IDEAS home Printed from https://ideas.repec.org/f/pst751.html
   My authors  Follow this author

Damian Stelmasiak

Personal Details

First Name:Damian
Middle Name:
Last Name:Stelmasiak
Suffix:
RePEc Short-ID:pst751
[This author has chosen not to make the email address public]

Affiliation

Narodowy Bank Polski

Warszawa, Poland
http://www.nbp.pl/
RePEc:edi:nbpgvpl (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Paweł Macias & Damian Stelmasiak, 2019. "Food inflation nowcasting with web scraped data," NBP Working Papers 302, Narodowy Bank Polski.

Articles

  1. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
  2. Damian Stelmasiak & Grzegorz Szafrański, 2016. "Forecasting the Polish Inflation Using Bayesian VAR Models with Seasonality," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 21-42, March.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Paweł Macias & Damian Stelmasiak, 2019. "Food inflation nowcasting with web scraped data," NBP Working Papers 302, Narodowy Bank Polski.

    Cited by:

    1. Christian Beer & Fabio Rumler & Joel Tölgyes, 2021. "Prices and inflation in Austria during the COVID-19 crisis – an analysis based on online price data," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/20-Q1/, pages 65-75.
    2. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    3. Jennifer Peña & Elvira Prades, 2021. "Price setting in Chile: Micro evidence from consumer on-line prices during the social outbreak and Covid-19," Working Papers 2112, Banco de España.
    4. J. Peña & E. Prades, 2021. "Price setting in Chile: Micro evidence from consumer on-line prices during the social outbreak and Covid-19," Working Papers Central Bank of Chile 906, Central Bank of Chile.
    5. Mirko Ðukic, Iva Krsmanovic, Miodrag Petkovic & Mirko Ðukic & Iva Krsmanovic & Miodrag Petkovic, 2023. "Nowcasting inflation using prices from the web," Working Papers Bulletin 16, National Bank of Serbia.
    6. Ilaria Benedetti & Tiziana Laureti & Luigi Palumbo & Brandon M. Rose, 2022. "Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques," Economies, MDPI, vol. 10(4), pages 1-20, April.
    7. Solórzano Diego, 2023. "Stylized Facts From Prices at Multi-Channel Retailers in Mexico," Working Papers 2023-09, Banco de México.

Articles

  1. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.

    Cited by:

    1. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    2. Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023. "Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany," Discussion Papers 34/2023, Deutsche Bundesbank.
    3. Muñoz-Villamizar, Andrés & Piatti, Matias & Mejía-Argueta, Christopher & Pirabe, Luis Felipe & Namdar, Jafar & Gomez, Juan Felipe, 2024. "Navigating retail inflation in Brazil: A machine learning and web scraping approach to the basic food basket," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    4. Dedola, Luca & Ehrmann, Michael & Hoffmann, Peter & Lamo, Ana & Paz-Pardo, Gonzalo & Slacalek, Jiri & Strasser, Georg, 2023. "Digitalisation and the economy," Working Paper Series 2809, European Central Bank.
    5. Barış Soybilgen & M. Ege Yazgan & Hüseyin Kaya, 2023. "Nowcasting Turkish Food Inflation Using Daily Online Prices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 171-190, September.

  2. Damian Stelmasiak & Grzegorz Szafrański, 2016. "Forecasting the Polish Inflation Using Bayesian VAR Models with Seasonality," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 8(1), pages 21-42, March.

    Cited by:

    1. Anttonen, Jetro, 2018. "Nowcasting the Unemployment Rate in the EU with Seasonal BVAR and Google Search Data," ETLA Working Papers 62, The Research Institute of the Finnish Economy.
    2. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    3. Karol Szafranek, 2017. "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers 262, Narodowy Bank Polski.
    4. Dmytro Krukovets & Olesia Verchenko, 2019. "Short-Run Forecasting of Core Inflation in Ukraine: a Combined ARMA Approach," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 248, pages 11-20.
    5. Justyna Wróblewska & Anna Pajor, 2019. "One-period joint forecasts of Polish inflation, unemployment and interest rate using Bayesian VEC-MSF models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 11(1), pages 23-45, March.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 1 paper announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (1) 2019-04-08. Author is listed
  2. NEP-MAC: Macroeconomics (1) 2019-04-08. Author is listed

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Damian Stelmasiak should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.