Relevant bibliographies by topics / Learning – Econometric models

Contents

  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
Last updated: 31 July 2025

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Journal articles on the topic "Learning – Econometric models"

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Silahtaroğlu, Yenilmez Oğuz. "Machine Learning Integration in Econometric Models." Next Generation Journal for The Young Researchers 8, no. 1 (2024): 77. http://dx.doi.org/10.62802/8c33p210.

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The integration of machine learning (ML) into econometric models represents a transformative advancement in the field of econometrics, enabling researchers to tackle complex, high-dimensional datasets while maintaining the interpretability and rigor of traditional econometric approaches. This research investigates the synergies between machine learning and econometrics, focusing on how ML techniques can enhance model flexibility, predictive accuracy, and causal inference in economic analysis. By leveraging methods such as regularization, ensemble learning, and deep learning, the study explores
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Dokumacı, Melis. "AI-Driven Econometric Models for Legal Issues." Human Computer Interaction 8, no. 1 (2024): 137. https://doi.org/10.62802/btfvze98.

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Artificial intelligence (AI) is reshaping the landscape of econometric modeling, offering innovative tools to address complex legal issues involving predictive analysis, resource allocation, and policy evaluation. This research explores the application of AI-driven econometric models to legal challenges, focusing on areas such as contract enforcement, intellectual property disputes, and regulatory compliance. By integrating machine learning with traditional econometric techniques, these models enhance the precision and adaptability of legal forecasts and decision-making processes. Key methodol
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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 marg
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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
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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 ec
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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
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Anand, Kumar Dohare, and Abuzaid Mohammad. "Forecasting Stock Prices through Time Series, Econometric, Machine Learning, and Deep Learning Models." International Journal of Engineering and Management Research 14, no. 1 (2024): 77–85. https://doi.org/10.5281/zenodo.10688767.

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Over an comprehensive ending, scientist have loyal solid efforts to plan a strong and exact predicting foundation for guessing stock prices. Academic discourse emphasizes that intricately devised and refined predicting models occupy the competency to carefully and dependably expect future stock principles. This case introduces a various array of models, including methods to a degree period succession reasoning, econometrics, and miscellaneous knowledge-based approaches tailor-made for stock price guess. Analyzing dossier connecting from January 2004 to December 2019 for famous enterprises to a
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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 ec
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Bukina, T., and D. Kashin. "Regional Inflation Forecasting: Econometric Models Versus Machine Learning Methods?" Higher School of Economics Economic Journal 28, no. 1 (2024): 81–107. http://dx.doi.org/10.17323/1813-8691-2024-28-1-81-107.

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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 heterogeneo
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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
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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
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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-establishe
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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
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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 res
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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 r
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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. Properties of equilibrium asset prices under alternative learning schemes. Federal Reserve Bank of St. Louis, 2005.

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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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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 functi
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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 f
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Biswas, Abhijit, and Meghdoot Ghosh. "Application of Machine Learning, Deep Learning, and Econometric Models in Stock Price Movement of Rain Industries." In Deep Learning Applications in Operations Research. Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781032725444-10.

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Jahnavi, M., Purushottam Bung, N. Nagasubba Reddy, and Katari Santosh. "Comparing Econometric and Machine Learning Models for Gold Price Forecasting: A Comprehensive Approach." In Studies in Big Data. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83915-3_22.

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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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Charpentier, Arthur. "Quantifying Fairness and Discrimination in Predictive Models." In Machine Learning for Econometrics and Related Topics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43601-7_3.

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Briggs, William M. "What Makes a Good Model?" In Machine Learning for Econometrics and Related Topics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-43601-7_2.

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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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Rajaure, Tribikram. "Mode Choice Prediction: Comparing Econometric Models with Combination of Machine Learning Models." In International Conference on Transportation and Development 2025. American Society of Civil Engineers, 2025. https://doi.org/10.1061/9780784486191.035.

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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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Tsolacos, Sotiris, and Tatiana Franus. "Assessing the forecast performance of machine learning algorithms and econometric models in real estate." In 30th Annual European Real Estate Society Conference. European Real Estate Society, 2024. http://dx.doi.org/10.15396/eres2024-251.

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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, tha
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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 e
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Ounsakul, T., T. Techanukul, C. Phasook, and P. Harke. "Spread Rate Forecasting in Well Cost Estimation – A Study of Methods and Applications." In SPE/IADC Asia Pacific Drilling Technology Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/219600-ms.

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Abstract In the realm of well cost estimation, the accurate forecasting of spread rates is pivotal, given the substantial financial implications of erroneous assumptions. This paper, "Spread Rate Forecasting in Well Cost Estimation – A Study of Methods and Applications," delves into the uncertainty inherent. Through a thorough examination of predictive methodologies, the research harnesses both econometric and machine learning models, which are commonly utilized in forecasting crude oil prices. The study formulates models based on publicly available data, such as ‘West Texas Intermediate’ (WTI
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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, gradie
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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; an
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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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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 m
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Koop, Gary, Jamie Cross, and Aubrey Poon. Introduction to Bayesian Econometrics in MATLAB. Instats Inc., 2022. http://dx.doi.org/10.61700/t3wrch7yujr7a469.

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This seminar provides an introduction to Bayesian econometrics. It covers the general theory underlying Bayesian econometrics and Bayesian inference in the linear regression model including an introduction of Bayesian machine learning methods for Big Data regression. Bayesian computational methods such as Gibbs sampling and the Metropolis-Hastings algorithm will be covered, with hands-on lab sections run using real-world data so that you will be able to apply these methods in your ongoing research. An official Instats certificate of completion is provided at the conclusion of the seminar. For
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Koop, Gary, Jamie Cross, and Aubrey Poon. Introduction to Bayesian Econometrics in MATLAB. Instats Inc., 2023. http://dx.doi.org/10.61700/aebi3thp50fr3469.

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This seminar provides an introduction to Bayesian econometrics. It covers the general theory underlying Bayesian econometrics and Bayesian inference in the linear regression model including an introduction of Bayesian machine learning methods for Big Data regression. Bayesian computational methods such as Gibbs sampling and the Metropolis-Hastings algorithm will be covered, with hands-on lab sections run using real-world data so that you will be able to apply these methods in your ongoing research. An official Instats certificate of completion is provided at the conclusion of the seminar. For
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Torres, Arturo, Fernando Santiago, Natalia Gras, Claudia De Fuentes, and Gabriela Dutrénit. Innovation and Productivity in the Service Sector: The Case of Mexico. Inter-American Development Bank, 2013. http://dx.doi.org/10.18235/0006954.

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An extensive literature analyzes the determinants of research and development (R&amp;D) and the impacts of R&amp;D on firms' innovation performance and productivity. Because most available studies focus on manufacturing firms, very little is known about firms in the service sector. The gap is even more noticeable in the case of service firms in developing countries. Based on data from the latest available Mexican innovation survey, we explore the determinants of-including the barriers to-technological innovation and the impact of innovation on service firms' productivity in Mexico. Results fro
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