Generalized Maximum Entropy Estimator Research Articles

Tourism Development and Economic Growth in Southeast Asian Countries under the Presence of Structural Break: Panel Kink with GME Estimator

This study examines the nonlinear impact of tourism development on economic growth in Southeast Asian countries using the panel kink regression model. Due to the paucity of Southeast Asian data, we may face the overparameterization problem in our model. To deal with this problem, this study proposes the Generalized Maximum Entropy (GME) estimator to estimate the unknown parameters in this nonlinear model. Several important tourism development indicators consisting of the total international arrivals, international tourism expenditure, and tourism receipts are considered. In addition, we also consider the gross capital formation and real effective exchange rate as a control variable in our nonlinear model. Our findings show that the effect of international tourist arrivals on economic growth should be separated into two regimes, while other factors do not exhibit a nonlinear relationship with Southeast Asian economic growth. Thus, we construct the empirical model with the kink effect in the variable of international tourist arrivals. To confirm the performance of the GME estimator, we compare it to the ordinary least squares and the fixed effect estimators. According to the mean squared and root means squared errors, we find that our GME estimator performs better than the ordinary least squares and the fixed effect estimators. This indicates that GME estimation is an applicable method for estimating the nonlinear effect of tourism growth on economic growth. Our empirical results show that there are positive impacts of tourism growth on economic growth for regime 1 (low tourism demand) and regime 2 (high tourism demand) with the effect of the low tourist arrivals regime being relatively larger. We also find a positive influence of gross capital formation, real effective exchange rate, international tourism expenditure, and tourism receipts on Southeast Asian economic growth.

Generalized maximum entropy in electrical energy price modeling for households and non-households in Portugal

In a high tax burden environment related to electricity prices in Portugal, it is needed to establish fiscal and economic-political measures to lower the electrical price weight imposed on both households and non-households. The most effective way is to first be aware of its determinants. This paper contributes to the existent literature by analyzing how important are fuel costs, emissions, gross domestic product, and renewable energy share in energy consumption in explaining the variations in Portuguese electricity prices for both households and non-households, using generalized maximum entropy estimation. Results suggest that when income increases, electrical energy prices increase 0.0026 Euro/kilowatt-hour for households and 0.0034 Euro/kilowatt-hour for non-households. Fuel costs even lead to higher electricity prices for both, with the highest burden being on the non-household side. Results also point that carbon emissions negatively influence electricity prices and that reductions in electricity bills for non-households may be achieved through the increased share of the industrial sector energy in gross final energy consumption. Thus, increased renewable energy shares do not seem to be the solution to lower electricity prices, despite being necessary to reach sustainability goals.

The nexus between CO2 emissions from electricity generation, GDP and energy intensity using a complete maximum entropy approach: The case of Iran

This study examines the nexus between energy intensity (EI), gross domestic product (GDP) and carbon emissions from electricity generation (CEEG) in Iran, where energy intensity has been increasing during the last decades. Iran holds one of the world’s largest fossil fuel reserves and its electricity sector is highly dependent on natural gas. In recent years, the empirical literature focuses on intensity and efficiency of energy due to global warming and climate change resulting from burning conventional fossil fuels. Therefore, finding the role and impact of energy intensity in an economy is assumed as a very important issue. The results of the current study show a positive impact of EI on CEEG, and no impact of EI on GDP. On the other hand, a nexus between CEEG and GDP is clearly identified in all the scenarios considered in the study. The generalized maximum entropy estimator is used to estimate all the parameters of the replicated models generated by the maximum entropy bootstrap for time series, which represents a novelty and an important improvement towards stability of the entire estimation procedure.

Use of Generalized Maximum Entropy Estimation for Freight Flows Modelling and an Application

In this paper, freight transportation is taken into account. One of the models used for modelling \Origin-Destination freight ows is log- regression model obtained by applying a log-transformation to the tradi- tional gravity model. Freight ows between ten provinces of Turkey is ana- lyzed by using generalized maximum entropy estimator of the log-regression model for freight ow. The data set is gathered together from the axle load survey performed by Turkish Directorate of Highways and other so- cioeconomic and demographic variables related with provinces of interest. Relations between considered socioeconomic and demographic variables and freight ows are gured out and results are discussed.

Entropy inference in smooth transition kink regression

This study proposes a smooth transition kink regression model to capture the nonlinear relationship between dependent and independent variables. Our model generalizes that considered in Hansen to allow the continuous regression to be smoothed at any threshold or kink points. We allow the kink effects to be different for all relationships between each independent variable and the dependent variable. Also, in some cases, the regression typed model may have ill-posed problems (if the number of unknown parameters exceeds the number of observations or the underlying distribution is unknown). Therefore, Generalized Maximum Entropy (GME) estimation is applied for estimating our model. This study conducts experiments based on both simulation and real dataset, with comparison to multiple traditional estimations, including the standard Least Squares, Bayesian, and Maximum Likelihood. Experimental results show that the GME estimation is a useful tool for parameter estimates. Simulations also reveal excellent finite sample properties of the suggested method of estimation where the data is limited, and non-normal distribution is held.

Entropy-Based Solutions for Ecological Inference Problems: A Composite Estimator

Information-based estimation techniques are becoming more popular in the field of Ecological Inference. Within this branch of estimation techniques, two alternative approaches can be pointed out. The first one is the Generalized Maximum Entropy (GME) approach based on a matrix adjustment problem where the only observable information is given by the margins of the target matrix. An alternative approach is based on a distributionally weighted regression (DWR) equation. These two approaches have been studied so far as completely different streams, even when there are clear connections between them. In this paper we present these connections explicitly. More specifically, we show that under certain conditions the generalized cross-entropy (GCE) solution for a matrix adjustment problem and the GME estimator of a DWR equation differ only in terms of the a priori information considered. Then, we move a step forward and propose a composite estimator that combines the two priors considered in both approaches. Finally, we present a numerical experiment and an empirical application based on Spanish data for the 2010 year.

K-means generalized maximum entropy estimation for structural equation modeling

This paper provides an adjusted method of structural equation modeling using the generalized maximum entropy estimation method (GME-SEM). The adjusted method, called K-means-GME-SEM, can reduce the volume of calculation using k-means centroids as a representative for data in consistency constraints. Moreover, the simulation results also show that the new method improves appropriate goodness of fit in the sense of pseudo- $$R^2$$ . From a real-data example, we demonstrate how the proposed method is useful when the number of observation is small.

Modified Entropy Estimator Using a Haar Matrix to Fit the Weibull Growth Model

It is well known that the Generalized Maximum Entropy method can be used to fit linear regression models, especially as they are not restricted by the conditions to be verified as are other classical methods. Therefore, in this paper, a new method for estimating the parameters of the four-parameter Weibull growth model was proposed using the Generalized Maximum Entropy function by fitting data based on the Haar matrix which was used in the wavelet method. The suggested and classical entropy estimators for Weibull growth model parameters were compared using simulation and the preference for the suggested method estimator was shown. The Modified Generalized Maximum Entropy estimator was applied to the real data representing annual Iraqi oil production for the period 2010–2017. Iraqi crude oil production for the year 2022 was predicted and appeared as 4.4 million bb/day.

Expiry Dates, Consumer Behavior, and Food Waste: How Would Italian Consumers React If There Were No Longer “Best Before” Labels?

Much research has been carried out on food losses and waste in the various stages of the food supply chain, consolidating the “fight” against food waste as one of the most important challenges in industrialized countries. Numerous different studies have focused on food waste at the household level, identifying both the multiple causes linked to this behavior and the factors that can drive towards the reduction of food waste. In this paper, a different approach was used, trying to analyze different individuals’ reactions to a concrete action consisting of removing “best before” labels from some food products, following the recent proposal by European Union to simplify date marking. How could any action in this area be implemented in every single country? Bearing in mind the general results of the cross-sectional official survey Flash Eurobarometer n. 425, the purpose of this study was to go deeper into the study of how consumers would behave if the expiry date were no longer available on a pack of spaghetti. The heterogeneity observed in the possible alternative reactions across European Union 28 countries, as well as by considering the importance played by the local context in which individuals reside led us to focus on a single country, and specifically on the Italian context, as an example of country in which citizens have a higher average level of knowledge about expiration dates than the global EU28 citizens, but where, at the same time, there is a more conservative behavior regarding using a product with no expiry date. The multinomial regression model—estimated using the generalized maximum entropy estimator—enabled us to identify different profiles and groups of individuals with which—as a suggestion to policy makers—it would be first necessary to intervene in order to standardize the level of knowledge on this specific topic. In this direction, territorial macro-areas proved to be strongly associated with the various reactions; the probability of consuming or throwing away was found to significantly differ across all the studied regional macro-areas, with a higher likelihood of throwing away the product with no best-before date in southern regions.

Determinants of non-cash payments in Asian countries

Non-cash payments, such as payment cards and digital payment, are more efficient ways to complete transactions when compared to cash payment. This study investigates the factors that influence the use of non-cash payments in 10 Asian countries, namely China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Thailand, and Vietnam. A panel data is used in this study and the period 2016-2017 is considered. To address the ill-posed problem caused by limited observations, generalized maximum entropy (GME) estimator is employed. The purposes of the study are to identify the cross-country determinants of non-cash payments and prove that GME estimator is the most effective estimation method. The results of data show that online banking penetration and debit card penetration are important factors boosting non-cash payments. In addition, the sales of desktop-PCs and the number of individuals who overed 35 years are the determinants of non-cash payments.

Analyzing Regional Agricultural Production Responses using a Positive Mathematical Programming Method

This study constructs regional crop production models of 8 cities in Korea using the PMP Leontief-quadratic and the generalized-CES models suggested by Mérel and Bucaram (2010) and Mérel et al. (2011). A generalized maximum entropy estimation method was used to derive the set of supply elasticity estimates allowing a complete calibration of the models. We demonstrate that those two PMP models fit the Korean regional data quite well. The impacts of two policy changes, raising the rice producer price under the rice direct payment system and raising the minimum wage for hired rural labor were simulated. Our simulation results show that the policy impacts are varying over region and crop. Moreover, it is shown that the choice of the functional specification substantially affects the direction and the magnitudes of the policy impacts. More specifically, production responses are more flexible in the CES specification rather than in the Loentief specification, and the former specification suggests that a substantial increase in rural minimum wage may result in a decline in capital and labor employment in rural areas while the latter specification suggests that the overall resource use in rural areas will not be affected by the policy.

Comparison of entropy measures in generalized maximum entropy estimation

This study aims at comparing the efficiency of different Entropy measures in generalized maximum entropy estimation of the unknown parameters in macroeconomic panel data models by investigating the impacts of foreign direct investment on economic growth of 10 ASEAN countries over the period of 16 years (2001-2016). The three entropy measures (Shannon, Tsallis and Renyi entropy) are used as ordinary least squares estimator and generalized maximum entropy estimator. The results show that Tsallis entropy is the most appropriate to describe the effect of FDI on real GDP in ASEAN countries as it provides the maximum Max Entropy value and the lowest mean square error (MSE). The results of FDI indicates its positive impact on real GDP with the coefficient equal to 0.194 meaning when the FDI increases by 1 percent then the real GDP will increase by 0.194%. FDI is considered as one of the main factors affecting inward real GDP as an increase in production of goods and services leads to a rise in GDP This, in turn, makes the economy more attractive to the foreign investors.

Generalized Maximum Entropy Estimation of Linear Models

In this article, we describe the user-written gmentropylinear command, which implements the generalized maximum entropy estimation method for linear models. This is an information-theoretic procedure preferable to its maximum likelihood counterparts in many applications; it avoids making distributional assumptions, works well when the sample is small or covariates are highly correlated, and is more efficient than its maximum likelihood equivalent. We give a brief introduction to the generalized maximum entropy procedure, present the gmentropylinear command, and give an example using the command.

Estimation for the multi-way error components model with ill-conditioned panel data

Ill-posed problems resulting from limited, partial or incomplete sample information have occurred frequently in econometric practice. The traditional methods of information recovery may cause the estimates highly unstable with low precision, known as ill-conditioned problems. In this paper, we propose a dual generalized maximum entropy estimator for the multi-way error components model with ill-conditioned panel data, based on an unconstrained dual Lagrange multiplier method. The numerical results for the panel data regression model with highly correlated and endogeneous covariates are in favor of our dual generalized maximum entropy estimation method in terms of quality of estimates.

Entropy Type Estimator to Simple Linear Measurement Error Models

The classical maximum likelihood estimation fails to estimate the simple linear measurement error model, with or without equation error, unless additional assumptions are made about the structural parameters. In the literature there are six different assumptions that could be added in order to solve the measurement error models. In this paper, we proposed an entropy-type estimator based on the generalized maximum entropy estimation approach, which allows one to abstract away from the additional assumptions that are made in the classical method. Monte Carlo experiments were carried out in order to investigate the performance of the proposed estimators. The simulation results showed that the entropy-type estimator of unknown parameters has outperformed the classical estimators in terms of mean square error criterion.

A generalized maximum entropy estimator to simple linear measurement error model with a composite indicator

We extend the simple linear measurement error model through the inclusion of a composite indicator by using the generalized maximum entropy estimator. A Monte Carlo simulation study is proposed for comparing the performances of the proposed estimator to his counterpart the ordinary least squares "Adjusted for attenuation". The two estimators are compared in term of correlation with the true latent variable, standard error and root mean of squared error. Two illustrative case studies are reported in order to discuss the results obtained on the real data set, and relate them to the conclusions drawn via simulation study.

Generalized Maximum Entropy Estimation of Discrete Choice Models

In this article, we describe the gmentropylogit command, which implements the generalized maximum entropy estimation methodology for discrete choice models. This information theoretic procedure is preferred over its maximum likelihood counterparts because it is more efficient, avoids strong parametric assumptions, works well when the sample size is small, performs well when the covariates are highly correlated, and functions well when the matrix is ill conditioned. Here we introduce the generalized maximum entropy procedure and provide an example using the gmentropylogit command.

The GME estimator for the regression model with a composite indicator as explanatory variable

We use the generalized maximum entropy (GME) estimator to take into account the measurement error in the regression model with a composite indicator, Likert-type scales based, as explanatory variable. We show that, the reliability measure of the observed composite indicator can be used to define an estimator of the error variance and the supports required by the GME approach. As well as to obtain an estimate of the slope parameter of the model, that has statistical properties similar to the classical ordinary least squares adjusted for attenuation estimator, GME approach allows to estimate the measurement error that can be used to adjust the composite indicator of the latent explanatory variable. An extensive simulation and two case studies show the usefulness of this approach.

Two steps generalized maximum entropy estimation procedure for fitting linear regression when both covariates are subject to error

This paper presents a procedure utilizing the generalized maximum entropy (GME) estimation method in two steps to quantify the uncertainty of the simple linear structural measurement error model parameters exactly. The first step estimates the unknowns from the horizontal line, and then the estimates were used in a second step to estimate the unknowns from the vertical line. The proposed estimation procedure has the ability to minimize the number of unknown parameters in formulating the GME system within each step, and hence reduce variability of the estimates. Analytical and illustrative Monte Carlo simulation comparison experiments with the maximum likelihood estimators and a one-step GME estimation procedure were presented. Simulation experiments demonstrated that the two steps estimation procedure produced parameter estimates that are more accurate and more efficient than the classical estimation methods. An application of the proposed method is illustrated using a data set gathered from the Centre for Integrated Government Services in Delma Island – UAE to predict the association between perceived quality and the customer satisfaction.

Generalized Maximum Entropy Analysis of the Linear Simultaneous Equations Model

A generalized maximum entropy estimator is developed for the linear simultaneous equations model. Monte Carlo sampling experiments are used to evaluate the estimator’s performance in small and medium sized samples, suggesting contexts in which the current generalized maximum entropy estimator is superior in mean square error to two and three stage least squares. Analytical results are provided relating to asymptotic properties of the estimator and associated hypothesis testing statistics. Monte Carlo experiments are also used to provide evidence on the power and size of test statistics. An empirical application is included to demonstrate the practical implementation of the estimator.