Relevant bibliographies by topics / Learning – Econometric models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Learning – Econometric models'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Learning – Econometric models"

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Kim, Dong-sup, and Seungwoo Shin. "THE ECONOMIC EXPLAINABILITY OF MACHINE LEARNING AND STANDARD ECONOMETRIC MODELS-AN APPLICATION TO THE U.S. MORTGAGE DEFAULT RISK." International Journal of Strategic Property Management 25, no. 5 (2021): 396–412. http://dx.doi.org/10.3846/ijspm.2021.15129.

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This study aims to bridge the gap between two perspectives of explainability−machine learning and engineering, and economics and standard econometrics−by applying three marginal measurements. The existing real estate literature has primarily used econometric models to analyze the factors that affect the default risk of mortgage loans. However, in this study, we estimate a default risk model using a machine learning-based approach with the help of a U.S. securitized mortgage loan database. Moreover, we compare the economic explainability of the models by calculating the marginal effect and marginal importance of individual risk factors using both econometric and machine learning approaches. Machine learning-based models are quite effective in terms of predictive power; however, the general perception is that they do not efficiently explain the causal relationships within them. This study utilizes the concepts of marginal effects and marginal importance to compare the explanatory power of individual input variables in various models. This can simultaneously help improve the explainability of machine learning techniques and enhance the performance of standard econometric methods.
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Liao, Ruofan, Paravee Maneejuk, and Songsak Sriboonchitta. "Beyond Deep Learning: An Econometric Example." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, Supp01 (2020): 31–38. http://dx.doi.org/10.1142/s0218488520400036.

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In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.
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Salmon, Timothy C. "An Evaluation of Econometric Models of Adaptive Learning." Econometrica 69, no. 6 (2001): 1597–628. http://dx.doi.org/10.1111/1468-0262.00258.

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Pérez-Pons, María E., Javier Parra-Dominguez, Sigeru Omatu, Enrique Herrera-Viedma, and Juan Manuel Corchado. "Machine Learning and Traditional Econometric Models: A Systematic Mapping Study." Journal of Artificial Intelligence and Soft Computing Research 12, no. 2 (2021): 79–100. http://dx.doi.org/10.2478/jaiscr-2022-0006.

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Abstract Context: Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods. Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of econometric and ML models. The study aimed to find empirical evidence for the performance of ML algorithms being superior to traditional econometric models. The Methodology of systematic mapping of literature has been followed to carry out this research, according to the guidelines established by [39], and [58] that facilitate the identification of studies published about this subject. Results: The results show, that in most cases ML outperforms econometric models, while in other cases the best performance has been achieved by combining traditional methods and ML applications. Conclusion: inclusion and exclusions criteria have been applied and 52 articles closely related articles have been reviewed. The conclusion drawn from this research is that it is a field that is growing, which is something that is well known nowadays and that there is no certainty as to the performance of ML being always superior to that of econometric models.
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Zapata, Hector O., and Supratik Mukhopadhyay. "A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing." Journal of Risk and Financial Management 15, no. 11 (2022): 535. http://dx.doi.org/10.3390/jrfm15110535.

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Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better suited for, problems of prediction. While some ML methods have been applied in econometrics for decades, their success in prediction has been limited, and examples of this abound in the asset pricing literature. In recent years, the ML literature has advanced new, more efficient, computation methods for regularization, modeling nonlinearity, and improved out-of-sample prediction. This article conducted a comprehensive, objective, and quantitative bibliometric analysis of this growing literature using Web of Science (WoS) data. We identified trends in the literature over the past decade, the geographical distribution of articles, authorship, and institutional contributions worldwide. The paper also identifies the dominant literature using citations in WoS and discusses computational algorithms that are expanding the econometric frontiers in asset pricing. The top cited papers were reviewed, highlighting their contribution. The limitations of ML learning methods and recent advances in ML were used to provide a conic view to future ML econometric practice.
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Athey, Susan, and Guido W. Imbens. "Machine Learning Methods That Economists Should Know About." Annual Review of Economics 11, no. 1 (2019): 685–725. http://dx.doi.org/10.1146/annurev-economics-080217-053433.

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We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and econometrics that typically perform better than either off-the-shelf ML or more traditional econometric methods when applied to particular classes of problems, including causal inference for average treatment effects, optimal policy estimation, and estimation of the counterfactual effect of price changes in consumer choice models.
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Fan, Jianqing, Kunpeng Li, and Yuan Liao. "Recent Developments in Factor Models and Applications in Econometric Learning." Annual Review of Financial Economics 13, no. 1 (2021): 401–30. http://dx.doi.org/10.1146/annurev-financial-091420-011735.

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This article provides a selective overview of the recent developments in factor models and their applications in econometric learning. We focus on the perspective of the low-rank structure of factor models and particularly draw attention to estimating the model from the low-rank recovery point of view. Our survey mainly consists of three parts. The first part is a review of new factor estimations based on modern techniques for recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and their applications in statistical learning models. The final part summarizes new developments dealing with unbalanced panels from the matrix completion perspective.
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Shen, Ze, Qing Wan, and David J. Leatham. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN." Journal of Risk and Financial Management 14, no. 7 (2021): 337. http://dx.doi.org/10.3390/jrfm14070337.

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One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in forecasting accuracy and risk management efficiency. The results demonstrate that the RNN outperforms GARCH and EWMA in average forecasting performance. However, it is less efficient in capturing the bitcoin market’s extreme events. Moreover, the RNN shows poor performance in Value at Risk forecasting, indicating that it could not work well as the econometric models in explaining extreme volatility. This study proposes an alternative method of bitcoin volatility analysis and provides more motivation for economic researchers to apply machine learning methods to the less volatile financial market conditions. Meanwhile, it also shows that the machine learning approaches are not always more advanced than econometric models, contrary to common belief.
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Ifft, Jennifer, Ryan Kuhns, and Kevin Patrick. "Can machine learning improve prediction – an application with farm survey data." International Food and Agribusiness Management Review 21, no. 8 (2018): 1083–98. http://dx.doi.org/10.22434/ifamr2017.0098.

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Businesses, researchers, and policymakers in the agricultural and food sector regularly make use of large public, private, and administrative datasets for prediction, including forecasting, public policy targeting, and management research. Machine learning has the potential to substantially improve prediction with these datasets. In this study we demonstrate and evaluate several machine learning models for predicting demand for new credit with the 2014 Agricultural Resource Management Survey. Many, but not all, of the machine learning models used are shown to have stronger predictive power than standard econometric approaches. We provide a cost based model evaluation approach for managers to analyze returns to machine learning methods relative to standard econometric approaches. While there are potentially significant returns to machine learning methods, research objectives and firm-level costs are important considerations that in some cases may favor standard econometric approaches.
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Rondina, Francesca. "An Econometric Learning Approach to Approximate Expectations in Empirical Macro Models." International Advances in Economic Research 23, no. 4 (2017): 437–38. http://dx.doi.org/10.1007/s11294-017-9662-8.

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Dissertations / Theses on the topic "Learning – Econometric models"

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Boumediene, Farid Jimmy. "Determinacy and learning stability of economic policy in asymmetric monetary union models." Thesis, University of St Andrews, 2010. http://hdl.handle.net/10023/972.

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This thesis examines determinacy and E-stability of economic policy in monetary union models. Monetary policy takes the form of either a contemporaneous or a forecast based interest rate rule, while fiscal policy follows a contemporaneous government spending rule. In the absence of asymmetries, the results from the closed economy literature on learning are retained. However, when introducing asymmetries into monetary union frameworks, the determinacy and E-stability conditions for economic policy differ from both the closed and open economy cases. We find that a monetary union with heterogeneous price rigidities is more likely to be determinate and E-stable. Specifically, the Taylor principle, a key stability condition for the closed economy, is now relaxed. Furthermore, an interest rate rule that stabilizes the terms of trade in addition to output and inflation, is more likely to induce determinacy and local stability under RLS learning. If monetary policy is sufficiently aggressive in stabilizing the terms of trade, then determinacy and E-stability of the union economy can be achieved without direct stabilization of output and inflation. A fiscal policy rule that supports demand for domestic goods following a shock to competitiveness, can destabilize the union economy regardless of the interest rate rule employed by the union central bank. In this case, determinacy and E-stability conditions have to be simultaneously and independently met by both fiscal and monetary policy for the union economy to be stable. When fiscal policy instead stabilizes domestic output gaps while monetary policy stabilizes union output and inflation, fiscal policy directly affects the stability of monetary policy. A contemporaneous monetary policy rule has to be more aggressive to satisfy the Taylor principle, the more aggressive fiscal policy is. On the other hand, when monetary policy is forward looking, an aggressive fiscal policy rule can help induce determinacy.
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Pesantez, Narvaez Jessica Estefania. "Risk Analytics in Econometrics." Doctoral thesis, Universitat de Barcelona, 2021. http://hdl.handle.net/10803/671864.

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This thesis addresses the framework of risk analytics as a compendium of four main pillars: (i) big data, (ii) intensive programming, (iii) advanced analytics and machine learning, and (iv) risk analysis. Under the latter mainstay, this PhD dissertation reviews potential hazards known as “extreme events” that could negatively impact the wellbeing of people, profitability of firms, or the economic stability of a country, but which also have been underestimated or incorrectly treated by traditional modelling techniques. The objective of this thesis is to develop econometric and machine learning algorithms that can improve the predictive capacity of those extreme events and improve the comprehension of the phenomena contrary to some modern advanced methods which are black boxes in terms of interpretation. This thesis presents seven chapters that provide a methodological contribution to the existing literature by building techniques that transform the new valuable insights of big data into more accurate predictions that support decisions under risk, and increase robustness for more reliable and real results. This PhD thesis focuses uniquely on extremal events which are trigged into a binary variable, mostly known as class-imbalanced data and rare events in binary response, in other words, whose classes that are not equally distributed. The scope of research tackle real cases studies in the field of risk and insurance, where it is highly important to specify a level of claims of an event in order to foresee its impact and to provide a personalized treatment. After Chapter 1 corresponding to the introduction, Chapter 2 proposes a weighting mechanism to incorporated in the weighted likelihood estimation of a generalized linear model to improve the predictive performance of the highest and lowest deciles of prediction. Chapter 3 proposes two different weighting procedures for a logistic regression model with complex survey data or specific sampling designed data. Its objective is to control the randomness of data and provide more sensitivity to the estimated model. Chapter 4 proposes a rigorous review of trials with modern and classical predictive methods to uncover and discuss the efficiency of certain methods over others, and which and how gaps in machine learning literature can be addressed efficiently. Chapter 5 proposes a novel boosting-based method that overcomes certain existing methods in terms of predictive accuracy and also, recovers some interpretation of the model with imbalanced data. Chapter 6 develops another boosting-based algorithm which is able to improve the predictive capacity of rare events and get approximated as a generalized linear model in terms of interpretation. And finally, Chapter 7 includes the conclusions and final remarks. The present thesis highlights the importance of developing alternative modelling algorithms that reduces uncertainty, especially when there are potential limitations that impede to know all the previous factors that influence on the presence of a rare event or imbalanced-data phenomenon. This thesis merges two important approaches in modelling predictive literature as they are: “econometrics” and “machine learning”. All in all, this thesis contributes to enhance the methodology of how empirical analysis in many experimental and non-experimental sciences have being doing so far.
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Ropele, Andrea <1994&gt. "The Blockchain technology and a comparison between classical statistical models and machine learning methods for time series analysis." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/13238.

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This thesis wants to put together the area of computer science and statistics. For the IT side, the mechanisms of the blockchain technology and classical concept of computer science necessary for understanding it will be outlined. On the other hand, the quantitative part will present the state of the art of machine learning algorithms. The work will end with an empirical chapter where machine learning methods will be compared to classical statistical models. The comparison metric will be the forecasting error of the conditional mean and the conditional variance of timeseries belonging to the cryptocurrency world.
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Nguyen, Trong Nghia. "Deep Learning Based Statistical Models for Business and Financial Data." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26944.

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We investigate a wide range of statistical models commonly used in many business and financial econometrics applications and propose flexible ways to combine these highly interpretable models with powerful predictive models in the deep learning literature to leverage the advantages and compensate the disadvantages of each of the modelling approaches. Our approaches of utilizing deep learning techniques for financial data are different from the recently proposed deep learning-based models in the financial econometrics literature in several perspectives. First, we do not overlook well-established structures that have been successfully used in statistical modelling. We flexibly incorporate deep learning techniques to the statistical models to capture the data effects that cannot be explained by the simple linear components of those models. Our proposed modelling frameworks therefore normally include two components: a linear part to explain linear dependencies and a deep learning-based part to capture data effects rather than linearity possibly exhibited in the underlying process. Second, we do not use the neural network structures in the same fashion as they are implemented in the deep learning literature but modify those black-box methods to make them more explainable and hence improve the interpretability of the proposed models. As the results, our hybrid models not only perform better than the pure deep learning techniques in term of interpretation but also often produce more accurate out-of-sample forecasts than the counterpart statistical frameworks. Third, we propose advanced Bayesian inference methodologies to efficiently quantify the uncertainty about the model estimation and prediction. For the proposed high dimensional deep learning-based models, performing efficient Bayesian inference is extremely challenging and is often ignored in the engineer-oriented papers, which generally prefer the frequentist estimation approaches mainly due to the simplicity.
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Azari, Soufiani Hossein. "Revisiting Random Utility Models." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11605.

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This thesis explores extensions of Random Utility Models (RUMs), providing more flexible models and adopting a computational perspective. This includes building new models and understanding their properties such as identifiability and the log concavity of their likelihood functions as well as the development of estimation algorithms.<br>Engineering and Applied Sciences
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Zhao, Zilong. "Extracting knowledge from macroeconomic data, images and unreliable data." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALT074.

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L'identification de système et l'apprentissage automatique sont deux concepts similaires utilisés indépendamment dans la communauté automatique et informatique. L'identification des systèmes construit des modèles à partir de données mesurées. Les algorithmes d'apprentissage automatique construisent des modèles basés sur des données d'entraînement (propre ou non), afin de faire des prédictions sans être explicitement programmé pour le faire. Sauf la précision de prédiction, la vitesse de convergence et la stabilité sont deux autres facteurs clés pour évaluer le processus de l'apprentissage, en particulier dans le cas d'apprentissage en ligne, et ces propriétés ont déjà été bien étudiées en théorie du contrôle. Donc, cette thèse implémente des recherches suivantes : 1) Identification du système et contrôle optimal des données macroéconomiques : Nous modélisons d'abord les données macroéconomiques chinoises sur le modèle VAR (Vector Auto-Regression), puis identifions la relation de cointégration entre les variables et utilisons le Vector Error Correction Model (VECM) pour étudier le court terme fluctuations autour de l'équilibre à long terme, la causalité de Granger est également étudiée avec VECM. Ce travail révèle la tendance de la transition de la croissance économique de la Chine : de l'exportation vers la consommation ; La deuxième étude est avec des données de la France. On représente le modèle dans l'espace d'états, mettons le modèle dans un cadre de feedback-control, le contrôleur est conçu par un régulateur linéaire-quadratique (LQR). On peut également imposer des perturbations sur les sorties et des contraintes sur les entrées, ce qui simule la situation réelle de crise économique. 2) Utilisation de la théorie du contrôle pour améliorer l'apprentissage en ligne du réseau neuronal profond : Nous proposons un algorithme de taux d'apprentissage basé sur les performances : E (Exponential)/PD (Proportional Derivative) contrôle, qui considère le Convolutional Neural Network (CNN) comme une plante, taux d'apprentissage comme signal de commande et valeur de loss comme signal d'erreur. Le résultat montre que E/PD surpasse l'état de l'art en termes de précision finale, de loss finale et de vitesse de convergence, et le résultat est également plus stable. Cependant, une observation des expériences E/PD est que le taux d'apprentissage diminue tandis que la loss diminue continuellement. Mais la loss diminue, le modèle s’approche d’optimum, on ne devait pas diminuer le taux d'apprentissage. Pour éviter cela, nous proposons un event-based E/PD. Le résultat montre qu'il améliore E/PD en précision finale, loss finale et vitesse de convergence ; Une autre observation de l'expérience E/PD est que l'apprentissage en ligne fixe des époques constantes pour chaque batch. Puisque E/PD converge rapidement, l'amélioration significative ne vient que des époques initiales. Alors, nous proposons un autre event-based E/PD, qui inspecte la loss historique. Le résultat montre qu'il peut épargner jusqu'à 67% d'époques sur la donnée CIFAR-10 sans dégrader beaucoup les performances.3) Apprentissage automatique à partir de données non fiables : Nous proposons un cadre générique : Robust Anomaly Detector (RAD), la partie de sélection des données de RAD est un cadre à deux couches, où la première couche est utilisée pour filtrer les données suspectes, et la deuxième couche détecte les modèles d'anomalie à partir des données restantes. On dérive également trois variantes de RAD : voting, active learning et slim, qui utilisent des informations supplémentaires, par exempe, les opinions des classificateurs conflictuels et les requêtes d'oracles. Le résultat montre que RAD peut améliorer la performance du modèle en présence de bruit sur les étiquettes de données. Trois variations de RAD montrent qu'elles peuvent toutes améliorer le RAD original, et le RAD Active Learning fonctionne presque aussi bien que dans le cas où il n'y a pas de bruit sur les étiquettes<br>System identification and machine learning are two similar concepts independently used in automatic and computer science community. System identification uses statistical methods to build mathematical models of dynamical systems from measured data. Machine learning algorithms build a mathematical model based on sample data, known as "training data" (clean or not), in order to make predictions or decisions without being explicitly programmed to do so. Except prediction accuracy, converging speed and stability are another two key factors to evaluate the training process, especially in the online learning scenario, and these properties have already been well studied in control theory. Therefore, this thesis will implement the interdisciplinary researches for following topic: 1) System identification and optimal control on macroeconomic data: We first modelize the China macroeconomic data on Vector Auto-Regression (VAR) model, then identify the cointegration relation between variables and use Vector Error Correction Model (VECM) to study the short-time fluctuations around the long-term equilibrium, Granger Causality is also studied with VECM. This work reveals the trend of China's economic growth transition: from export-oriented to consumption-oriented; Due to limitation of China economic data, we turn to use France macroeconomic data in the second study. We represent the model in state-space, put the model into a feedback control framework, the controller is designed by Linear-Quadratic Regulator (LQR). The system can apply the control law to bring the system to a desired state. We can also impose perturbations on outputs and constraints on inputs, which emulates the real-world situation of economic crisis. Economists can observe the recovery trajectory of economy, which gives meaningful implications for policy-making. 2) Using control theory to improve the online learning of deep neural network: We propose a performance-based learning rate algorithm: E (Exponential)/PD (Proportional Derivative) feedback control, which consider the Convolutional Neural Network (CNN) as plant, learning rate as control signal and loss value as error signal. Results show that E/PD outperforms the state-of-the-art in final accuracy, final loss and converging speed, and the result are also more stable. However, one observation from E/PD experiments is that learning rate decreases while loss continuously decreases. But loss decreases mean model approaches optimum, we should not decrease the learning rate. To prevent this, we propose an event-based E/PD. Results show that it improves E/PD in final accuracy, final loss and converging speed; Another observation from E/PD experiment is that online learning fixes a constant training epoch for each batch. Since E/PD converges fast, the significant improvement only comes from the beginning epochs. Therefore, we propose another event-based E/PD, which inspects the historical loss, when the progress of training is lower than a certain threshold, we turn to next batch. Results show that it can save up to 67% epochs on CIFAR-10 dataset without degrading much performance. 3) Machine learning out of unreliable data: We propose a generic framework: Robust Anomaly Detector (RAD), The data selection part of RAD is a two-layer framework, where the first layer is used to filter out the suspicious data, and the second layer detects the anomaly patterns from the remaining data. We also derive three variations of RAD namely, voting, active learning and slim, which use additional information, e.g., opinions of conflicting classifiers and queries of oracles. We iteratively update the historical selected data to improve accumulated data quality. Results show that RAD can continuously improve model's performance under the presence of noise on labels. Three variations of RAD show they can all improve the original setting, and the RAD Active Learning performs almost as good as the case where there is no noise on labels
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Mayer, Alexander Simon [Verfasser], Michael [Gutachter] Massmann, and Jörg [Gutachter] Breitung. "Testing for exogeneity and an essay on the econometrics of adaptive learning models / Alexander Simon Mayer ; Gutachter: Michael Massmann, Jörg Breitung." Vallendar : WHU - Otto Beisheim School of Management, 2021. http://d-nb.info/1238595677/34.

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Machado, Vicente da Gama. "Essays on inflation and monetary policy." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/40247.

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Esta tese é composta de três artigos relacionados à política monetária e inflação e possuem em comum a ênfase na importância das expectativas tanto para o desenho da política monetária como para a dinâmica inflacionária. No primeiro ensaio, contribuímos para o debate sobre a resposta apropriada de política monetária a flutuações de preços de ativos em um contexto de aprendizagem adaptativa. O modelo conta com dois tipos de regras de juros instrumentais como em Bullard e Mitra (2002), porém com um papel adicional para preços de ativos. Do ponto de vista da E-Estabilidade, conclui-se que uma resposta a preços de ativos não é desejável nem com a regra que utiliza expectativas futuras nem com a regra que responde a valores contemporâneos. Crenças heterogêneas a respeito da dinâmica das flutuações de preços de ativos, inflação e hiato do produto são introduzidas. Também é avaliada uma regra de política monetária ótima que inclui um peso para os preços de ativos. De forma geral, conclui-se que o princípio de Taylor é relevante para todas as regras de juros analisadas e que os bancos centrais devem agir com cautela ao considerar a introdução de preços de ativos na política monetária. No segundo ensaio, oferecemos estimativas recentes de persistência inflacionária no Brasil, com uma abordagem multivariada de componentes não-observados, na qual são consideradas as seguintes fontes que impactam na persistência da inflação: desvios das expectativas da meta real de inflação; persistência dos fatores que provocam inflação; e termos defasados da inflação. Dados de inflação, produto e taxas de juros são decompostos em componentes não-observados e, para simplificar a estimativa de um número grande de variáveis desconhecidas, utilizamos análise bayesiana, seguindo Dossche e Everaert (2005). Os resultados indicam que a persistência baseada em expectativas tem grande participação na persistência inflacionária no Brasil, que tem diminuído nos últimos anos. Tal resultado implica que apenas as tradicionais fricções no ajuste de preços usadas nos modelos macroeconômicos não são suficientes para representar a real persistência da inflação. No último capítulo estimamos diversas curvas de Phillips reduzidas com dados brasileiros recentes, numa abordagem de séries de tempo com componentes não-observados, que se apresenta como alternativa às tradicionais estimativas, baseadas em métodos GMM, de curvas de Phillips Novo-Keynesianas (NKPC), que raramente foram bem sucedidas empiricamente. A decomposição em tendência, sazonalidade e ciclo oferece, através do resultado gráfico, interpretação econômica direta. Diferentemente de Harvey (2011), incluímos expectativas de inflação nas estimações, assim como na NKPC habitual. A inflação no Brasil parece ter respondido cada vez menos às medidas de atividade econômica consideradas. Isso consiste em evidência de achatamento da curva de Phillips no Brasil, o que significa por um lado custos de desinflação mais altos, mas por outro lado menores pressões inflacionárias derivadas de crescimento do produto.<br>This thesis is composed of three essays on monetary policy and inflation that share particular emphasis on the importance of expectations for both monetary policy design and inflation dynamics. First we contribute to the debate on the appropriate response of monetary policy to asset price fluctuations in an adaptive learning context. Our model accounts for two types of instrumental rules in the spirit of Bullard and Mitra (2002), but with an additional role for asset prices. From the point of view of EStability, we find that a response to stock prices is not desirable under both a forward expectations policy rule and an interest rate rule responding to contemporaneous values. Heterogeneous beliefs about the dynamics of asset price fluctuations, inflation and the output gap are introduced. We also evaluate an optimal monetary policy rule including a weight on asset prices. Overall we find that the Taylor principle remain important over all interest rate rules analysed and that central banks should remain cautious when considering the introduction of stock prices in monetary policy. In the second essay, we provide recent estimates of inflation persistence in Brazil in a multivariate framework of unobserved components, whereby we account for the following sources affecting inflation persistence: First, deviations of expectations from the actual policy target; second, persistence of the factors driving inflation; and third, lagged inflation terms. Data on inflation, output and interest rates are decomposed into unobserved components and to simplify the estimation of a great number of unknown variables, we utilize bayesian analysis as in Dossche and Everaert (2005). Our results indicate that expectations-based persistence matters considerably for inflation persistence in Brazil, which has experienced an overall decrease in the last few years. This finding implies that traditional price-setting frictions used in macroeconomic models are not enough to represent actual inflation persistence. In the last chapter we estimate alternative reduced-form Phillips curves with recent Brazilian data, using a framework of time series with unobserved components, as an alternative to traditional GMM estimations of the New Keynesian Phillips Curve (NKPC), which have seldom been empirically successful. The decomposition into trend, seasonal and cycle features offers, through the graphical output, straightforward economic interpretations. Differently from Harvey (2011), we allow for inflation expectations as in the usual NKPC. Inflation in Brazil seems to have responded gradually less to measures of economic activity in recent years. This provides some evidence of a flattening of the Phillips curve in Brazil, which means higher costs of disinflation on the one hand, but also lower inflationary pressures derived from output growth, on the other.
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Ormeño, Sánchez Arturo. "Essays on Inflation Expectations, Heterogeneous Agents, and the Use of Approximated Solutions in the Estimation of DSGE models." Doctoral thesis, Universitat Pompeu Fabra, 2011. http://hdl.handle.net/10803/51247.

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In this thesis I evaluate the departures of three common assumptions in macroeconomic modeling and estimation, namely the Rational Expectations (RE) hypothesis, the representative agent assumption and the use of first-order approximations in the estimation of dynamic stochastic general equilibrium (DSGE) models. In the first chapter I determine how the use of survey data on inflation expectations in the estimation of a model alters the evaluation of the RE assumption in comparison to an alternative assumption, namely learning. In chapter two, I use heterogeneous agent models to determine the relationship between income volatility and the demand for durable goods. In the third chapter I evaluate if the use of first-order approximations in the estimation of a model could affect the evaluation of the determinants of the Great Moderation.<br>En esta tesis analizo desvíos de tres supuestos comunes en la elaboración y estimación de modelos macroeconómicos. Estos supuestos son la Hipótesis de Expectativas Racionales (ER), el supuesto del Agente Representativo, y el uso de aproximaciones de primer orden en la estimación de los modelos de equilibrio general. En el primer capítulo determino como el empleo de datos de expectativas de inflación en la estimación de un modelo puede alterar la evaluación del supuesto de ER en comparación a un supuesto alternativo como learning. En el segundo capítulo, utilizo modelos de agentes heterogéneos para determinar la relación entre la volatilidad de los ingresos y la demanda de bienes durables. En el tercer capítulo, analizo si el uso de aproximaciones de primer orden afecta la evaluación de los determinantes de la Gran Moderación.
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ADAM, Klaus. "Learning and Price Behavior: microeconomic and macroeconomic implications." Doctoral thesis, 2001. http://hdl.handle.net/1814/4863.

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Defence date: 4 May 2001<br>Examining board: Prof. Seppo Honkapohja, University of Helsinki ; Prof. Ramon Marimon, EUI and Under-Secretary for Science and Technology, Madrid, Supervisor ; Prof. Thomas Sargent, Hoover Institution, Stanford University ; Prof. Karl Schlag, EUI<br>PDF of thesis uploaded from the Library digitised archive of EUI PhD theses completed between 2013 and 2017
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Books on the topic "Learning – Econometric models"

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Acemoglu, Daron. Learning and disagreement in an uncertain world. National Bureau of Economic Research, 2006.

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Acemoglu, Daron. Learning and disagreement in an uncertain world. Massachusetts Institute of Technology, Dept. of Economics, 2006.

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Gourinchas, Pierre-Olivier. Exchange rate dynamics and learning. National Bureau of Economic Research, 1996.

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Guidolin, Massimo. Home bias and high turnover in an overlapping generations model with learning. Federal Reserve Bank of St. Louis, 2005.

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Guidolin, Massimo. Pessimistic beliefs under rational learning: Quantitative implications for the equity premium puzzle. Federal Reserve Bank of St. Louis, 2005.

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Guidolin, Massimo. Properties of equilibrium asset prices under alternative learning schemes. Federal Reserve Bank of St. Louis, 2005.

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Gilchrist, Simon. Expectations, asset prices, and monetary policy: The role of learning. National Bureau of Economic Research, 2006.

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Bouakez, Hafedh. Learning-by-doing or habit formation? Bank of Canada, 2005.

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Bouakez, Hafedh. Learning-by-doing or habit formation? Bank of Canada, 2005.

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Jacques. Productivity shocks, learning, and open economy dynamics. International Monetary Fund, IMF Institute, 2004.

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Book chapters on the topic "Learning – Econometric models"

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Chan, Felix, and László Mátyás. "Linear Econometric Models with Machine Learning." In Advanced Studies in Theoretical and Applied Econometrics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15149-1_1.

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Chan, Felix, Mark N. Harris, Ranjodh B. Singh, and Wei Ern Yeo. "Nonlinear Econometric Models with Machine Learning." In Advanced Studies in Theoretical and Applied Econometrics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15149-1_2.

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Mariel, Petr, David Hoyos, Jürgen Meyerhoff, et al. "Econometric Modelling: Extensions." In Environmental Valuation with Discrete Choice Experiments. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62669-3_6.

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AbstractThis chapter is devoted to advanced issues of econometric modelling. The topics covered are, among others, models in willingness to pay space, the meaning of scale heterogeneity in discrete choice models and the application of various information processing rules such as random regret minimisation or attribute non-attendance. Other topics are anchoring and learning effects when respondents move through a sequence of choice tasks as well as different information processing strategies such as lexicographic preferences or choices based on elimination-by-aspects.
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Lehrer, Steven F., Tian Xie, and Guanxi Yi. "Do the Hype of the Benefits from Using New Data Science Tools Extend to Forecasting Extremely Volatile Assets?" In Data Science for Economics and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_13.

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AbstractThis chapter first provides an illustration of the benefits of using machine learning for forecasting relative to traditional econometric strategies. We consider the short-term volatility of the Bitcoin market by realized volatility observations. Our analysis highlights the importance of accounting for nonlinearities to explain the gains of machine learning algorithms and examines the robustness of our findings to the selection of hyperparameters. This provides an illustration of how different machine learning estimators improve the development of forecast models by relaxing the functional form assumptions that are made explicit when writing up an econometric model. Our second contribution is to illustrate how deep learning can be used to measure market-level sentiment from a 10% random sample of Twitter users. This sentiment variable significantly improves forecast accuracy for every econometric estimator and machine algorithm considered in our forecasting application. This provides an illustration of the benefits of new tools from the natural language processing literature at creating variables that can improve the accuracy of forecasting models.
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Buckmann, Marcus, Andreas Joseph, and Helena Robertson. "Opening the Black Box: Machine Learning Interpretability and Inference Tools with an Application to Economic Forecasting." In Data Science for Economics and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66891-4_3.

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AbstractWe present a comprehensive comparative case study for the use of machine learning models for macroeconomics forecasting. We find that machine learning models mostly outperform conventional econometric approaches in forecasting changes in US unemployment on a 1-year horizon. To address the black box critique of machine learning models, we apply and compare two variables attribution methods: permutation importance and Shapley values. While the aggregate information derived from both approaches is broadly in line, Shapley values offer several advantages, such as the discovery of unknown functional forms in the data generating process and the ability to perform statistical inference. The latter is achieved by the Shapley regression framework, which allows for the evaluation and communication of machine learning models akin to that of linear models.
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Vovsha, Peter. "Comparison of Traditional Econometric Models and Machine Learning Methods in the Context of Travel Decision Making and Perspectives for Synergy." In Decision Economics: Minds, Machines, and their Society. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75583-6_18.

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Arminger, Gerhard. "The Analysis of Growth and Learning Curves with Mean- and Covariance Structure Models." In Econometrics in Theory and Practice. Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-642-47027-1_14.

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Parvin Hosseini, Seyed Mehrshad, and Aydin Azizi. "Machine Learning Approach to Identify Predictors in an Econometric Model of Innovation." In Big Data Approach to Firm Level Innovation in Manufacturing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6300-3_4.

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Yu, Lean, Shouyang Wang, and Kin Keung Lai. "A Hybrid Econometric-AI Ensemble Learning Model for Chinese Foreign Trade Prediction." In Computational Science – ICCS 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72590-9_14.

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Seregina, Ekaterina. "Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance." In Advanced Studies in Theoretical and Applied Econometrics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15149-1_8.

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Conference papers on the topic "Learning – Econometric models"

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Sedlak, Otilija, Jelena Birovljev, Zoran Ciric, Jelica Eremic, and Ivana Ciric. "ANALYSIS OF COMPETITIVENESS OF HIGHER EDUCATION WITH ECONOMETRIC MODELS." In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.1121.

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Chatterjee, Ananda, Hrisav Bhowmick, and Jaydip Sen. "Stock Price Prediction Using Time Series, Econometric, Machine Learning, and Deep Learning Models." In 2021 IEEE Mysore Sub Section International Conference (MysuruCon). IEEE, 2021. http://dx.doi.org/10.1109/mysurucon52639.2021.9641610.

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Asensio, Omar Isaac, Daniel J. Marchetto, Sooji Ha, and Sameer Dharur. "Extracting User Behavior at Electric Vehicle Charging Stations with Transformer Deep Learning Models." In CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11613.

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Mobile applications have become widely popular for their ability to access real-time information. In electric vehicle (EV) mobility, these applications are used by drivers to locate charging stations in public spaces, pay for charging transactions, and engage with other users. This activity generates a rich source of data about charging infrastructure and behavior. However, an increasing share of this data is stored as unstructured text—inhibiting our ability to interpret behavior in real-time. In this article, we implement recent transformer-based deep learning algorithms, BERT and XLnet, that have been tailored to automatically classify short user reviews about EV charging experiences. We achieve classification results with a mean accuracy of over 91% and a mean F1 score of over 0.81 allowing for more precise detection of topic categories, even in the presence of highly imbalanced data. Using these classification algorithms as a pre-processing step, we analyze a U.S. national dataset with econometric methods to discover the dominant topics of discourse in charging infrastructure. After adjusting for station characteristics and other factors, we find that the functionality of a charging station is the dominant topic among EV drivers and is more likely to be discussed at points-of-interest with negative user experiences.
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Dehon, Catherine, Philippe Emplit, and Emma Van Lierde. "A case study of learning analytics within a statistics course for undergraduate students in economics." In Decision Making Based on Data. International Association for Statistical Education, 2019. http://dx.doi.org/10.52041/srap.19407.

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Higher education institutions globally face a continuous expansion of their enrolment in which learner success constitutes a major challenge. Therefore, there is growing interest in the analysis of data linked to student learning engagement. Indeed, large amounts of learning-related student data are currently not being fully exploited, while their aggregation and quantitative analysis would definitely be elements valuable to support teachers and students, to optimize students’ learning experience. In this global context, we have applied, in a public university without any academic filter for enrolment, such analysis to virtually tutor first-year undergraduate students in a statistics course. By supporting them in the form of voluntary online self-assessing tests, we examined what were the personal profiles of the students who were using available tests and how they exploited this help. Finally, using econometric models we tried to determine if there was a link between student success and the use of this help.
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Takara, Lucas de Azevedo, Viviana Cocco Mariani, and Leandro dos Santos Coelho. "Autoencoder Neural Network Approaches for Anomaly Detection in IBOVESPA Stock Market Index." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2021. http://dx.doi.org/10.21528/cbic2021-37.

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Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. Anomaly detection has been applied to many problems such as bank fraud, fault detection, noise reduction, among many others. Some approaches to detect anomalies include classical statistical econometric methods such as AutoRegressive Moving Average (ARMA) and AutoRegressive Integrated Moving Average (ARIMA) approaches. More recently, with the progress of artificial intelligence and more specifically, machine learning, new algorithms such as one-class support vector machines, isolation forest, gradient boosting, and deep neural networks were applied to such tasks. This paper focuses on propose an anomaly detection framework for the Índice da Bolsa de Valores de São Paulo (IBOVESPA). It is a major stock market index that tracks the performance of around 50 most liquid stocks traded on the São Paulo Stock Exchange in Brazil. Exploring unsupervised autoencoder neural network algorithms, we compare the long short-term autoencoder, bidirectional long short-term autoencoder, and convolutional autoencoder models, aiming to explore the performance of these architectures for anomaly detection. Due to the ability of autoencoders to learn a compressed representation of their respective input, we train these models with standard data by minimizing the mean absolute error (MAE) loss function and evaluate them with anomalous inputs. We set a reconstruction error threshold, and in case that the reconstruction error of the test data sample is beyond it, anomalies are detected. Our results show that these models perform quite well and can be applied to real stock market data.
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Silva, Roberto, Bruna Barreira, Fernando Xavier, Antonio Saraiva, and Carlos Cugnasca. "Use of econometrics and machine learning models to predict the number of new cases per day of COVID-19." In Anais Principais do Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/sbcas.2020.11525.

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The COVID-19 pandemics will impact the demand for healthcare severely. It is essential to continually monitor and predict the expected number of new cases for each country. We explored the use of econometrics, machine learning, and ensemble models to predict the number of new cases per day for Brazil, China, Italy, and South Korea. These models can be used to make predictions in the short term, complementing the epidemiological models. Our main findings were: (i) there is no single best model for all countries; (ii) ensembles can, in some instances, improve the results of individual models; and (iii) the ML models had worse results due to the lack of data.
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Silva, Roberto F., Bruna L. Barreira, and Carlos E. Cugnasca. "Prediction of Corn and Sugar Prices Using Machine Learning, Econometrics, and Ensemble Models." In EFITA International Conference. MDPI, 2021. http://dx.doi.org/10.3390/engproc2021009031.

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Ren, Yi, and Panos Y. Papalambros. "On the Use of Active Learning in Engineering Design." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70624.

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Active learning refers to the mechanism of querying users to accomplish a classification task in machine learning or a conjoint analysis in econometrics with minimum cost. Classification and conjoint analysis have been introduced to design research to automate design feasibility checking and to construct marketing demand models, respectively. In this paper, we review active learning algorithms from computer and marketing science, and establish the mathematical commonality between the two approaches. We compare empirically the performance of active learning and static D-optimal design on simulated classification and conjoint analysis test problems with labelling noise. Results show that active learning outperforms D-optimal design when query size is large or noise is small.
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Oladipo, Adenike, Esther Oladele, and David Oke. "Perceived Influence of Emerging Technologies on Lifelong Learning and Resilience among Women Who Dare Open Distance Learning." In Tenth Pan-Commonwealth Forum on Open Learning. Commonwealth of Learning, 2022. http://dx.doi.org/10.56059/pcf10.8949.

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In this study, the authors investigated the influence of emerging technologies on lifelong learning and resilience among women who dare Open and Distance Learning in a dual mode tertiary institution in the South-West geo-political zone of Nigeria. The sample consisted of 431 female learners from the three available departments; Management Sciences, Social Sciences and Science Education. Both secondary and primary data were collected; the latter was collected using a questionnaire on Google Forms. The data were analysed descriptively and using the ordinary least squares regression with robust estimates of standard error. This latter method helps to overcome the possible econometric problems of serial correlation and heteroscedasticity in the model. Preliminary data shows that women enrolments over a period of ten years have consistently increased. Also, most learners were aware of the emerging technologies except technologies such as edublog.com, Edmodo, Weebly and Wiki spaces. The regression result shows that emerging technologies influenced lifelong learning and resilience of the women. Meanwhile, emerging technologies that influenced lifelong learning and resilience were google classroom and Facebook only, while google drive posed a negative influence. Thus, more robust technologies, special female facilities and newly emerging technology job oriented fields should be introduced.
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Gui, Jiyuan, and Xiaoyun Wu. "Forecasting the stock price of vaccine manufacturers in China using machine learning and econometrics model." In International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), edited by Yuanchang Zhong. SPIE, 2022. http://dx.doi.org/10.1117/12.2647506.

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Reports on the topic "Learning – Econometric models"

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Hlushak, Oksana M., Svetlana O. Semenyaka, Volodymyr V. Proshkin, Stanislav V. Sapozhnykov, and Oksana S. Lytvyn. The usage of digital technologies in the university training of future bachelors (having been based on the data of mathematical subjects). [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3860.

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This article demonstrates that mathematics in the system of higher education has outgrown the status of the general education subject and should become an integral part of the professional training of future bachelors, including economists, on the basis of intersubject connection with special subjects. Such aspects as the importance of improving the scientific and methodological support of mathematical training of students by means of digital technologies are revealed. It is specified that in order to implement the task of qualified training of students learning econometrics and economic and mathematical modeling, it is necessary to use digital technologies in two directions: for the organization of electronic educational space and in the process of solving applied problems at the junction of the branches of economics and mathematics. The advantages of using e-learning courses in the educational process are presented (such as providing individualization of the educational process in accordance with the needs, characteristics and capabilities of students; improving the quality and efficiency of the educational process; ensuring systematic monitoring of the educational quality). The unified structures of “Econometrics”, “Economic and mathematical modeling” based on the Moodle platform are the following ones. The article presents the results of the pedagogical experiment on the attitude of students to the use of e-learning course (ELC) in the educational process of Borys Grinchenko Kyiv University and Alfred Nobel University (Dnipro city). We found that the following metrics need improvement: availability of time-appropriate mathematical materials; individual approach in training; students’ self-expression and the development of their creativity in the e-learning process. The following opportunities are brought to light the possibilities of digital technologies for the construction and research of econometric models (based on the problem of dependence of the level of the Ukrainian population employment). Various stages of building and testing of the econometric model are characterized: identification of variables, specification of the model, parameterization and verification of the statistical significance of the obtained results.
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