Submitted by Diogo Saraiva (diogosaraivarj@gmail.com) on 2016-06-21T18:22:50Z No. of bitstreams: 1 Tese_Diogo_Saraiva.pdf: 1438960 bytes, checksum: 75bf0c000613e5b32c7480ba2c0a1f9b (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2016-06-30T14:47:59Z (GMT) No. of bitstreams: 1 Tese_Diogo_Saraiva.pdf: 1438960 bytes, checksum: 75bf0c000613e5b32c7480ba2c0a1f9b (MD5) / Approved for entry into archive by Maria Almeida (maria.socorro@fgv.br) on 2016-07-13T12:51:22Z (GMT) No. of bitstreams: 1 Tese_Diogo_Saraiva.pdf: 1438960 bytes, checksum: 75bf0c000613e5b32c7480ba2c0a1f9b (MD5) / Made available in DSpace on 2016-07-13T12:51:41Z (GMT). No. of bitstreams: 1 Tese_Diogo_Saraiva.pdf: 1438960 bytes, checksum: 75bf0c000613e5b32c7480ba2c0a1f9b (MD5) Previous issue date: 2015-11-27 / The knowledge of the current state of the economy is crucial for policy makers, economists and analysts. However, a key economic variable, the gross domestic product (GDP), are typically colected on a quartely basis and released with substancial delays by the national statistical agencies. The first aim of this paper is to use a dynamic factor model to forecast the current russian GDP, using a set of timely monthly information. This approach can cope with the typical data flow problems of non-synchronous releases, mixed frequency and the curse of dimensionality. Given that Russian economy is largely dependent on the commodity market, our second motivation relates to study the effects of innovations in the russian macroeconomic fundamentals on commodity price predictability. We identify these innovations through a news index which summarizes deviations of offical data releases from the expectations generated by the DFM and perform a forecasting exercise comparing the performance of different models.