Academic literature on the topic 'Pseudo-Poisson Maximum Likelihood'

Abstract. Poisson regression is a statistical method used to analyze the relationship between the response variable and the predictor variable where the data from the response variable is in the form of count data and follows the Poisson distribution. Poisson regression is used to model rare or rare events, so that the response variable is very likely to have a value of zero. Poisson regression is a regression model whose response variable is non-negative. Usually, this model fits the linear regression applied to the log-transformed response variable. However, when the response variable data has a value of zero and is modeled using a log-linear model it will create a biased estimator as well as log-linear regression where heteroscedasticity occurs in the response variable will produce a biased parameter estimator. However, the pseudo poisson maximum likelihood (PPML) provides a natural way to deal with the problem. The purpose of this study is to determine the factors that influence leprosy cases in West Java in 2018 using PPML estimates. The results show that health facilities, healthy homes, and health insurance are factors that influence the number of leprosy cases in West Java in 2018. Using the AIC value, it shows that the use of PPML estimates produces better results than the log-linear model. Abstrak. Regresi poisson merupakan suatu metode statistika yang digunakan untuk menganalisa hubungan antara variabel respon dengan variabel prediktor dimana data dari variabel respon berbentuk data cacahan atau count data dan mengikuti distribusi poisson. Regresi poisson digunakan untuk memodelkan kejadian langka atau jarang terjadi, sehingga variabel respon sangat memungkinkan memiliki nilai nol. Regresi poisson merupakan model regresi yang variabel responnya bernilai non-negatif. Biasanya, model ini cocok dengan regresi linier yang diterapkan pada variabel respon yang ditransformasikan log. Namun, ketika data variabel respon memiliki nilai nol dan dimodelkan menggunakan model log-linear akan menciptakan suatu penaksir yang bias begitu juga regresi log-linear yang terjadi heteroskedastisitas pada variabel responnya akan menghasilkan suatu penaksir parameter yang bias. Namun, pseudo poisson maximum likelihood (PPML) menyediakan cara alami untuk menangani masalah tersebut. Tujuan penelitian ini yaitu untuk mengetahui faktor yang berpengaruh terhadap kasus kusta di Jawa Barat tahun 2018 menggunakan estimasi PPML. Hasil penelitian menunjukkan bahwa fasilitas kesehatan, rumah sehat, dan jaminan kesehatan menjadi faktor yang berpengaruh terhadap jumlah kasus kusta di Jawa Barat tahun 2018. Dengan menggunakan nilai AIC, menunjukkan bahwa penggunaan estimasi PPML menghasilkan hasil yang lebih baik dari pada model log-linear.

Purpose The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of failing to check for the existence of the estimates – convergency issues – as well as being sensitive to numerical problems. Design/methodology/approach The author details the main issues with the log-linear model, drawing from the applied econometric literature in favor of estimating multiplicative models for non-count data. Then, he provides the Stata commands and illustrates the differences in the coefficient and standard errors between both OLS and Poisson models using the health expenditure dataset from the RAND Health Insurance Experiment (RHIE). Findings The results indicate that the use of Poisson pseudo maximum likelihood estimators yield better results that the log-linear model, as well as other alternative models, such as Tobit and two-part models. Originality/value The originality of this study lies in demonstrating an alternative microeconometric technique to deal with positive skewness of dependent variables.

COVID-19 country spikes have been reported at varying temporal scales as a result of differences in the disease-driving factors. Factors affecting case load and mortality rates have varied between countries and regions. We investigated the association between socio-economic, weather, demographic and health variables with the reported cases of COVID-19 in Eswatini using the maximum likelihood estimation method for count data. A generalized Poisson regression (GPR) model was fitted with the data comprising 15 covariates to predict COVID-19 risk in the whole of Eswatini. The results show that the variables that were key determinants in the spread of the disease were those that included the proportion of elderly above 55 years at 98% (95% CI: 97–99%) and the proportion of youth below the age of 35 years at 8% (95% CI: 1.7–38%) with a pseudo R-square of 0.72. However, in the early phase of the virus when cases were fewer, results from the Poisson regression showed that household size, household density and poverty index were associated with reported COVID-19 cases in the country. We then produced a disease-risk map of predicted COVID-19 in Eswatini using variables that were selected by the regression model at a 5% significance level. The map could be used by the country to plan and prioritize health interventions against COVID-19. The identified areas of high risk may be further investigated to find out the risk amplifiers and assess what could be done to prevent them.

In this article, we present ppmlhdfe, a new command for estimation of (pseudo-)Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the iteratively reweighted least-squares algorithm that allows for fast estimation in the presence of HDFE. Because the code is built around the reghdfe package ( Correia, 2014 , Statistical Software Components S457874, Department of Economics, Boston College), it has similar syntax, supports many of the same functionalities, and benefits from reghdfe‘s fast convergence properties for computing high-dimensional leastsquares problems. Performance is further enhanced by some new techniques we introduce for accelerating HDFE iteratively reweighted least-squares estimation specifically. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo)maximum likelihood estimates.

Generally, the movement of cattle within any country including Italy is very essential to the economics of livestock industry. However, this transmission can also carry and spread the risk of contracting the disease (infectious diseases) by other cattle in various geographical areas. For example, this pattern of animal movements brought about an outbreak of foot-and-mouth infectious disease throughout the UK in 2001. Also as Taylor et. al (2001) mentioned that these diseases can give rise to the productivity decline and even can lead to some threats to human health. Therefore, to reduce the risk and economic loss of this kind of contagious diseases, authorities should be able to manage and control them (Andesron, 2002). This control measures can consist of monitoring the animal trades, inspecting entry to and exit from premises, adopting some eradication programs (say, sending sick cattle to slaughterhouses), quarantining cattle, and etc. To properly assess this kind of control measures, we need a comprehensive and detailed information and regulation about the cattle movement pattern. To address these issues, European Economic Community (EEC) devised some regulations and imposed them on its member states to adhere to these measures (EU traceability framework). Actually, these measures originate from the public health and food health concerns, which are related to animal health and the economic impacts of the outbreak of infectious diseases. The EEC issued Council Directive 92/102/EEC in 1992 (with latest modifications in 2013), which obliged member states to record the origin and destination point of each cattle. Also, each cattle should be tagged by an ear tag to be traceable . Specifically, European Parliament and European council in 2000 tried to simplify and implement this process through a digital framework that allows countries to identify and register their bovines . In Italy, this procedure was done by Italian National Animal Identification and Registration Database which proposes us a rich and valuable dataset, including the all required parameters, to perform and analyze some important determinants of Bovines movements and the effects of this kind of disease outbreaks on the pattern of bovines trade among different holdings. In recognition of the importance of national and international bovines’ trade, this thesis attempts to assess and investigate some relevant determinants of bovines movements among Italian holdings and Italian provinces. This study consists of three chapters. In the first chapter, we introduced a structural gravity model of trade and then we linked it to the Italian bovine trade system. Then, we assessed two important determinants of any animal movements, i.e. feed prices and financial literacy rate of farmers. In addition, we tried to analyze the interaction of these determinants on the movements of bovines among Italian provinces. We found that feed (corn, in our case) price shocks and financial literacy rate of farmers could significantly affect on the pattern of bovines movement. Furthermore, our findings suggest that this two factors have a close relationship together and can offset each other's effects, in the sense that the enhancing financial literacy rate of farmers can somehow immune them to the unexpected price shocks and actually undermine the effects of unfavorable price shocks on their business. In the second chapter, we used again the structural gravity model and employed it to investigate the risk of the outbreak of bovine diseases among Italian provinces. We found that the disease incidence rate has a significant positive effect of the movement of bovines from origin nodes to destination points. Also, we tried to merge our findings with the feed (corn) price effects and saw that these two factors act in a different direction. That is the more is the incidence rate, the effect of feed prices become less relevant to the movement of bovines among different provinces. In chapter 3, we tried to have a more detailed view on the effects of disease outbreaks on the movements of bovines among farms and slaughterhouses. In general, we found that the disease status has a negative effect on bovines movement from farms to farms, and a positive influence on the movements of bovines from farms to slaughterhouses. In addition, we found that the ownership has a significant role in determining the pattern of trade among holdings, in the sense that the results are reliable only if two trading partners have different owner/keeper. Although, if the effects were driven by movements between farms with the same owner, it would be a rational decision by the owners in order to separate and protect healthy bovines from sick bovines. However, we found that in the case of positive disease tests, distance has a positive effect on the movement of bovines between various farms. This somehow shows us that some farms may act in such an opportunistic behaviors that can even lead to the spread of diseases among different regions. Finally, we analyzed some network characteristics of the bovines movements to see the effect of network structure and its interaction with disease status on the pattern of movements. More specifically, we used the most commonly used feature of any network i.e. indegree, outdegree, and degree and found that in the case of diseases the farms tend to send more bovines to the slaughterhouses. Actually, the results on the interaction of indegree and disease tests show us that the farms that have received more (probably sick) bovines from more suppliers in the previous period, tend to send more bovines to other farms in the current time period. Also, we should note that the indegree analysis can help us tracing back the diseases to the nodes that are more exposed to receiving bovines and consequently more subject to contracting diseases. On the other hand, we found the outdegree is also important only in case of the trade from farms to other farms. This tells us that in the case of diseases, the farms tend to not change significantly the pattern of their trading partners (other farms), and probably they tend to send their (probably sick) bovines to the other farms and this situation can even decrease the number of heads which transfer from farms to slaughterhouses. This confirms the importance of such nodes in the network in the sense that those with higher outdegree can be considered as most dangerous nodes and can act as a source of spreading diseases among different premises.

Mestrado em Econometria Aplicada e Previsão
The Portuguese National Health System states that access to health care should depend mainly on need. Conditional on need, access to pharmaceuticals should not depend on socio-economic factors such as income, social class, education or geographical factors such as the access to pharmacies. This study uses data from the last two waves of National Health Survey (1995/1996 and 1998/1999) and focuses on equity issues testing for the existence of insurance inequalities, income-related and pharmacies density related inequalities. A two-part model was adopted. To model the probability of occurrence of medicines private expenditure, a modified LOGIT model was specified accounting for the double nature of the zeros of the dependent variable and asymmetry. In the second part a Poisson pseudo maximum likelihood estimator was adopted. No misspecification was detected in the two-part model. The main results showed inequity in Portuguese private medicines expenditures with respect to supplementary health insurance (private and job related), income and pharmacies density.
O Serviço Nacional de Saúde Português estabelece que o acesso a cuidados de saúde deve depender essencialmente das necessidades clínicas. Condicionado nas necessidades individuais, o acesso e utilização de medicamentos não deveria depender de factores económicos como rendimento, classe social, nível de educação ou o acesso a farmácias ou postos de vendas de medicamentos. Utilizando dados das últimas duas realizações do Inquérito Nacional de Saúde (1995/96 e 1998/1999), este estudo testa a existência de inequidades nas despesas com medicamentos, condicionadas na necessidade, relacionadas com o rendimento, com a densidade de farmácias e com possuir seguro de saúde privado ou relacionado com o local de trabalho. Foi aplicado um modelo em duas partes. Para a probabilidade individual de efectuar despesas com medicamentos, foi adoptado um estimador LOGIT modificado para acomodar a dupla natureza dos zeros da variável dependente e que permitisse assimetria. Para modelar as despesas positivas com medicamentos foram utilizadas as propriedades da pseudo verosimilhança através da utilização de um modelo de Poisson. Não se detectou má especificação do modelo em duas partes e concluiu-se que existem inequidades na despesa privada com medicamentos relacionadas com a existência de seguro de saúde privado ou relacionado com o local de trabalho, o rendimento e a densidade de farmácias.

This paper proposes a new panel data structural gravity approach for estimating the trade and welfare effects of Brexit. The suggested Constrained Poisson Pseudo Maximum Likelihood Estimator exhibits some useful properties for trade policy analysis and allows to obtain estimates and confidence intervals which are consistent with structural trade theory. Assuming different counterfactual post-Brexit scenarios, our main findings suggest that UKs (EUs) exports of goods to the EU (UK) are likely to decline within a range between 7.2% and 45.7% (5.9% and 38.2%) six years after the Brexit has taken place. For the UK, the negative trade effects are only partially offset by an increase in domestic goods trade and trade with third countries, inducing a decline in UKs real income between 1.4% and 5.7% under the hard Brexit scenario. The estimated welfare effects for the EU are negligible in magnitude and statistically not different from zero.
Series: Department of Economics Working Paper Series

The transnational traffic of narcotics has had undeniable impacts on international development, for instance, stagnant economic growth in Myanmar (Chin, 2009), unsustainable agricultural practices in Yemen (Robins, 2016), and human security threats in Columbia (Thoumi, 2013). Furthermore, globalization is a catalyst for the transnational narcotics traffic (Robins, 2016; Aas, 2007; Kelly, Maghan & Serio, 2005). Several qualitative studies exist on the transnational narcotics traffic, yet few quantitative studies examine the issue. There is thus an opportunity for novel quantitative studies on the general question: “what are the main economic factors that influence the transnational traffic of narcotics between countries?” This study looked at the specific question: “are distance and economic size correlated with the volume of narcotics traffic between countries?” This study chose the gravity model as it centres on bilateral trade (Tinbergen, 1962), accounts for trade barriers (Kalirajan, 2008) and is empirically robust (Anderson 2011). This study defined a basic functional gravity model relating a proxy of the narcotics traffic to distance and economic size. Four augmented functional gravity models were also advanced to address omitted variable bias. The research was limited conceptually to cross sectional and pooled time series data. In addition, the data was also limited practically to a convenience sample of secondary data drawn from: the United Nations Office on Drugs and Crime’s (UNODC) (2016a) Individual Drug Seizures (IDS); the World Bank’s (2016) World Development Indicators; and the CEPII’s GeoDist (2016) datasets. This study used a novel “dosage” approach to unit standardization to overcome the challenge posed by the many measures and forms of narcotics. The study used the Poisson pseudo maximum likelihood (PPML) estimator as its estimations of the gravity model are consistent (Gourieroux et al., 1984), allow heteroscedasticity (Silva & Tenreyro, 2006) and avoid back transformation bias (Cox et al., 2008). The evidence analyzed in this study seem to indicate that the gravity model may not be applicable in its current form to the transnational narcotics traffic among countries that report drug seizures to the UNODC. However, the sampling method and the choice of proxy are likely to influence these findings. Moreover, the low explanatory power of the gravity model for the narcotics traffic, reflected in the values of the pseudo-R-squared coefficient of determination, indicates that other factors are at play. For instance, authors such as Asad and Harris (2003) and Thoumi (2003) argue that institutions could be a key factor in the narcotics traffic. Future empirical research into this topic could build on the theses findings to introduce new proxies and to explore alternate theoretical frameworks.

We study the main determinants of international trade flows in goods between Ukraine and the EU-28 member states in the period 1995-2017. We estimate the augmented gravity model of international trade with robust Ordinary Least Squares (OLS) and Poisson (PPML), and PPML with fixed effects too estimate trade elasticities for total trade, exports and imports functions. The regression results are used to recommend trade policy makers on the implementation of the EU Association Free-Trade Agreement with Ukraine. The main findings reveal that, the income of Ukraine, the income of the EU-28 trading partners, distance but also income differences between Ukraine and its trading partners (Linder hypothesis), and the real exchange rate are important determinants of international trade. These results are robust to different trade specification functions of the augmented gravity model of international trade.
Estudamos os principais determinantes dos fluxos de comércio internacional de mercadorias entre a Ucrânia e os países membros da UE-28 no período 1995-2017. Estimamos o modelo de gravidade aumentada do comércio internacional com robustos Quadrados Mínimos Ordinários (QMO) e Poisson (PPML), e o PPML com efeitos fixos também estima as elasticidades do comércio para as funções de comércio total, exportações e importações. Os resultados da regressão são utilizados para recomendar os decisores políticos comerciais sobre a implementação do Acordo de Comércio Livre da Associação com a Ucrânia. As principais conclusões revelam que o rendimento da Ucrânia, o rendimento dos parceiros comerciais da UE-28, distância, mas também as diferenças de rendimento entre a Ucrânia e os seus parceiros comerciais (hipótese de Linder) e a taxa de câmbio real são determinantes importantes do comércio internacional. Estes resultados são robustos para diferentes funções de especificação comercial do modelo de gravidade aumentada do comércio internacional.
Mestrado em Economia

Abstract A gravity trade model can be used to determine the effects of policy on bilateral trade flows. The gravity model is initially explained and then used to determine the effect that U.S. tariffs have on softwood lumber (SWL) imports from Canada, using information from the 2006 Softwood Lumber Agreement. Quarterly data for seven Canadian and three U.S. regions for the period 2007-2017 are used to estimate a gravity model of SWL trade. The model is subsequently expanded to include Japan and China as separate regions, and then as a combined China-Japan region. The model is estimated using OLS and a Poisson Pseudo-Maximum-Likelihood method for trade quantity and value. Findings indicate that: (1) the imposition of a countervailing and/or anti-dumping duty usually has a negative effect on Canada's physical exports, but not in all cases; (2) the value of softwood lumber trade decreases by 26% on average under a tax/tariff compared with no duties; (3) the tax/tariff has a smaller but still significant impact on Canadian exports when China and Japan are included, as SWL exports are diverted from the U.S.; and, not surprisingly, (4) duties affect the value of lumber exports to a much greater extent than quantity.

Abstract A gravity trade model can be used to determine the effects of policy on bilateral trade flows. The gravity model is initially explained and then used to determine the effect that U.S. tariffs have on softwood lumber (SWL) imports from Canada, using information from the 2006 Softwood Lumber Agreement. Quarterly data for seven Canadian and three U.S. regions for the period 2007-2017 are used to estimate a gravity model of SWL trade. The model is subsequently expanded to include Japan and China as separate regions, and then as a combined China-Japan region. The model is estimated using OLS and a Poisson Pseudo-Maximum-Likelihood method for trade quantity and value. Findings indicate that: (1) the imposition of a countervailing and/or anti-dumping duty usually has a negative effect on Canada's physical exports, but not in all cases; (2) the value of softwood lumber trade decreases by 26% on average under a tax/tariff compared with no duties; (3) the tax/tariff has a smaller but still significant impact on Canadian exports when China and Japan are included, as SWL exports are diverted from the U.S.; and, not surprisingly, (4) duties affect the value of lumber exports to a much greater extent than quantity.

In this chapter, the features of a continuous time GARCH (COGARCH) process is discussed since the process can be applied as an explicit solution for the stochastic differential equation which is defined for the volatility of unequally spaced time series. COGARCH process driven by a Lévy process is an analogue of discrete time GARCH process and is further generalized to solutions of Lévy driven stochastic differential equations. The Compound Poisson and Variance Gamma processes are defined and used to derive the increments for the COGARCH process. Although there are various parameter estimation methods introduced for COGARCH, this study is focused on two methods which are Pseudo Maximum Likelihood Method and General Methods of Moments. Furthermore, an example is given to illustrate the findings.

Twin deficits in energy and financing are extensively detrimental in Africa which in turn entails foreign direct investment (FDI) to be effectively promoted. This study intends to examine the determinants of FDI in the clean power industry in Africa over the period 2003-2019. By using a robust model of FDI panel gravity fixed effects Poisson pseudo-maximum likelihood, a range of encouraging and reassuring results are found. Importantly, enhancing the awareness of the importance of renewable energy robustly attracts FDI in Africa. Moreover, as anticipated, geographical distance is not the main factor in influencing the decision made by foreign investors. Moving forward, improving renewable energy education with the timely availability of data promotes awareness in society and thus may facilitate the development of the clean power industry in Africa in the near future.

This chapter applies the gravity model approach to examine the trade creation and trade diversion effects in terms of total trade, export, and import for 86 countries over 1994-2018. To evaluate the possible difference between countries, the analyzing sample is divided into three groups: high income, upper middle, and lower middle income countries. Therefore, specific for each income level group, trade creation and trade diversion effects are estimated by Poisson pseudo maximum likelihood estimator. Moreover, along with this, the trade potential for each group and even for each country is calculated based on the estimated results. The results indicate that even in average, the country groups look like they reached their trade potential. The country-based analysis reveals that there is vital potential to expand trade relations. In this context, the findings point out the possibility of increasing income level by developing trade potential, especially in continuously rising globalization.

Economic diplomacy is an unavoidable tool for improving economic standards, and it needs to be an important instrument for policy makers in stimulating international trade and supporting domestic firms. This chapter analyses the impact of economic diplomacy on bilateral trade flows in Croatia in the period from 1992 to 2017. The authors use an applied gravity model of trade by employing fixed effects model (FE), random effects model (RE), and pseudo Poisson maximum likelihood (PPML) estimator. PPML estimator takes into count zero trade flows because estimating zero trade flows with OLS estimator could lead to several biases. The problem of dependence between diplomacy representatives was solved by constructing individual regressions using FE model and PPML estimator. The hypothesis of the chapter, which was tested, states that diplomatic representation has had positive and significant effects on bilateral trade flows (imports and exports) of Croatia. The results of the analysis have shown that the diplomatic representation via embassies and consulates is a relevant trade and trade-enhancing factor.

The financial time series have a high frequency and the difference between their observations is not regular. Therefore, continuous models can be used instead of discrete-time series models. The purpose of this chapter is to define Lévy-driven continuous autoregressive moving average (CARMA) models and their applications. The CARMA model is an explicit solution to stochastic differential equations, and also, it is analogue to the discrete ARMA models. In order to form a basis for CARMA processes, the structures of discrete-time processes models are examined. Then stochastic differential equations, Lévy processes, compound Poisson processes, and variance gamma processes are defined. Finally, the parameter estimation of CARMA(2,1) is discussed as an example. The most common method for the parameter estimation of the CARMA process is the pseudo maximum likelihood estimation (PMLE) method by mapping the ARMA coefficients to the corresponding estimates of the CARMA coefficients. Furthermore, a simulation study and a real data application are given as examples.

Economic diplomacy is an unavoidable tool for improving economic standards, and it needs to be an important instrument for policy makers in stimulating international trade and supporting domestic firms. This chapter analyses the impact of economic diplomacy on bilateral trade flows in Croatia in the period from 1992 to 2017. The authors use an applied gravity model of trade by employing fixed effects model (FE), random effects model (RE), and pseudo Poisson maximum likelihood (PPML) estimator. PPML estimator takes into count zero trade flows because estimating zero trade flows with OLS estimator could lead to several biases. The problem of dependence between diplomacy representatives was solved by constructing individual regressions using FE model and PPML estimator. The hypothesis of the chapter, which was tested, states that diplomatic representation has had positive and significant effects on bilateral trade flows (imports and exports) of Croatia. The results of the analysis have shown that the diplomatic representation via embassies and consulates is a relevant trade and trade-enhancing factor.

On June 5, 2017, an airspace blockade was imposed on the State of Qatar by four of its neighbors: Saudi Arabia, Bahrain, United Arab Emirates, and Egypt. We study the exogenous increase in air transportation costs with non-blockading countries to examine the effect of increased travel distance on bilateral trade. Based on a gravity model estimated with a Poisson pseudo-maximum likelihood, we find a distance elasticity of trade between -0.3 and -0.5. Our findings revise downwards cross-sectional estimates of the distance elasticity of trade and confirm more recent estimates exploiting similar time-varying shocks to distance.