Hasil untuk "Regional economics. Space in economics"

Carlos Eduardo Valencia-Villa

Este artículo ofrece un panorama sobre la historia digital en la historia económica en América Latina durante el siglo xxi. Para ello, se construyó una muestra con 4 160 textos procedentes de congresos, revistas y directorios web. La información fue clasificada según periodos, espacios y temas abordados. Los resultados muestran que el análisis de redes sociales y los sistemas de información geográfica en la historia dominan las prácticas digitales. También se constata que la participación de la historia digital representa apenas el 2 y 6% de la producción en historia económica. Su desarrollo es, por tanto, limitado. Más aún, investigadores en solitario o en binomio la aplican con mínimas interacciones. La hipótesis que explica la situación se sustenta en tres factores: la falta de entrenamiento tecnológico, el desinterés metodológico y la estructura institucional de financiamiento. Cada uno de ellos es verificado empíricamente. Finalmente, se concluye que la inercia de estos factores mantendrá las mismas condiciones en los próximos años.

Jiahong Gao, Robert R. Reed

Cesar A. Hidalgo, Viktor Stojkoski

We develop a dynamic model of economic complexity that endogenously generates a transition between unconditional and conditional convergence. In this model, convergence turns conditional as the capability intensity of activities rises. We solve the model analytically, deriving closed-form solutions for the boundary separating unconditional from conditional convergence and show that this model also explains the path-dependent diversification process known as the principle of relatedness. This model provides an explanation for transitions between conditional and unconditional convergence and path-dependent diversification.

Craig S Wright

The CAP theorem asserts a trilemma between consistency, availability, and partition tolerance. This paper introduces a rigorous automata-theoretic and economically grounded framework that reframes the CAP trade-off as a constraint optimization problem. We model distributed systems as partition-aware state machines and embed economic incentive layers to stabilize consensus behavior across adversarially partitioned networks. By incorporating game-theoretic mechanisms into the global transition semantics, we define provable bounds on convergence, liveness, and correctness. Our results demonstrate that availability and consistency can be simultaneously preserved within bounded epsilon margins, effectively extending the classical CAP limits through formal economic control.

Guhan Sivakumar

This paper explores the application of Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) neural networks for economic forecasting, focusing on predicting CPI inflation rates. The study explores a new approach that integrates HMM-derived hidden states and means as additional features for LSTM modeling, aiming to enhance the interpretability and predictive performance of the models. The research begins with data collection and preprocessing, followed by the implementation of the HMM to identify hidden states representing distinct economic conditions. Subsequently, LSTM models are trained using the original and augmented data sets, allowing for comparative analysis and evaluation. The results demonstrate that incorporating HMM-derived data improves the predictive accuracy of LSTM models, particularly in capturing complex temporal patterns and mitigating the impact of volatile economic conditions. Additionally, the paper discusses the implementation of Integrated Gradients for model interpretability and provides insights into the economic dynamics reflected in the forecasting outcomes.

Mahdi Goldani

This study explores various feature selection techniques applied to macro-economic forecasting, using Iran's World Bank Development Indicators. Employing a comprehensive evaluation framework that includes Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) within a 10-fold cross-validation setup, this research systematically analyzes and ranks different feature selection methodologies. The study distinctly highlights the efficiency of Stepwise Selection, Tree-based methods, Hausdorff distance, Euclidean distance, and Mutual Information (MI) Score, noting their superior performance in reducing predictive errors. In contrast, methods like Recursive Feature Elimination with Cross-Validation (RFECV) and Variance Thresholding showed relatively lower effectiveness. The results underline the robustness of similarity-based approaches, particularly Hausdorff and Euclidean distances, which consistently performed well across various datasets, achieving an average rank of 9.125 out of a range of tested methods. This paper provides crucial insights into the effectiveness of different feature selection methods, offering significant implications for enhancing the predictive accuracy of models used in economic analysis and planning. The findings advocate for the prioritization of stepwise and tree-based methods alongside similarity-based techniques for researchers and practitioners working with complex economic datasets.

Jacob Smith

This paper presents a method for incorporating risk aversion into existing decision tree models used in economic evaluations. The method involves applying a probability weighting function based on rank dependent utility theory to reduced lotteries in the decision tree model. This adaptation embodies the fact that different decision makers can observe the same decision tree model structure but come to different conclusions about the optimal treatment. The proposed solution to this problem is to compensate risk-averse decision makers to use the efficient technology that they are reluctant to adopt.

Francisco Estrada, Richard S. J. Tol, Wouter Botzen

Damage functions in integrated assessment models (IAMs) map changes in climate to economic impacts and form the basis for most of estimates of the social cost of carbon. Implicit in these functions lies an unwarranted assumption that restricts the spatial variation (Svar) and temporal variability (Tvar) of changes in climate to be null. This could bias damage estimates and the climate policy advice from IAMs. While the effects of Tvar have been studied in the literature, those of Svar and their interactions with Tvar have not. Here we present estimates of the economic costs of climate change that account for both Tvar and Svar, as well as for the seasonality of damages across sectors. Contrary to the results of recent studies which show little effect that of Tvar on expected losses, we reveal that ignoring Svar produces large downward biases, as warming is highly heterogeneous over space. Using a conservative calibration for the damage function, we show that previous estimates are biased downwards by about 23-36%, which represents additional losses of about US$1,400-US$2,300 billion by 2050 and US$17-US$28 trillion by the end of the century, under a high emissions scenario. The present value of losses during the period 2020-2100 would be larger than reported in previous studies by $47-$66 trillion or about 1/2 to 3/4 of annual global GDP in 2020. Our results imply that using global mean temperature change in IAMs as a summary measure of warming is not adequate for estimating the costs of climate change. Instead, IAMs should include a more complete description of climate conditions.

Louis H. Amato, Richard J. Cebula, John E. Connaughton

This study uses Bureau of Economic Analysis data on state-level productivity levels and growth rates over the period 1977–2019. We find that states with relatively high productivity tend to experience somewhat lower productivity growth over time, whereas states with relatively lower productivity experience somewhat higher productivity growth over time. We find compelling evidence for significant contributions from education (in the form of a college degree) as well as the role played by higher growth rates in the state-level Hispanic population as factors contributing to increased productivity. Worker/labour productivity constitutes a good indicator of changes to wages and living standards. Empirically examining interstate differences in state-level worker productivity growth across different time intervals helps to identify factors that influence geographical differentials in productivity as well as aids in the identification of the specific factors that determine rates of productivity growth and decline.

Gnanothayan J. Gaberial, Kauškale Linda

The construction industry forms an important part of any economy, and development of the construction industry could be of high significance for economic development. The construction industry includes the production of construction objects, consumption of resources, investment that contribute to the creation of employment places and also to the economic development of Sri Lanka. The aim of the research is to analyse the development trends of the construction industry and its interconnection with economic development in Sri Lanka. Particular focus of the research is placed on interconnection between the construction industry and economic development. Historical analysis, logical access and other methods have been used in the research. The analysis includes theoretical and practical aspects: actual tendencies and historical overview.

Leonid N. Yasnitsky , Vitaliy L. Yasnitsky , Aleksandr O. Alekseev

Существующие математические модели массовой оценки и прогнозирования рыночной стоимости жилых объектов обладают рядом недостатков: разработанные для какого-либо одного региона модели не годятся для других регионов. Все они быстро устаревают и требуют постоянной актуализации, поскольку не учитывают постоянно меняющуюся экономическую обстановку. Они не пригодны для оптимизации строительного бизнеса. Целью исследования является создание системы оценки недвижимости городов России, применимой к любым ее регионам, причем независимо от постоянно меняющейся экономической ситуации. Эта цель была достигнута благодаря тому, что в качестве входных параметров нейронной сети помимо строительно-эксплуатационных факторов были учтены географические параметры, фактор времени, а также ряд параметров, характеризующих экономическую ситуацию в конкретных регионах, в России и в мире. Статистические данные о рынках недвижимости РФ, необходимые для обучения нейронной сети, были собраны за длительный период с 2006 г. по 2020 г., что обусловило ее динамические свойства. В качестве примера применения системы были проведены виртуальные компьютерные эксперименты, которые, например, показали, что в Москве самую высокую удельную стоимость квадратного метра имеют однокомнатные квартиры минимальных размеров — 16 м2. Максимальная стоимость двухкомнатных квартир достигается при их площади 90 м2, трехкомнатных — 100 м2, четырехкомнатных — 110 м2, пятикомнатных — 120 м2. Для условий Екатеринбурга среди двухкомнатных квартир наибольшую стоимость квадратного метра имеют квартиры общей площадью 30 м2, трехкомнатных — 110 м2, четырехкомнатных — 130 м2, пятикомнатных — 150 м2. Таким образом, система может быть использована для оптимизации строительного бизнеса. Она может быть полезна государственным структурам, занимающимся вопросами управления рынком городской недвижимости, вопросами имущественного налогообложения, вопросами повышения эффективности жилищного рынка.

mehrdad sadrara, Yasser Rezaei pitenoei (Ph.D), Gholamreza Mahfoozi

The main purpose of this research is to investigate the effectiveness of Surveillance measures and provide a model for Updating of appropriate supervisory systems with market anomalies in Tehran Stock Exchange and Overseas Iran. This research is a descriptive survey that consists of two parts. The first part is the prioritization of market abnormalities, which the method of theoretical discussions collecting has been library, as well as the information necessary for the realization of the research objectives using distribution and collection a questionnaire was conducted among 22 supervisors of Tehran Stock Exchange. In the second part of the research, the effects of identified abnormalities in securities transactions on the efficiency of Tehran Stock Exchange at two levels of large and small companies been investigated. The results of the survey briefly state that in large companies the most violations are affected by price and transactions manipulation, while in small companies, often violations will be due to the inside trading-based transactions. It can also be concluded that the anomalies of trades on the stock exchange reduce market efficiency.

Patrizio Bifulco, Jochen Glück, Oliver Krebs et al.

This paper characterizes equilibrium properties of a broad class of economic models that allow multiple heterogeneous agents to interact in heterogeneous manners across several markets. Our key contribution is a new theorem providing sufficient conditions for uniqueness and stability of equilibria in this class of models. To illustrate the applicability of our theorem, we characterize the general equilibrium properties of two commonly used quantitative trade models. Specifically, our analysis provides a first proof of uniqueness and stability of the equilibrium in multi-country trade models featuring (i) multiple sectors, or (ii) heterogeneity across countries in terms of their labor cost shares. These examples also provide a practical toolkit for future research on how our theorem can be applied to establish uniqueness and stability of equilibria in a broad set of economic models.

Nikolay Khabarov, Alexey Smirnov, Michael Obersteiner

Shadow prices are well understood and are widely used in economic applications. However, there are limits to where shadow prices can be applied assuming their natural interpretation and the fact that they reflect the first order optimality conditions (FOC). In this paper, we present a simple ad-hoc example demonstrating that marginal cost associated with exercising an optimal control may exceed the respective cost estimated from a ratio of shadow prices. Moreover, such cost estimation through shadow prices is arbitrary and depends on a particular (mathematically equivalent) formulation of the optimization problem. These facts render a ratio of shadow prices irrelevant to estimation of optimal marginal cost. The provided illustrative optimization problem links to a similar approach of calculating social cost of carbon (SCC) in the widely used dynamic integrated model of climate and the economy (DICE).

Joel Dyer, Patrick Cannon, J. Doyne Farmer et al.

Simulation models, in particular agent-based models, are gaining popularity in economics. The considerable flexibility they offer, as well as their capacity to reproduce a variety of empirically observed behaviours of complex systems, give them broad appeal, and the increasing availability of cheap computing power has made their use feasible. Yet a widespread adoption in real-world modelling and decision-making scenarios has been hindered by the difficulty of performing parameter estimation for such models. In general, simulation models lack a tractable likelihood function, which precludes a straightforward application of standard statistical inference techniques. Several recent works have sought to address this problem through the application of likelihood-free inference techniques, in which parameter estimates are determined by performing some form of comparison between the observed data and simulation output. However, these approaches are (a) founded on restrictive assumptions, and/or (b) typically require many hundreds of thousands of simulations. These qualities make them unsuitable for large-scale simulations in economics and can cast doubt on the validity of these inference methods in such scenarios. In this paper, we investigate the efficacy of two classes of black-box approximate Bayesian inference methods that have recently drawn significant attention within the probabilistic machine learning community: neural posterior estimation and neural density ratio estimation. We present benchmarking experiments in which we demonstrate that neural network based black-box methods provide state of the art parameter inference for economic simulation models, and crucially are compatible with generic multivariate time-series data. In addition, we suggest appropriate assessment criteria for future benchmarking of approximate Bayesian inference procedures for economic simulation models.

Nooshin hemmasian Etefagh, Saeid Aliahmadi

Disclosure of information in the capital market affects stock prices. The reaction of investors to the gradual and continuous disclosure of information or to the immediate and discrete disclosure of information can have a different effect on the stock price. In addition, the existence of a momentum strategy in the capital market and its combination with discrete disclosure of information can increase these effects. Therefore, the purpose of this study is to investigate the effect of momentum and information discreteness on the excess stock return. The sampling method in this research is the systematic method of elimination and research period from 2010 to 2018. We select of 104 the companies and the method used to estimate the model of multivariate regression method. The results of the research showed that the momentum is effect the on the excess stock return. Therefore, investors can gain returns in the Iranian capital market by implementing a momentum strategy. Also, the results showed that the information discreteness is effect on the excess stock return. Therefore, investors can make higher returns by choosing companies whose information is disclosed immediately and discretely. In addition, the results showed that the combine of momentum and information discreteness have effect on the excess stock return. investors can achieve higher returns by choosing companies based on a combination of momentum strategy and information discreteness. Overall, the results showed that the information discreteness was added to the asset pricing models in addition to the variables of the Fama and French three-factor model.

Kensuke Ohtake

We consider the extension of a tractable NEG model with a quasi-linear log utility to continuous space, and investigate the behavior of its solution mathematically. The model is a system of nonlinear integral and differential equations describing the market equilibrium and the time evolution of the spatial distribution of population density. A unique global solution is constructed and a homogeneous stationary solution with evenly distributed population is shown to be unstable. Furthermore, it is shown numerically that the destabilized homogeneous stationary solution eventually forms spiky spatial distributions. The number of the spikes decreases as the preference for variety increases or the transport cost decreases.

Makoto Niwa, Yasushi Hara, Yusuke Matsuo et al.

During the global spread of COVID-19, Japan has been among the top countries to maintain a relatively low number of infections, despite implementing limited institutional interventions. Using a Tokyo Metropolitan dataset, this study investigated how these limited intervention policies have affected public health and economic conditions in the COVID-19 context. A causal loop analysis suggested that there were risks to prematurely terminating such interventions. On the basis of this result and subsequent quantitative modelling, we found that the short-term effectiveness of a short-term pre-emptive stay-at-home request caused a resurgence in the number of positive cases, whereas an additional request provided a limited negative add-on effect for economic measures (e.g. the number of electronic word-of-mouth (eWOM) communications and restaurant visits). These findings suggest the superiority of a mild and continuous intervention as a long-term countermeasure under epidemic pressures when compared to strong intermittent interventions.

Nitish Gupta, Ruchir Kaul, Satwik Gupta et al.

The results based on the nonparametric nearest neighbor matching suggest a statistically significant positive effect of the EU ETS on the economic performance of the regulated firms during Phase I of the EU ETS. A year-by-year analysis shows that the effect was only significant during the first year of Phase I. The EU ETS, therefore, had a particularly strong effect when it was introduced. It is important to note that the EU ETS does not homogeneously affect firms in the manufacturing sector. We found a significant positive impact of EU ETS on the economic performance of regulated firms in the paper industry.

Jacob Turton, Ali Kabiri, David Tuckett et al.

There is growing interest in the role of sentiment in economic decision-making. However, most research on the subject has focused on positive and negative valence. Conviction Narrative Theory (CNT) places Approach and Avoidance sentiment (that which drives action) at the heart of real-world decision-making, and argues that it better captures emotion in financial markets. This research, bringing together psychology and machine learning, introduces new techniques to differentiate Approach and Avoidance from positive and negative sentiment on a fundamental level of meaning. It does this by comparing word-lists, previously constructed to capture these concepts in text data, across a large range of semantic features. The results demonstrate that Avoidance in particular is well defined as a separate type of emotion, which is evaluative/cognitive and action-orientated in nature. Refining the Avoidance word-list according to these features improves macroeconomic models, suggesting that they capture the essence of Avoidance and that it plays a crucial role in driving real-world economic decision-making.