Relevant bibliographies by topics / Bayesian VARX

Contents

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

Academic literature on the topic 'Bayesian VARX'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Bayesian VARX"

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Belomestny, Denis, Ekaterina Krymova, and Andrey Polbin. "Bayesian TVP-VARX models with time invariant long-run multipliers." Economic Modelling 101 (August 2021): 105531. http://dx.doi.org/10.1016/j.econmod.2021.105531.

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Elias, Intisar, and Taha Hussein Ali. "Choosing an Appropriate Wavelet for VARX Time Series Model Analysis." Journal of Economics and Administrative Sciences 31, no. 146 (2025): 174–96. https://doi.org/10.33095/px3b7908.

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The paper purports to improve the accuracy of VARX (vector autoregressive with exogenous variables) models adopted for economic time series analysis through wavelet transform techniques applied for noise reduction. The research assessed various wavelet types, including Coiflets, Daubechies, Symlets, Biorthogonal, and Reverse Biorthogonal, for the most appropriate wavelet to be used for improving the performance of the models. Furthermore, it made use of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for evaluating the efficacy of each wavelet in the dimension o
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Marchev, Angel, and Boyan Lomev. "Forecasting of the Event-driven Processes Using LSTM Network in the Context of Time of Arrival of On-demand City Transport." IOP Conference Series: Materials Science and Engineering 1317, no. 1 (2024): 012006. http://dx.doi.org/10.1088/1757-899x/1317/1/012006.

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Abstract The precise forecasting of bus arrival times is an important element of implementing on demand city transport. This research uses of Long Short Term Memory (LSTM) networks for predicting bus arrival times in Sofia, Bulgaria. We evaluate the LSTM model against advanced models such as ARIMAX, VARX SARIMAX with Fourier terms Vector Autoregression, Bayesian Fourier models and Backpropagation Neural Networks using Root Mean Squared Error (RMSE) as the performance measure. The results points towards LSTM being better than approaches on routes by adeptly capturing intricate temporal relation
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Oo, May Zun, Chukiat Chaiboonsri, and Kanchana Chokethaworn. "The Impact of Political Transition on Myanmar's Border Trade with Thailand, China, and India after 2021 Myanmar Military Coup: A Panel Analysis." International Journal of Science and Social Science Research 3, no. 1 (2025): 119–28. https://doi.org/10.5281/zenodo.15385589.

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This study investigates Myanmar benefited from competitive advantages in the ASEAN Community through data collection and observation before and after the 2021 Myanmar Military Coup. In our study, it consists of the exchange of goods and services between Myanmar and its neighboring counties, Thailand, China and India through their shared borders often important for economically interdependent regions. The trade involves agricultural products, raw materials, and manufactured goods. Myanmar Military Coup in 2021 can impact especially on the trade because of the changes of customs policies, securi
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Marcos, Vinícius Monteiro da Rocha. "OS STAKEHOLDERS DAS COOPERATIVAS." Revistaft 28, no. 131 (2024): 21. https://doi.org/10.5281/zenodo.10695021.

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O objetivo deste trabalho é, por meio da teoria proposta por Freeman (1984), identificar e analisar a relação dos stakeholders das cooperativas utilizando como referência a teoria da saliência dos stakeholders. Os dados foram coletados através de questionário online direcionado aos gestores de alto escalão da organização. Foi coletado um grupo de variáveis independentes (atributos das cooperativas) e variáveis independentes (percepção dos stakeholders) e a análise de dados será por mei
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Eraker, Bjørn, Ching Wai (Jeremy) Chiu, Andrew T. Foerster, Tae Bong Kim, and Hernán D. Seoane. "Bayesian Mixed Frequency VARs." Journal of Financial Econometrics 13, no. 3 (2014): 698–721. http://dx.doi.org/10.1093/jjfinec/nbu027.

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Billio, Monica, Roberto Casarin, and Luca Rossini. "Bayesian nonparametric sparse VAR models." Journal of Econometrics 212, no. 1 (2019): 97–115. http://dx.doi.org/10.1016/j.jeconom.2019.04.022.

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Yoon, Byung-Jo. "A Study on Economic Policy Uncertainty and Stock Market Using Bayesian Time-Varying Parameter VAR Model." INTERNATIONAL BUSINESS REVIEW 24, no. 3 (2020): 85–93. http://dx.doi.org/10.21739/ibr.2020.09.24.3.85.

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Chan, Joshua C. C. "Asymmetric conjugate priors for large Bayesian VARs." Quantitative Economics 13, no. 3 (2022): 1145–69. http://dx.doi.org/10.3982/qe1381.

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Large Bayesian VARs are now widely used in empirical macroeconomics. One popular shrinkage prior in this setting is the natural conjugate prior as it facilitates posterior simulation and leads to a range of useful analytical results. This is, however, at the expense of modeling flexibility, as it rules out cross‐variable shrinkage, that is, shrinking coefficients on lags of other variables more aggressively than those on own lags. We develop a prior that has the best of both worlds: it can accommodate cross‐variable shrinkage, while maintaining many useful analytical results, such as a closed‐
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Carriero, Andrea, Todd E. Clark, and Massimiliano Marcellino. "Common Drifting Volatility in Large Bayesian VARs." Journal of Business & Economic Statistics 34, no. 3 (2016): 375–90. http://dx.doi.org/10.1080/07350015.2015.1040116.

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Dissertations / Theses on the topic "Bayesian VARX"

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Huber, Florian, Tamás Krisztin, and Philipp Piribauer. "Forecasting Global Equity Indices Using Large Bayesian VARs." WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4318/1/wp184.pdf.

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This paper proposes a large Bayesian Vector Autoregressive (BVAR) model with common stochastic volatility to forecast global equity indices. Using a dataset consisting of monthly data on global stock indices the BVAR model inherently incorporates co-movements in the stock markets. The time-varying specification of the covariance structure moreover accounts for sudden shifts in the level of volatility. In an out-of-sample forecasting application we show that the BVAR model with stochastic volatility significantly outperforms the random walk both in terms of root mean squared errors as well as B
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Houghton, Adrian James. "Variational Bayesian inference for comparison Var(1) models." Thesis, University of Newcastle Upon Tyne, 2009. http://hdl.handle.net/10443/790.

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Suppose that we wish to determine which models in a candidate set are most likely to have given rise to a set of observed data. Then, it is well-established that, from a Bayesian viewpoint, evaluation of the marginal likelihood for each candidate is a crucial step to this end. For the purposes of model comparison, this will enable subsequent computation of both Bayes’ factors and posterior model probabilities. Given its evident significance in this area, it is thus regrettable that analytic calculation of the marginal likelihood is often not possible. To tackle this problem, one recent additio
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Siu, Wai-shing. "On a subjective modelling of VaR fa Bayesian approach /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22823785.

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Lanteri, Luis. "Modelos de VAR alternativos para pronósticos (VAR bayesianos y FAVAR): el caso de las exportaciones argentinas." Economía, 2012. http://repositorio.pucp.edu.pe/index/handle/123456789/117477.

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Exports are one of the key aggregates in the Argentina’s economy, both because to its links with thedomestic demand and by its influence on the behaviour of the trade balance and current account.Have adequate forecasts for this variable is useful to design policies to keep surpluses in the externalsector and prevent recurring crises seen in the past. In this work, we considered some modelsfor forecasting the performance of this aggregate, which could be an alternative to the estimationof structural econometric models. For this purpose, we used two approaches: the first is based instandard and
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Kim, Jae-yoon. "Essays on DSGE Models and Bayesian Estimation." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83515.

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This thesis explores the theory and practice of sovereignty. I begin with a conceptual analysis of sovereignty, examining its theological roots in contrast with its later influence in contestations over political authority. Theological debates surrounding God’s sovereignty dealt not with the question of legitimacy, which would become important for political sovereignty, but instead with the limits of his ability. Read as an ontological capacity, sovereignty is coterminous with an existent’s activity in the world. As lived, this capacity is regularly limited by the ways in which space is produc
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Hauer, Mariana. "Os modelos VAR e VEC espaciais : uma abordagem bayesiana." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/12585.

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O objetivo deste trabalho é apresentar o Modelo Vetorial Autorregressivo (VAR) e uma das suas variações, o Modelo Vetorial de Correções de Erros (VEC), segundo uma abordagem Bayesiana, considerando componentes regionais, que serão inseridos nos modelos apresentados através de informações a priori que levam em consideração a localização dos dados. Para formar tais informações a priori são utilizados conceitos referentes à econometria espacial, como por exemplo, as relações de contigüidade e as implicações que estas trazem. Como exemplo ilustrativo, o modelo em questão será aplicado a um conjunt
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Contino, Christian. "A Bayesian Approach to Risk Management in a World of High-Frequency Data." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14728.

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A Realised Volatility GARCH model using high-frequency data is developed within a Bayesian framework for the purpose of forecasting Value at Risk and Conditional Value at Risk. A Skewed Student-t return distribution is combined with a Student-t distribution in the measurement equation in a GARCH framework. Realised Volatility GARCH models show a marked improvement compared to ordinary GARCH. A Skewed Student-t Realised DCC copula model using Realised Volatility GARCH marginal functions is developed within a Bayesian framework for the purpose of forecasting portfolio tail risk. The use of co
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蕭偉成 and Wai-shing Siu. "On a subjective modelling of VaR: fa Bayesianapproach." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31225159.

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Samuel, Marco Antonio Castelo Branco. "Mudanças de Estado e Multiplicadores Fiscais no Brasil entre 1999-2012." Universidade do Estado do Rio de Janeiro, 2014. http://www.bdtd.uerj.br/tde_busca/arquivo.php?codArquivo=9006.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Este trabalho avalia o comportamento dos multiplicadores fiscais no Brasil entre 1999-2012. Para tanto, utiliza a metodologia desenvolvida por Sims, Waggoner e Zha (2008), que é um procedimento Bayesiano de estimação no qual os parâmetros do modelo mudam com alterações no estado da economia e os estados (regimes) seguem um processo de mudança de regime markoviano. Ou seja, foi estimado um modelo VAR Estrutural Bayesiano com mudança de regimes Markoviana (Markov Switching Structural Bayesian Vector Autoregression - MS-SBVAR). A base
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Unosson, Måns. "A Mixed Frequency Steady-State Bayesian Vector Autoregression: Forecasting the Macroeconomy." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297406.

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This thesis suggests a Bayesian vector autoregressive (VAR) model which allows for explicit parametrization of the unconditional mean for data measured at different frequencies, without the need to aggregate data to the lowest common frequency. Using a normal prior for the steady-state and a normal-inverse Wishart prior for the dynamics and error covariance, a Gibbs sampler is proposed to sample the posterior distribution. A forecast study is performed using monthly and quarterly data for the US macroeconomy between 1964 and 2008. The proposed model is compared to a steady-state Bayesian VAR m
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Books on the topic "Bayesian VARX"

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Kenny, Geoff. Bayesian VAR models for forecasting Irish inflation. Central Bank of Ireland, Economic Analysis, Research and Publications Department, 1998.

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Crone, Theodore M. A Bayesian VAR forecasting model for the Philadelphia Metropolitan Area. Federal Reserve Bank of Philadelphia, Economic Research Division, 1999.

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Wong, Jason. Forecasting inflation and real GDP: Bayesian VAR models of the New Zealand economy. Reserve Bank of New Zealand, 1993.

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Ciccarelli, Matteo. Bayesian VARs: A survey of the recent literature with an application to the European monetary system. International Monetary Fund, Research Department, 2003.

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sterholm, Pr, and Helge Berger. Does Money Matter for U. S. Inflation? Evidence from Bayesian Vars. International Monetary Fund, 2008.

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Berger, Helge, and Pär Österholm. Does Money Matter for U. S. Inflation? Evidence from Bayesian Vars. International Monetary Fund, 2008.

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sterholm, Pr, and Helge Berger. Does Money Matter for U. S. Inflation? Evidence from Bayesian Vars. International Monetary Fund, 2008.

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Abrego, Lisandro, and Pär Österholm. External Linkages and Economic Growth in Colombia: Insights from a Bayesian VAR Model. International Monetary Fund, 2008.

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sterholm, Pr, and Lisandro Abrego. External Linkages and Economic Growth in Colombia: Insights from a Bayesian Var Model. International Monetary Fund, 2008.

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sterholm, Pr, and Lisandro Abrego. External Linkages and Economic Growth in Colombia: Insights from a Bayesian Var Model. International Monetary Fund, 2008.

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Book chapters on the topic "Bayesian VARX"

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Polasek, Wolfgang, and Hideo Kozumi. "The VAR-VARCH model: A Bayesian approach." In Modelling and Prediction Honoring Seymour Geisser. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2414-3_26.

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Bijak, Jakub. "Bayesian VAR Modelling ‘from General to Specific’." In Forecasting International Migration in Europe: A Bayesian View. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8897-0_6.

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Ebrahimijam, Saeed, Cahit Adaoglu, and Korhan K. Gokmenoglu. "Inter-Market Sentiment Analysis Using Markov Switching Bayesian VAR Analysis." In Regulation of Finance and Accounting. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99873-8_6.

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Mokrzycka, Justyna. "VaR and ES Calculation with a Bayesian Dynamic tCopula-GARCH Model." In Advances in Cross-Section Data Methods in Applied Economic Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38253-7_46.

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Safi, Samir K., Olajide Idris Sanusi, and Afreen Arif. "Comparing MIDAS and Bayesian VAR Models for GDP Forecasting: Insights from Simulation and Empirical Studies." In Studies in Big Data. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-71213-5_63.

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Kato, Hisakazu. "Low Fertility and Female Labor Supply in Japan—Time Series Analysis Using Bayesian VAR Approach." In Macro-econometric Analysis on Determinants of Fertility Behavior. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3927-2_1.

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Mitra, Rajarshi, and Maria Evgenievna Guseva. "Does Population Ageing Reduce FDI Inflows in OECD Countries? Evidence from Bayesian Panel VAR Estimates." In Advances in Innovation, Trade and Business. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60354-0_6.

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Diep, Nguyen Thi Ngoc, Tran Quang Canh, and Nguyen Ngọc Thach. "Market Share Forecast of Vietnam and of the World’s Leading Textile and Garment Exporters by VAR Bayesian Model." In Optimal Transport Statistics for Economics and Related Topics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35763-3_30.

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Srichaikul, Wilawan, and Woraphon Yamaka. "Interdependence of Macroeconomic Factors and Economic Growth in OECD Countries: Evidence Based on a Bayesian Panel VAR Model." In Credible Asset Allocation, Optimal Transport Methods, and Related Topics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97273-8_23.

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"Bayesian VARs." In Methods for Applied Macroeconomic Research. Princeton University Press, 2011. http://dx.doi.org/10.2307/j.ctvcm4hrv.13.

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Conference papers on the topic "Bayesian VARX"

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Kumar, Kundan, Aditya Akilesh Mantha, and Gelli Ravikumar. "Bayesian Optimization for Deep Reinforcement Learning for Robust Volt-Var Control." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10688889.

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Wang, Hui, Sreelakshmi Sreeharan, and Homero Casteneda. "Indirect Inspection-based Bayesian Machine Learning Model for Probabilistic Coating Defect Severity Interpretation." In CONFERENCE 2025. AMPP, 2025. https://doi.org/10.5006/c2025-00400.

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Abstract Corrosion pit nucleation and propagation in underground pipelines vary by geographic position along the pipeline right of way (RoW). Effective pipeline integrity management requires both the modeling of corrosion initiation/propagation and the integration of field-acquired data. To mitigate external corrosion, onshore pipelines use barrier coatings and either sacrificial or impressed current cathodic protection (CP) systems. Pipeline integrity is monitored through direct and/or indirect assessments. In this work, we focused on leveraging indirect methods, such as close interval potent
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Chin, Kuo-Hsuan, and Xue Li. "BAYESIAN FORECAST COMBINATION IN VAR-DSGE MODELS." In 32nd International Academic Conference, Geneva. International Institute of Social and Economic Sciences, 2017. http://dx.doi.org/10.20472/iac.2017.032.008.

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Liao, Ruofan, Petchaluck Boonyakunakorn, and Songsak Sriboonchiita. "VaR of SSE returns Based on Bayesian Markov-Switching GARCH Approach." In the 2nd International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3358528.3358545.

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Rojniruttikul, Nuttawut, and Adirek Vajrapatkul. "ICT and Thai Economic Growth Nexus in the Bayesian VAR Model." In IECC 2021: 2021 3rd International Electronics Communication Conference. ACM, 2021. http://dx.doi.org/10.1145/3475971.3475978.

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Rojniruttikul, Nuttawut, and Adirek Vajrapatkul. "The Projection of Thai Manufacturing Export in the Bayesian VAR Model." In IECC 2022: 2022 4th International Electronics Communication Conference. ACM, 2022. http://dx.doi.org/10.1145/3560089.3560106.

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Feng, Wei, Qiaofeng Li, and Qiuhai Lu. "A Hierarchical Bayesian Method for Time Domain Structure Damage Detection." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97026.

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Abstract A time domain structural damage detection method based on hierarchical Bayesian framework is proposed. Due to local stiffness reductions, the responses of damaged structures vary from those in undamaged status under the same external excitation. In this paper, the responses of damaged structures are assumed as the result of a summation of known external forces and unknown virtual forces exerted on corresponding undamaged structures. The damages can thus be detected, located, and quantified by the identification of associated virtual forces. A hierarchical Bayesian formulation consider
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ANTON, George. "THE IMPACT OF ECONOMIC UNCERTAINTY ON HOUSEHOLD CONSUMPTION CHOICES. EVIDENCE FROM EUROPE." In International Management Conference. Editura ASE, 2022. http://dx.doi.org/10.24818/imc/2021/03.18.

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This paper is evaluating the impact of uncertainty shocks that are affecting the household behavior in the European Union countries by employing a quantitative approach. By employing a Bayesian VAR model, this paper provides an answer on the importance of the uncertainty shocks on the household consumption choices by using impulse response functions and variance decompositions statistics. The relevance of the study is a major one as it quantifies the impact of the uncertainty pressure on choices consumers make during uncertain times such as the great recession or covid-19 health crisis. Given
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Jiang, Zejun. "Assessing the Effect of Quantitative Easing on the US Economy from 2008 to 2015 by a Bayesian-VAR Model." In Proceedings of the 2nd International Symposium on Social Science and Management Innovation (SSMI 2019). Atlantis Press, 2019. http://dx.doi.org/10.2991/ssmi-19.2019.9.

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Sernaqué, Humberto, Moly Meca, Eduardo Zapata, et al. "Comparison of Arima and Holt-Winters forecasting models for time series of cereal production in Peru." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001007.

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Agricultural commodities present remarkable volatility in their production levels, which severely affects farmers. The variational dynamics in the prices of the inputs used and the constant variations in weather conditions have a significant influence on the cereal production chain in Peru; therefore, compared to the ARIMA model, the Additive Holt-Winters forecasting model presented a better fit according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), forecasting the production of Oryza sativa, Zea mays L. var. Indurata and Amaranthus caudatus; however,
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Reports on the topic "Bayesian VARX"

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Álvarez Florens Odendahl, Luis J., and Germán López-Espinosa. Data outliers and Bayesian VARs in the euro area. Banco de España, 2022. http://dx.doi.org/10.53479/23552.

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We propose a method to adjust for data outliers in Bayesian Vector Autoregressions (BVARs), which allows for different outlier magnitudes across variables and rescales the reduced form error terms. We use the method to document several facts about the effect of outliers on estimation and out-of-sample forecasting results using euro area macroeconomic data. First, the COVID-19 pandemic led to large swings in macroeconomic data that distort the BVAR estimation results. Second, these swings can be addressed by rescaling the shocks’ variance. Third, taking into account outliers before 2020 leads t
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Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202305.

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In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference us
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Kurozumi, Takushi, Ryohei Oishi, and Willem Van Zandweghe. Sticky Information Versus Sticky Prices Revisited: A Bayesian VAR-GMM Approach. Federal Reserve Bank of Cleveland, 2022. http://dx.doi.org/10.26509/frbc-wp-202234.

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Several Phillips curves based on sticky information and sticky prices are estimated and compared using Bayesian VAR-GMM. This method derives expectations in each Phillips curve from a VAR and estimates the Phillips curve parameters and the VAR coefficients simultaneously. Quasi-marginal likelihood-based model comparison selects a dual stickiness Phillips curve in which, each period, some prices remain unchanged, consistent with micro evidence. Moreover, sticky information is a more plausible source of inflation inertia in the Phillips curve than other sources proposed in previous studies. Stic
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McCracken, Michael W., Michael T. Owyang, and Tatevik Sekhposyan. Real-Time Forecasting and Scenario Analysis using a Large Mixed-Frequency Bayesian VAR. Federal Reserve Bank of St. Louis, 2015. http://dx.doi.org/10.20955/wp.2015.030.

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Rincón-Castro, Hernán, and Norberto Rodríguez-Niño. Nonlinear pass-through of exchange rate shocks on inflation : a bayesian smooth transition VAR approach. Banco de la República, 2016. http://dx.doi.org/10.32468/be.930.

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de Padua, David, Matteo Lanzafame, Irfan Qureshi, and Kiyoshi Taniguchi. Understanding the Drivers of Remittances to Pakistan. Asian Development Bank, 2024. http://dx.doi.org/10.22617/wps240348-2.

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This paper analyzes remittances to Pakistan and identifies the key macroeconomic variables influencing this important source of external financing. Remittances account for approximately 10% of gross domestic product in Pakistan and a better understanding of remittance drivers is needed to inform policies that can bolster their contribution to poverty reduction and other development priorities. To develop this understanding, the authors combined a database of bilateral remittances between Pakistan and its main remittance-sending countries with monthly macroeconomic data over 2003–2021, and appl
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Hajdini, Ina. Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202203r.

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The paper considers a New Keynesian framework in which agents form expectations based on a combination of autoregressive mis-specified forecasts and myopia. The proposed expectations formation process is shown to be consistent with all three empirical facts on consensus inflation forecasts. However, while mis-specified forecasts can be both sufficient and necessary to match all three facts, myopia alone is neither. The paper then derives the general equilibrium solution consistent with the proposed expectations formation process and estimates the model with likelihood-based Bayesian methods, y
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Rossi, José Luiz, and João Paulo Madureira Horta da Costa. Shock Dependent Exchange Rate Pass-Through - An Analysis for Latin American Countries. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005129.

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This paper investigates the exchange rate pass through considering the source of the shocks that hit the economy. With a Bayesian Global VAR model, the exchange rate pass-through is analyzed for 5 Latin American countries: Brazil, Chile, Colombia, Mexico and Peru. The model is estimated with Bayesian techniques and is identified by sign and zero restrictions. The BGVAR estimation enable us to allow spillover between countries mimicking the real conditions when the shocks hit the economies. Four domestic shocks for each Latin American countries are considered: an exchange rate shock, a risk pre
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Granados, Camilo, and Daniel Parra-Amado. Output Gap Measurement after COVID for Colombia: Lessons from a Permanent-Transitory Approach. Banco de la República, 2025. https://doi.org/10.32468/be.1295.

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We estimate the output gap for the Colombian economy explicitly accounting for the COVID-19 period. Our estimates reveal a significant $20$\% decline in the output gap but with a faster recovery compared to previous crises. Our empirical strategy follows a two-stage Bayesian vector autoregressive (BSVAR) model where i) a scaling factor in the reduced form of VAR is used to model extreme data, such as those observed around the COVID-19 period, and ii) permanent and transitory shocks are structurally identified. As a result, we obtain that a single structural shock explains the potential GDP, wh
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Angrist, Noam, and Rachael Meager. The role of implementation in generalisability: A synthesis of evidence on targeted educational instruction and a new randomised trial. Centre for Excellence and Development Impact and Learning (CEDIL), 2022. http://dx.doi.org/10.51744/cswp4.

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Targeted instruction is one of the most effective educational interventions in low- and middle-income countries, yet the reported impacts of this approach vary, from 0.07 to 0.78 standard deviations (SDs) across contexts. We study this variation and the contextual factors associated with it by combining an evidence aggregation covering 10 study arms with a new randomised trial. The results show that two factors explain most of the heterogeneity in reported effects: the degree of implementation (intention-to-treat or treatment-on-the-treated effects) and the instruction delivery model (teachers
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