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Bayesian Variable Selection for Nowcasting Economic Time Series. (2013). Varian, Hal ; Scott, Steven L..
In: NBER Working Papers.
RePEc:nbr:nberwo:19567.

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Cited: 21

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Cocites: 26

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  1. GDP nowcasting with Machine Learning and Unstructured Data to Peru. (2023). TENORIO, JUAN ; Perez, Wilder.
    In: Working Papers.
    RePEc:apc:wpaper:197.

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  2. Can Google Trends data provide information on consumer’s perception regarding hotel brands?. (2022). Bakirtas, Hulya ; Demirci, Vildan Gulpinar.
    In: Information Technology & Tourism.
    RePEc:spr:infott:v:24:y:2022:i:1:d:10.1007_s40558-022-00220-1.

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  3. The impact of policy priority flexibility on the speed of renewable energy adoption. (2022). Inglesi-Lotz, Roula ; Oosthuizen, A M.
    In: Renewable Energy.
    RePEc:eee:renene:v:194:y:2022:i:c:p:426-438.

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  4. Quantifying the impact of COVID-19 on non-motorized transportation: A Bayesian structural time series model. (2021). Fricker, Jon D ; Zhang, Yunchang.
    In: Transport Policy.
    RePEc:eee:trapol:v:103:y:2021:i:c:p:11-20.

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  5. Using four different online media sources to forecast the crude oil price. (2021). Battistoni, E ; Colladon, Fronzetti A ; Elshendy, M ; Gloor, P A.
    In: Papers.
    RePEc:arx:papers:2105.09154.

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  6. Revealing the mood of economic agents based on search queries. (2020). Trunin, Pavel ; Petrova, Diana.
    In: Applied Econometrics.
    RePEc:ris:apltrx:0400.

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  7. The Effect of Market-Oriented Government Fiscal Expenditure on the Evolution of Industrial Structure: Evidence from Shenzhen, China. (2020). Qi, Zhongying ; Yingqi, Zhong ; Shu, Yumin.
    In: Sustainability.
    RePEc:gam:jsusta:v:12:y:2020:i:9:p:3703-:d:353648.

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  8. Crude oil price analysis and forecasting: A perspective of “new triangle”. (2020). Wang, Shouyang ; Chai, Jian ; Li, Yuze ; Lu, Quanying.
    In: Energy Economics.
    RePEc:eee:eneeco:v:87:y:2020:i:c:s0140988320300608.

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  9. Forecasting the patterns of COVID-19 and causal impacts of lockdown in top five affected countries using Bayesian Structural Time Series Models. (2020). Feroze, Navid.
    In: Chaos, Solitons & Fractals.
    RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305920.

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  10. Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach. (2020). Ukkusuri, Satish ; Zhang, Yunchang ; Yabe, Takahiro.
    In: Papers.
    RePEc:arx:papers:2004.11121.

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  11. The effect of bariatric surgery on health care costs: A synthetic control approach using Bayesian structural time series. (2019). Holle, Rolf ; Schwarzkopf, Larissa ; Laxy, Michael ; Teuner, Christina ; Rehm, Martin ; Kurz, Christoph F.
    In: Health Economics.
    RePEc:wly:hlthec:v:28:y:2019:i:11:p:1293-1307.

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  12. Exploring the dynamics of Bitcoin’s price: a Bayesian structural time series approach. (2019). Poyser, Obryan.
    In: Eurasian Economic Review.
    RePEc:spr:eurase:v:9:y:2019:i:1:d:10.1007_s40822-018-0108-2.

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  13. Business Cycle Narratives. (2019). Thorsrud, Leif ; Larsen, Vegard.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_7468.

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  14. Exploring the determinants of Bitcoins price: an application of Bayesian Structural Time Series. (2017). Poyser, Obryan.
    In: Papers.
    RePEc:arx:papers:1706.01437.

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  15. Forecasting Chinese tourist volume with search engine data. (2015). Yang, Xin ; Lv, Benfu ; Evans, James A ; Pan, Bing.
    In: Tourism Management.
    RePEc:eee:touman:v:46:y:2015:i:c:p:386-397.

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  16. The Data Revolution and Economic Analysis. (2014). Levin, Jonathan ; Einav, Liran.
    In: Innovation Policy and the Economy.
    RePEc:ucp:ipolec:doi:10.1086/674019.

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  17. Using Social Media to Measure Labor Market Flows. (2014). Shapiro, Matthew ; Levenstein, Margaret ; Antenucci, Dolan ; Cafarella, Michael .
    In: NBER Working Papers.
    RePEc:nbr:nberwo:20010.

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  18. Variable Selection in Predictive MIDAS Models. (2014). Marsilli, Clément.
    In: Working papers.
    RePEc:bfr:banfra:520.

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  19. The Data Revolution and Economic Analysis. (2013). Levin, Jonathan ; Einav, Liran.
    In: Discussion Papers.
    RePEc:sip:dpaper:12-017.

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  20. The Data Revolution and Economic Analysis. (2013). Levin, Jonathan ; Einav, Liran.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:19035.

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  21. The Data Revolution and Economic Analysis. (2013). Levin, Jonathan ; Einav, Liran.
    In: NBER Chapters.
    RePEc:nbr:nberch:12942.

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References

References cited by this document

  1. Chris K. Carter and Robert Kohn. On Gibbs sampling for state space models. Biometrika, 81(3):541–553, 1994.
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  2. Concha Arola and Enrique Galan. Tracking the future on the web: Construction of leading indicators using internet searches. Technical report, Bank of Spain, 2012. URL http://www.bde.es/webbde/SES/Secciones/Publicaciones/ PublicacionesSeriadas/DocumentosOcasionales/12/Fich/do1203e.pdf.

  3. Jennifer L. Castle, Nicholas W. P. Fawcett, and David F. Hendry. Evaluating automatic model selection. Technical Report 474, Department of Economics, University of Oxford, 2010. URL http://economics.ouls.ox.ac.uk/14734/1/paper474.pdf. Hugh Chipman, Edward I. George, Robert E. McCulloch, Merlise Clyde, Dean P. Foster, and Rober A. Stine. The practical implementation of Bayesian model selection. Lecture

  4. Jennifer L. Castle, Xiaochuan Qin, and W. Robert Reed. How to pick the best regression equation: A review and comparison of model selection algorithms. Technical Report 13/2009, Department of Economics, University of Canterbury, 2009. URL http://www. econ.canterbury.ac.nz/RePEc/cbt/econwp/0913.pdf.

  5. Yan Carri` ere-Swallow and Felipe Labb e. Nowcasting with google trends in an emerging market. Journal of Forecasting, 2011. doi: 10.1002/for.1252. URL http://ideas.repec. org/p/chb/bcchwp/588.html. Working Papers Central Bank of Chile 588.
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Cocites

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    In: Questioni di Economia e Finanza (Occasional Papers).
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  2. Bank deposits and Google searches in a crisis economy: Bayesian non?linear evidence for Greece (2009–2015). (2021). Tsionas, Efthymios G ; Michaelides, Panayotis G ; Paraskeuopoulou, Despoina ; Konstantakis, Konstantinos N.
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  3. Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg. (2021). Fantazzini, Dean ; Pushchelenko, Julia ; Kurbatskii, Alexey ; Mironenkov, Alexey.
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  4. AN OVERVIEW OF DYNAMIC MODEL AVERAGING TECHNIQUES IN TIME?SERIES ECONOMETRICS. (2021). Nonejad, Nima.
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  5. The Proper Use of Google Trends in Forecasting Models. (2021). Pires, Henrique F ; Medeiros, Marcelo C.
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  6. Comparing time series characteristics of official and web job vacancy data. (2020). Colombo, Emilio ; Mezzanzanica, Mario ; Lovaglio, Pietro Giorgio.
    In: Quality & Quantity: International Journal of Methodology.
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  7. Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence. (2020). Bulut, Mehmet ; Badshah, Waqar.
    In: Economies.
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  9. Mixed-frequency models for tracking short-term economic developments in Switzerland. (2017). Scheufele, Rolf ; Hepenstrick, Christian ; Galli, Alain ; Alain, Rolf Scheufele .
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  10. Observational and Reinforcement Pattern-learning: An Exploratory Study. (2017). Pezanis-Christou, Paul ; Kirman, Alan ; Hanaki, Nobuyuki.
    In: GREDEG Working Papers.
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  11. The predictive power of Google searches in forecasting US unemployment. (2017). D'Amuri, Francesco ; Damuri, Francesco ; Marcucci, Juri .
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  12. Counter intuitive learning: An exploratory study. (2016). Pezanis-Christou, Paul ; Kirman, Alan ; Hanaki, Nobuyuki.
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  14. Blockchain in financial services: Regulatory landscape and future challenges. (2016). Pacce, Matias ; Camacho, Maximo.
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  15. Counter Intuitive Learning: An Exploratory Study. (2016). Pezanis-Christou, Paul ; Kirman, Alan ; Hanaki, Nobuyuki.
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  16. Bayesian Variable Selection for Nowcasting Economic Time Series. (2015). Varian, Hal ; Scott, Steven L..
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  17. Detecting Location Shifts during Model Selection by Step-Indicator Saturation. (2015). Pretis, Felix ; Hendry, David ; Doornik, Jurgen ; Castle, Jennifer.
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  18. Un-Networking: The Evolution of Networks in the Federal Funds Market. (2015). Klee, Elizabeth ; Bolotnyy, Valentin ; Beltran, Daniel.
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  19. Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?. (2015). López-Menéndez, Ana ; Vicente, Mara Rosala ; Prez, Rigoberto ; Lpez-Menndez, Ana J.
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  21. Big Data: New Tricks for Econometrics. (2014). Varian, Hal R..
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  22. Bayesian Variable Selection for Nowcasting Economic Time Series. (2013). Varian, Hal ; Scott, Steven L..
    In: NBER Working Papers.
    RePEc:nbr:nberwo:19567.

    Full description at Econpapers || Download paper

  23. Potential of Search Data in Assessment of Current Economic Conditions. (2013). Shiraki, Noriyuki ; Matsumoto, Azusa ; Matsumura, Kohei .
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  24. Panel Data Evidence on the Role of Institutions and Shocks for Unemployment Dynamics and Equilibrium. (2012). Nymoen, Ragnar ; Sparrman, Victoria.
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