Karol Szafranek

Nominal rigidities play a central role in monetary policy transmission, shaping how inflation and real activity respond to various shocks. Using a large longitudinal dataset of granular price data for Poland covering 2000–2024, we contribute to the literature on price stickiness by documenting key features of price-setting behaviour in Poland. First, overall price stickiness in Poland is substantial, with the average (median) implied price duration of four (over six) months. Second, there is considerable heterogeneity and asymmetry present at the sectoral level. Third, the size of price changes is large, underscoring the importance of idiosyncratic shocks in price formation. Fourth, there is little evidence of downward nominal price rigidity outside the services sector, while there is strong evidence that price discounts play a significant role for price-setting. Finally, in recent years the frequency of price changes has increased broadly, indicating strengthened effectiveness of monetary policy in bringing inflation back to target.

With Covid‐19 and Russian invasion of Ukraine triggering vast uncertainty, non‐traditional (potentially big) data sources could provide timely information on the state of the economy. We investigate the usefulness of big data during these events in a bottom‐up nowcasting exercise for Polish food inflation. We show that information embedded in roughly 250 million web‐scraped prices makes it possible to closely track official inflation and provides the most accurate nowcasts in times of elevated uncertainty and volatility, while competing model‐based frameworks skew nowcasts towards historical patterns, which leads to much lower accuracy. In turn, during normal times, time series models with web‐scraped prices provide slightly more accurate nowcasts but the difference in predictive quality is negligible in comparison to nowcasts based purely on online prices. We also extensively experiment on how to optimally aggregate daily online prices. For practitioners we formulate four guidelines. First, let big data speak, especially during uncertain times. Second, model‐based frameworks with online prices are not necessary to obtain precise nowcasts of price developments, even on long samples. Third, simplest aggregation methods of individual product prices lead to the most accurate nowcasts. Fourth, for nowcasting quality the gain from observing prices daily is marginal compared to weekly frequency of data collection.

We explore the effects of fiscal policy shocks on aggregate output and inflation. We use the novel Bayesian econometric methodology of Baumeister and Hamilton applied to the fiscal structural vector autoregressive model to evaluate key elasticities and fiscal multipliers using U.S. data from 1947Q1 to 2023Q4. In our baseline specification, the government spending multiplier is equal to approximately 1.1 and tax multiplier is approximately -1.4 after one year. The short-term output elasticity of government spending is slightly negative and the output elasticity of taxes is approximately equal to 2.6.

The Russian invasion of Ukraine triggered severe disruptions in the European energy markets and caused significant shifts in global natural gas flows. In this paper we investigate to what extent this European shock has affected the dynamics and altered the estimates of the elasticities on the U.S. natural gas market. For that purpose, we use the Bayesian Structural Vector Autoregression framework proposed by Baumeister and Hamilton (2019, BH) for the crude oil market and applied by Rubaszek, Uddin, and Szafranek (2021, RSU) to analyze the dynamics of U.S. natural gas market till year 2020. We modify the RSU model to account for natural gas trade and next derive the posterior of the model using observations till 2023. This allows us to approximate the impact of the European energy crisis on the U.S. market. Our result are twofold. First, we show that due to our modification the RSU model the estimates of the elasticities on the U.S. natural gas market change, while simply updating the same prior beliefs with most recent data impacts the posterior estimates to a very limited extent. Second, we find that even as major shock as the European energy crisis has only marginally contributed to the dynamics of the U.S. natural gas market. This result confirms earlier studies, which show that the U.S. natural gas market is barely affected by shocks to the European natural gas market.

We investigate the relationship between the three most popular uncertainty measures with the means of the state-of-the-art connectedness frameworks applied to the time-varying parameters vector autoregression model with stochastic volatility. We find marked increases in uncertainty connectedness during major economic turmoil and hostile events. VIX turns out to be the most forward-looking uncertainty measure that persistentlytransmits shocks to the remaining uncertainty proxies at lower frequencies. In turn, GPR, approximating specific information related to geopolitical risk, transmits shocks to other measures at short-term frequencies, while the EPU index is largely replicating unanticipated movements in the VIX or GPR. We also present implications of these findings for economic modelling.

We quantify intraday volatility connectedness between oil and key financial assets and assess how it is related to uncertainty and sentiment measures. For that purpose, we integrate the well-known spillover methodology with a TVP VAR model estimated on a unique, vast dataset of roughly 300 thousand 5 minute quotations for crude oil, the US dollar, S&P 500 index, gold and US treasury prices. This distinguishes our investigation from previous studies, which usually employ relatively short samples of daily or weekly data and focus on connectedness between two asset classes. We contribute to the literature across three margins. First, we document that market connectedness at intraday frequency presents new picture on markets co-movement compared to the estimates obtained using daily data. Second, we show that at 5 minute frequency volatility is mostly transmitted from the stock market and absorbed by the bond and dollar markets, with oil and gold markets being occasionally important for volatility transmission. Third, we present evidence that daily averages of intraday connectedness measures respond to changes in sentiment and market-specific uncertainty. Interestingly, our results contrast with earlier findings, as they show that connectedness among markets decreases in periods of high volatility owing to market-specific factors. Our study points to the importance of using high-frequency data in order to better understand market dynamics.