Relevant bibliographies by topics / Bayesian VAR

Contents

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

Academic literature on the topic 'Bayesian VAR'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Bayesian VAR"

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Poghosyan, Karen. "A Comparison of Different Short-Term Macroeconomic Forecasting Models: Evidence from Armenia." Journal of Central Banking Theory and Practice 5, no. 2 (May 1, 2016): 81–99. http://dx.doi.org/10.1515/jcbtp-2016-0012.

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Abstract We evaluate the forecasting performance of four competing models for short-term macroeconomic forecasting: the traditional VAR, small scale Bayesian VAR, Factor Augmented VAR and Bayesian Factor Augmented VAR models. Using Armenian quarterly actual macroeconomic time series from 1996Q1 – 2014Q4, we estimate parameters of four competing models. Based on the out-of-sample recursive forecast evaluations and using root mean squared error (RMSE) criterion we conclude that small scale Bayesian VAR and Bayesian Factor Augmented VAR models are more suitable for short-term forecasting than tra
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Billio, Monica, Roberto Casarin, and Luca Rossini. "Bayesian nonparametric sparse VAR models." Journal of Econometrics 212, no. 1 (September 2019): 97–115. http://dx.doi.org/10.1016/j.jeconom.2019.04.022.

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Korobilis, Dimitris. "VAR FORECASTING USING BAYESIAN VARIABLE SELECTION." Journal of Applied Econometrics 28, no. 2 (October 26, 2011): 204–30. http://dx.doi.org/10.1002/jae.1271.

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Williams, John T. "Dynamic Change, Specification Uncertainty, and Bayesian Vector Autoregression Analysis." Political Analysis 4 (1992): 97–125. http://dx.doi.org/10.1093/pan/4.1.97.

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The analysis of time-series data is fraught with problems of specification uncertainty and dynamic instability. Vector autoregression (VAR) is one attempt to overcome specification problems in time-series analysis, but this methodology has been criticized for being unparsimonious and potentially unstable through time.1 This article describes an important extension of VAR, one using Bayesian methods and allowing for time-varying parameters. These extensions improve VAR, making analysis less vulnerable to these criticisms. These VAR methods, developed by Doan, Litterman, and Sims (1984), provide
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Bodnar, Taras, Mathias Lindholm, Vilhelm Niklasson, and Erik Thorsén. "Bayesian portfolio selection using VaR and CVaR." Applied Mathematics and Computation 427 (August 2022): 127120. http://dx.doi.org/10.1016/j.amc.2022.127120.

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Sun, Dongchu, and Shawn Ni. "A Bayesian analysis of normalized VAR models." Journal of Multivariate Analysis 124 (February 2014): 247–59. http://dx.doi.org/10.1016/j.jmva.2013.11.004.

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George, Edward I., Dongchu Sun, and Shawn Ni. "Bayesian stochastic search for VAR model restrictions." Journal of Econometrics 142, no. 1 (January 2008): 553–80. http://dx.doi.org/10.1016/j.jeconom.2007.08.017.

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Chin, Kuo-Hsuan, and Xue Li. "Bayesian forecast combination in VAR-DSGE models." Journal of Macroeconomics 59 (March 2019): 278–98. http://dx.doi.org/10.1016/j.jmacro.2018.12.004.

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Koop, Gary. "Bayesian Methods for Empirical Macroeconomics with Big Data." Review of Economic Analysis 9, no. 1 (April 9, 2017): 33–56. http://dx.doi.org/10.15353/rea.v9i1.1434.

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Bayesian econometric methods are increasingly popular in empirical macroeconomics. They have been particularly popular among macroeconomists working with Big Data (where the number of variables under study is large relative to the number of observations). This paper, which is based on a keynote address at the Rimini Centre for Economic Analysis' 2016 Money-Macro-Finance Workshop, explains why this is so. It discusses the problems that arise with conventional econometric methods and how Bayesian methods can successfully overcome them either through use of prior shrinkage or through model averag
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Ward, Eric J., Kristin Marshall, and Mark D. Scheuerell. "Regularizing priors for Bayesian VAR applications to large ecological datasets." PeerJ 10 (November 8, 2022): e14332. http://dx.doi.org/10.7717/peerj.14332.

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Using multi-species time series data has long been of interest for estimating inter-specific interactions with vector autoregressive models (VAR) and state space VAR models (VARSS); these methods are also described in the ecological literature as multivariate autoregressive models (MAR, MARSS). To date, most studies have used these approaches on relatively small food webs where the total number of interactions to be estimated is relatively small. However, as the number of species or functional groups increases, the length of the time series must also increase to provide enough degrees of freed
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Dissertations / Theses on the topic "Bayesian VAR"

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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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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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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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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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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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Solcan, Mihaela. "Essays on Macroeconomic Price Adjustments." Thesis, Lyon 2, 2013. http://www.theses.fr/2013LYO22014.

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Au cours de la dernière décennie, les prix des logements ont augmenté de façon spectaculaire dans plusieurs pays à travers le monde. Par exemple, les prix des logements aux États-Unis, en Espagne et en Irlande ont été marqués par des cycles d'expansion et de récession les plus marquants de leur histoire. L'augmentation concomitante des prix des logements (et dans certains cas l’occurrence des épisodes d'expansion - récession) dans de nombreuses économies avancées soulève quelques questions importantes. Y a t-il eu une bulle immobilière dans les pays avancés? Quels sont les principaux détermina
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Sun, Lixin. "Monetary transmission mechanisms and the macroeconomy in China : VAR/VECM approach and Bayesian DSGE model simulation." Thesis, University of Birmingham, 2011. http://etheses.bham.ac.uk//id/eprint/2900/.

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In this thesis, by employing VAR/VECM approach and Bayesian Dynamic Stochastic General Equilibrium (DSGE) Model we have studied and tested the transmission mechanisms of China’s monetary policy and measured the effects of the monetary policy shocks and other exogenous macro shocks on the real macro economy to uncover the attributes of China’s business cycle. On the basis of the specified VAR/VEC Models, a bank lending channel, an interest rate channel and an asset price channel have been identified by using the time series (monthly) data of banks balance sheet variables (deposits, loans, secur
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Ribeiro, Ramos Francisco Fernando, and fr1960@clix pt. "Essays in time series econometrics and forecasting with applications in marketing." RMIT University. Economics, Finance and Marketing, 2007. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20071220.144516.

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This dissertation is composed of two parts, an integrative essay and a set of published papers. The essay and the collection of papers are placed in the context of development and application of time series econometric models in a temporal-axis from 1970s through 2005, with particular focus in the Marketing discipline. The main aim of the integrative essay is on modelling the effects of marketing actions on performance variables, such as sales and market share in competitive markets. Such research required the estimation of two kinds of time series econometric models: multivariate and multip
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Books on the topic "Bayesian VAR"

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Kenny, Geoff. Bayesian VAR models for forecasting Irish inflation. Dublin: 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. Philadelphia: 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. [Wellington]: Reserve Bank of New Zealand, 1993.

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M, Smith Adrian F., ed. Bayesian theory. Chichester, Eng: Wiley, 1994.

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1956-, Allenby Greg M., and McCulloch Robert E, eds. Bayesian statistics and marketing. Hoboken, NJ: Wiley, 2005.

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Structural equation modeling: A Bayesian approach. Chichester, England: Wiley, 2007.

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Box, George E. P. Bayesian inference in statistical analysis. New York: Wiley, 1992.

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Nick, Chater, and Oaksford M, eds. The probabilistic mind: Prospects for Bayesian cognitive science. Oxford: Oxford University Press, 2008.

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Nick, Chater, and Oaksford M, eds. The probabilistic mind: Prospects for Bayesian cognitive science. Oxford: Oxford University Press, 2008.

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An introduction to Bayesian inference in econometrics. New York: John Wiley & Sons, Inc., 1996.

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Book chapters on the topic "Bayesian VAR"

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Polasek, Wolfgang, and Hideo Kozumi. "The VAR-VARCH model: A Bayesian approach." In Modelling and Prediction Honoring Seymour Geisser, 402–13. New York, NY: 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, 117–36. Dordrecht: 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, 73–84. Cham: 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, 685–703. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38253-7_46.

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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, 1–23. Singapore: 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, 85–94. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60354-0_6.

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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, 339–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97273-8_23.

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ANDERSSON, M., and S. KARLSSON. "Bayesian forecast combination for VAR models☆." In Bayesian Econometrics, 501–24. Elsevier, 2008. http://dx.doi.org/10.1016/s0731-9053(08)23015-x.

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Aye, Goodness, Pami Dua, and Rangan Gupta. "Forecasting Indian Macroeconomic Variables Using Medium-Scale VAR Models." In Current Trends in Bayesian Methodology with Applications, 37–57. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b18502-3.

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"Forecasting Indian Macroeconomic Variables Using Medium-Scale VAR Models." In Current Trends in Bayesian Methodology with Applications, 77–98. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b18502-9.

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Conference papers on the topic "Bayesian VAR"

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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. New York, New York, USA: 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. New York, NY, USA: 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. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3560089.3560106.

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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). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/ssmi-19.2019.9.

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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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Sernaqué, Humberto, Moly Meca, Eduardo Zapata, Berenise Marchan, Junior Medina, Denis Nole, Cristhian Aldana, 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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Tuan, Nguyen Ngoc, and Huynh Quyet Thang. "ITERATION SCHEDULING USING BAYESIAN NETWORKS IN AGILE SOFTWARE DEVELOPMENT." In NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN. Publishing House for Science and Technology, 2017. http://dx.doi.org/10.15625/vap.2017.00038.

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Yang, Hojin, Tianshu Shen, and Scott Sanner. "Bayesian Critiquing with Keyphrase Activation Vectors for VAE-based Recommender Systems." In SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3404835.3463108.

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Pereira, Ricardo Cardoso, Pedro Henriques Abreu, and Pedro Pereira Rodrigues. "VAE-BRIDGE: Variational Autoencoder Filter for Bayesian Ridge Imputation of Missing Data." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206615.

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Reports on the topic "Bayesian VAR"

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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, November 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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Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, January 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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Rincón-Castro, Hernán, and Norberto Rodríguez-Niño. Nonlinear pass-through of exchange rate shocks on inflation : a bayesian smooth transition VAR approach. Bogotá, Colombia: Banco de la República, March 2016. http://dx.doi.org/10.32468/be.930.

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Hajdini, Ina. Mis-specified Forecasts and Myopia in an Estimated New Keynesian Model. Federal Reserve Bank of Cleveland, March 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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Álvarez Florens Odendahl, Luis J., and Germán López-Espinosa. Data outliers and Bayesian VARs in the euro area. Madrid: Banco de España, November 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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He, Yuping, Xiaolan He, Chunrong Li, Xiuqing Lu, Cuimin Shi, Junbing He, and Yao Lin. The Conservative Management for Improving Visual Analogue Pain Score (VAS) in Greater Trochanteric Pain Syndrome: A Bayesian analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2022. http://dx.doi.org/10.37766/inplasy2022.8.0068.

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Rijgersberg, Hajo, Frank van de Geijn, Alex van Schaik, Don Willems, and Esther Hogeveen. Grip op kwaliteit van Conference-peren met behulp van een Bayesiaans netwerk : GreenCHAINge G&F DP5 Export peren verre bestemmingen. Wageningen: Wageningen Food & Biobased Research, 2018. http://dx.doi.org/10.18174/465143.

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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), September 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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LI, Zhendong, Hangjian Qiu, xiaoqian Wang, chengcheng Zhang, and Yuejuan Zhang. Comparative Efficacy of 5 non-pharmaceutical Therapies For Adults With Post-stroke Cognitive Impairment: Protocol For A Bayesian Network Analysis Based on 55 Randomized Controlled Trials. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, June 2022. http://dx.doi.org/10.37766/inplasy2022.6.0036.

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Review question / Objective: This study will provide evidence-based references for the efficacy of 5 different non-pharmaceutical therapies in the treatment of post-stroke cognitive impairment(PSCI). 1. Types of studies. Only randomized controlled trials (RCTs) of Transcranial Magnetic Stimulation(TMS), Transcranial Direct Current Stimulation(tDCS), Acupuncture, Virtual Reality Exposure Therapy(VR) and Computer-assisted cognitive rehabilitation(CA) for PSCI will be recruited. Additionally, Studies should be available in full papers as well as peer reviewed and the original data should be clear
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