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Abstract This paper investigates the dynamic interactions of the cross‐section distribution of sectoral price changes and the output growth in the Chinese economy. We compare in depth the results of Granger causality tests, Impulse Response, and Forecast Error Variance Decompositions from Mixed Sampling Frequency Vector Autoregression (MFVAR) with those from common frequency vector autoregression (VAR). It shows that potential causalities for inflation, relative price variability, relative price skewness, and output growth can be successfully detected by the MFVAR. The cross‐section distribution of sectoral price changes stands to be a fundamental determinant of fluctuations in the aggregate economy, not only in the short run but also in the long run. Moreover, the empirical results are robust to the identification restrictions imposed as well as to alternative measures for model variables. Our findings are in line with the predictions of a standard sticky‐price model, and thus pricing frictions are important factors behind the short‐run nonneutrality of nominal shocks. We highlight the primacy of the information contained in the higher‐order moments of cross‐section distribution of sectoral price changes. We propose that policy authorities should make proper use of all of the valuable information available, particularly those embodied in the distribution of sectoral prices.