Pawel Skrzypczynski

National Bank of Poland

00-919 Warsaw

Poland

  • National Bank of Poland

SCHOLARLY PAPERS

5

DOWNLOADS

311

SSRN CITATIONS

2

CROSSREF CITATIONS

1

Scholarly Papers (5)

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1.

Can a Simple DSGE Model Outperform Professional Forecasters?

National Bank of Poland Working Paper No. 43
Number of pages: 26 Posted: 14 Apr 2008
Michał Rubaszek and Pawel Skrzypczynski
National Bank of Poland and National Bank of Poland
Downloads 113 (325,922)
Citation 1

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forecasting; real-time data; Survey of Professional Forecasters; DSGE; VAR

2.

Putting the New Keynesian DSGE Model to the Real-Time Forecasting Test

ECB Working Paper No. 1110
Number of pages: 33 Posted: 27 Nov 2009
Marcin Kolasa, Michał Rubaszek and Pawel Skrzypczynski
Warsaw School of Economics (SGH), National Bank of Poland and National Bank of Poland
Downloads 63 (461,053)
Citation 5

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Forecasting, DSGE, Bayesian VAR, SPF, Real-time data

3.

Can We Beat the Random Walk in Forecasting CEE Exchange Rates?

National bank of Poland Working Paper No. 127
Number of pages: 19 Posted: 19 Oct 2012 Last Revised: 08 Jul 2016
Jakub Muck and Pawel Skrzypczynski
National Bank of Poland - Department of Economics and National Bank of Poland
Downloads 53 (499,806)
Citation 1

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CEE currencies, exchange rate forecasting, random walk, VAR, BVAR

4.

Forecasting the Polish Zloty with Non-Linear Models

National Bank of Poland Working Paper No. 81
Number of pages: 29 Posted: 08 Apr 2011 Last Revised: 08 Jul 2016
Michał Rubaszek, Pawel Skrzypczynski and Grzegorz Koloch
National Bank of Poland, National Bank of Poland and National Bank of Poland
Downloads 44 (539,663)

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Exchange rate forecasting, Polish zloty, Markov-switching models, Artificial neural networks

5.

Are Business Cycles in the US and Emerging Economies Synchronized?

National Bank of Poland Working Paper No. 111
Number of pages: 27 Posted: 16 Apr 2012
Piotr Krupa and Pawel Skrzypczynski
affiliation not provided to SSRN and National Bank of Poland
Downloads 38 (569,529)

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business cycles synchronization, time series filtering, cross-spectral analysis, SVAR models