A small-scale New-Keynesian dynamic stochastic general equilibrium model is estimated by maximum likelihood method using quarterly data of China. Model specifications and parameter equalities between various competing model variants are addressed by formal statistical hypothesis tests, while implications for business cycle fluctuations are evaluated via a variance decomposition experiment, second-moments matching, and some out-of-sample forecast exercises. It is highlighted that both forward and backward components are important for the dynamics of output, inflation and real balances. The monetary authority will take a sufficient aggressive stance, with a significant lagged response, to the current inflation pressure, while leaving less attention to changes in aggregate output. Variance decomposition reveals that large percentages of variations in real and nominal variables are explained by the highly volatile preference shock and potential output shock, respectively. When nominal and real frictions as well as additional shocks are included, our estimated model overall can successfully reproduce the stylized facts of business cycles in the actual data of China and even frequently outperform those forecasts from an unconstrained VAR.
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.
This paper examines the relationship between inflation and relative price variability (RPV) by using provincial level data from China. The data contains three different inflation regimes and evidence of smooth transition is statistically prominent and the province‐specific time‐varying marginal impact of inflation on RPV varies substantially across inflation regimes. The inflation–RPV linkage is stronger when inflation is moderately high, but tends to fade out and might eventually disappear when inflation is steadily low. The policy implication of this result is that inflation targets should be set within a range in which the inflation–RPV link is the weakest (i.e. low‐inflation regime) to minimize undesirable price dispersion induced by inflation.
In this paper, we empirically investigate the dynamic effects of neutral & investment-specific technology shocks on labor employment in China. Under the assumption that investment-specific technology shock is the unique source of secular trend in relative price of investment goods, we find that labor employment increases (decreases) in response to a positive investment-specific (neutral) shock in an identified SVAR with long-run restriction. We argue that empirical results obtained in one-neutral-shock model do misrepresent the important short-run dynamics of employment to different technology shocks in the actual economy
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