Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy.This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution int...Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy.This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution into a high-order approximate dynamic stochastic general equilibrium(DSGE)model with Epstein–Zin preference to better analyze the dynamic effect of uncertainty risk on macroeconomics.Then,the high-dimensional DSGE model(DSGE-SV-t)is developed to examine the impact of uncertainty risk on the transmission mechanism among macroeconomic sectors.The empirical research found that uncertainty risk generates heterogeneous impacts on macroeconomic dynamics under different inflation levels and economic states.Among them,a technological shock has the strongest impact on employment and consumption channels.The crowding-out effect of a fiscal policy stimulus on consumption and private investments is relatively weakened when considering uncertainty risk but is more pronounced during periods of high inflation.Uncertainty risk can partly explain the decline in investments and the increase in interest rates and employment rates,given the impact of an agent’s risk preferences.Compared with external economic conditions,the inflation factor has a stronger impact on the macro transmission mechanism caused by uncertainty risk.展开更多
The L<sub>1</sub> regression is a robust alternative to the least squares regression whenever there are outliers in the values of the response variable, or the errors follow a long-tailed distribution. To ...The L<sub>1</sub> regression is a robust alternative to the least squares regression whenever there are outliers in the values of the response variable, or the errors follow a long-tailed distribution. To calculate the standard errors of the L<sub>1</sub> estimators, construct confidence intervals and test hypotheses about the parameters of the model, or to calculate a robust coefficient of determination, it is necessary to have an estimate of a scale parameterτ. This parameter is such that τ<sup>2</sup>/n is the variance of the median of a sample of size n from the errors distribution. [1] proposed the use of , a consistent, and so, an asymptotically unbiased estimator of τ. However, this estimator is not stable in small samples, in the sense that it can increase with the introduction of new independent variables in the model. When the errors follow the Laplace distribution, the maximum likelihood estimator of τ, say , is the mean absolute error, that is, the mean of the absolute residuals. This estimator always decreases when new independent variables are added to the model. Our objective is to develop asymptotic properties of under several errors distributions analytically. We also performed a simulation study to compare the distributions of both estimators in small samples with the objective to establish conditions in which is a good alternative to for such situations.展开更多
This paper proposes a general systems theory for fractals visualising the emergence of successively larger scale fluctuations resulting from the space-time integration of enclosed smaller scale fluctuations. Global gr...This paper proposes a general systems theory for fractals visualising the emergence of successively larger scale fluctuations resulting from the space-time integration of enclosed smaller scale fluctuations. Global gridded time series data sets of monthly mean temperatures for the period 1880- 2007/2008 are analysed to show that data sets and corresponding power spectra exhibit distributions close to the model predicted inverse power law distribution. The model predicted and observed universal spectrum for interannual variability rules out linear secular trends in global monthly mean temperatures. Global warming results in intensification of fluctuations of all scales and manifested immediately in high frequency fluctuations.展开更多
基金supported by the National Natural Science Foundation of China(Nos.72141304,71790594,71901130)。
文摘Sudden and uncertain events often cause cross-contagion of risk among various sectors of the macroeconomy.This paper introduces the stochastic volatility shock that follows a thick-tailed Student’s t-distribution into a high-order approximate dynamic stochastic general equilibrium(DSGE)model with Epstein–Zin preference to better analyze the dynamic effect of uncertainty risk on macroeconomics.Then,the high-dimensional DSGE model(DSGE-SV-t)is developed to examine the impact of uncertainty risk on the transmission mechanism among macroeconomic sectors.The empirical research found that uncertainty risk generates heterogeneous impacts on macroeconomic dynamics under different inflation levels and economic states.Among them,a technological shock has the strongest impact on employment and consumption channels.The crowding-out effect of a fiscal policy stimulus on consumption and private investments is relatively weakened when considering uncertainty risk but is more pronounced during periods of high inflation.Uncertainty risk can partly explain the decline in investments and the increase in interest rates and employment rates,given the impact of an agent’s risk preferences.Compared with external economic conditions,the inflation factor has a stronger impact on the macro transmission mechanism caused by uncertainty risk.
文摘The L<sub>1</sub> regression is a robust alternative to the least squares regression whenever there are outliers in the values of the response variable, or the errors follow a long-tailed distribution. To calculate the standard errors of the L<sub>1</sub> estimators, construct confidence intervals and test hypotheses about the parameters of the model, or to calculate a robust coefficient of determination, it is necessary to have an estimate of a scale parameterτ. This parameter is such that τ<sup>2</sup>/n is the variance of the median of a sample of size n from the errors distribution. [1] proposed the use of , a consistent, and so, an asymptotically unbiased estimator of τ. However, this estimator is not stable in small samples, in the sense that it can increase with the introduction of new independent variables in the model. When the errors follow the Laplace distribution, the maximum likelihood estimator of τ, say , is the mean absolute error, that is, the mean of the absolute residuals. This estimator always decreases when new independent variables are added to the model. Our objective is to develop asymptotic properties of under several errors distributions analytically. We also performed a simulation study to compare the distributions of both estimators in small samples with the objective to establish conditions in which is a good alternative to for such situations.
文摘This paper proposes a general systems theory for fractals visualising the emergence of successively larger scale fluctuations resulting from the space-time integration of enclosed smaller scale fluctuations. Global gridded time series data sets of monthly mean temperatures for the period 1880- 2007/2008 are analysed to show that data sets and corresponding power spectra exhibit distributions close to the model predicted inverse power law distribution. The model predicted and observed universal spectrum for interannual variability rules out linear secular trends in global monthly mean temperatures. Global warming results in intensification of fluctuations of all scales and manifested immediately in high frequency fluctuations.
