from National Resources Information databases. A two-part mixed-effect negative binomial hurdle model, consisting of a logistic and zero-truncated negative binomial part with farm and date as random effects, was used to identify factors affectingE. colicounts on spinach. Results indicated that the odds of a contamination event (non-zero versus zero counts) vary by state (odds ratio [OR] = 108.1). Odds of contamination decreased with implementation of hygiene practices (OR = 0.06) and increased with an increasing average precipitation amount (mm) in the past 29 days (OR = 3.5) and the application of manure (OR = 52.2). On contaminated spinach,E. colicounts increased with the average precipitation amount over the past 29 days. The relationship betweenE. colicount and the average maximum daily temperature over the 9 days prior to sampling followed a quadratic function with the highest bacterial count at around 24°C. These findings indicate that the odds of a contamination event in spinach are determined by farm management, environment, and weather factors. However, once the contamination event has occurred, the count ofE. colion spinach is determined by weather only.

scholarly journals Graphic Model-based Gene Regulatory Network Reconstruction using RNA Sequencing Count Data

2019 ◽  
Vol 8 ◽  
pp. 1078-1085
Author(s):  
Liliana Lopez-Kleine ◽  
Cristian Andres Gonzalez-Prieto
Keyword(s):  
Rna Sequencing ◽  
Negative Binomial Distribution ◽  
Count Data ◽  
Binomial Distribution ◽  
Regulatory Networks ◽  
Graphical Model ◽  
Negative Binomial ◽  
Sequencing Data ◽  
Model Based ◽  
Gene Regulatory

Interactions between genes, such as regulations are best represented by gene regulatory networks (GRN). These are often constructed based on gene expression data. Few methods for the construction of GRN exist for RNA sequencing count data. One of the most used methods for microarray data is based on graphical Gaussian networks. Considering that count data have different distributions, a method assuming RNA sequencing counts distribute Poisson has been proposed recently. Nevertheless, it has been argued that the most likely distribution of RNA sequencing counts is not Poisson due to overdispersion. Therefore, the negative binomial distribution is much more likely. For this distribution, no model-based method for the construction of GRN has been proposed until now. Here, we present a graphical, model-based method for the construction of GRN assuming a negative binomial distribution of the RNA sequencing count data. The R code is available under request. We used the method proposed both on simulated RNA sequencing count data and on real data. The graph is showed, and its descriptive measurements were assessed. They were found some interesting biological conclusions. We confirm that using negative binomial distribution for fitting the model is suitable because RNA sequencing data present overdispersion.

scholarly journals The Negative Binomial – Weighted Garima Distribution: Model, Properties and Applications

2020 ◽  
pp. 1-10
Author(s):  
Winai Bodhisuwan ◽  
Pornpop Saengthong
Keyword(s):  
Parameter Estimation ◽  
Count Data ◽  
Negative Binomial ◽  
Likelihood Estimation ◽  
Real Data ◽  
Distribution Model ◽  
Data Sets ◽  
Factorial Moments ◽  
Special Cases ◽  
First Four Moments

In this paper, a new mixed negative binomial (NB) distribution named as negative binomial-weighted Garima (NB-WG) distribution has been introduced for modeling count data. Two special cases of the formulation distribution including negative binomial- Garima (NB-G) and negative binomial-size biased Garima (NB-SBG) are obtained by setting the specified parameter. Some statistical properties such as the factorial moments, the first four moments, variance and skewness have also been derived. Parameter estimation is implemented using maximum likelihood estimation (MLE) and real data sets are discussed to demonstrate the usefulness and applicability of the proposed distribution.

A Qualified Assertion Database for the History of Places

2019 ◽  
Vol 13 (1-2) ◽  
pp. 95-115
Author(s):  
Brandon Plewe
Keyword(s):  
Data Model ◽  
Temporal Change ◽  
Religious Congregations ◽  
Historical Gis ◽  
The Past ◽  
Model Based ◽  
Complex Information ◽  
Invaluable Tool ◽  
History Of ◽  
The Rich

Historical place databases can be an invaluable tool for capturing the rich meaning of past places. However, this richness presents obstacles to success: the daunting need to simultaneously represent complex information such as temporal change, uncertainty, relationships, and thorough sourcing has been an obstacle to historical GIS in the past. The Qualified Assertion Model developed in this paper can represent a variety of historical complexities using a single, simple, flexible data model based on a) documenting assertions of the past world rather than claiming to know the exact truth, and b) qualifying the scope, provenance, quality, and syntactics of those assertions. This model was successfully implemented in a production-strength historical gazetteer of religious congregations, demonstrating its effectiveness and some challenges.

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