财政赤字货币化过程中铸币税收益问题分析

铸币税即政府发行货币所取得的收入 ,一般可理解为货币发行成本和货币面值间的差额。在通货膨胀情况下 ,货币的实际购买力下降 ,政府的实际铸币税收入小于其名义铸币税收入。铸币税问题的讨论一般是同财政赤字货币化问题相联系的。我国当前的积极财政政策是以增发国债为核心 ,财政赤字经常化。政府可以从财政赤字货币化中得到多少铸币税收入受制于三个因素 :经济增长对基础货币的需求 ,经济的实际增长率 ,以及货币需求对通货膨胀和收入的弹性     

Pseudo–Modelling of Outliers for Estimation of Regression And Time Series Models

This paper documents situations where the variance inflation model for outliers has undesirable properties. The model is commonly used to accommodate outliers in a Bayesian analysis of regression and time series models. The alternative approach provided here does not suffer from these undesirable properties but gives inferences similar to those of the variance inflation model when this is appropriate. It can be used with regression, time series, and regression with correlated errors in a unified way, and adheres to the scientific principle that inference should be based on the data after obvious outliers have been discarded. Only one parameter is required for outliers; it is interpretable as the a priori willingness to remove observations from the analysis.     

五代十国货币史初探

五代十国时期,由于铜钱缺乏,战争频繁,政治腐败,开支浩大,各国统治者为了挽救财政危机,往往铸行虚值大钱、铁钱以及铅锡等劣币,从而引起了通货膨胀、钱币贬值、物价飞涨、人民受损,这可作为今人的历史教训。     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects

Focusing on the model selection problems in the family of Poisson mixture models (including the Poisson mixture regression model with random effects and zero‐inflated Poisson regression model with random effects), the current paper derives two conditional Akaike information criteria. The criteria are the unbiased estimators of the conditional Akaike information based on the conditional log‐likelihood and the conditional Akaike information based on the joint log‐likelihood, respectively. The derivation is free from the specific parametric assumptions about the conditional mean of the true data‐generating model and applies to different types of estimation methods. Additionally, the derivation is not based on the asymptotic argument. Simulations show that the proposed criteria have promising estimation accuracy. In addition, it is found that the criterion based on the conditional log‐likelihood demonstrates good model selection performance under different scenarios. Two sets of real data are used to illustrate the proposed method.     

Machine Learning Vasicek Model Calibration with Gaussian Processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.     

POWER FUNCTION FOR INVERSE GAUSSIAN REGRESSION MODELS

Inverse Gaussian regression models are useful for data where both the independent and dependent variable are nonnegative and the variance of the dependent variable depends on the independent variable. Zero intercept inverse Gaussian regression models are presented with nonconstant variance, constant ratio of variance to the mean and constant coefficient of variation. The power function for testing hypotheses about the slope is given for all of these models.     

A new infinitely divisible discrete distribution with applications to count data modeling

A new discrete distribution involving geometric and discrete Pareto as special cases is introduced. The distribution possesses many interesting properties like decreasing hazard rate, zero vertex uni-modality, over-dispersion, infinite divisibility and compound Poisson representation, which makes the proposed distribution well suited for count data modeling. Other issues including closure property under minima, comparison of its distribution tail with other distributions via actuarial indices are discussed. The method of proportion and maximum likelihood method are presented for parameter estimation. Finally the performance of the proposed distribution over other classical and newly proposed infinitely divisible distributions are discussed.     

Breaking the taboo: a history of monetary financing in Canada, 1930–1975

Monetary financing – the funding of state expenditure via the creation of new money rather than through taxation or borrowing – has become a taboo policy instrument in advanced economies. It is generally associated with dangerously high inflation and/or war. Relatedly, a key institutional feature of modern independent central banks is that they are not obligated to support government expenditure via money creation. Since the financial crisis of 2007–2008, however, unorthodox monetary policies, in particular quantitative easing, coupled with stagnant growth and high levels of public and private debt have led to questions over the monetary financing taboo. Debates on the topic have so far been mainly theoretical with little attention to the social and political dynamics of historical instances of monetary financing. This paper analyses one of the most significant twentieth‐century cases: Canada from the period after the Great Depression up until the monetarist revolution of the 1970s. The period was a successful one for the Canadian economy, with high growth and employment and manageable inflation. It offers some interesting insights into the relationship between states and central banks and present‐day discussions around the governance of money creation.     

Difference-based matrix perturbation method for semi-parametric regression with multicollinearity

This paper addresses the collinearity problems in semi-parametric linear models. Under the difference-based settings, we introduce a new diagnostic, the difference-based variance inflation factor (DVIF), for detecting the presence of multicollinearity in semi-parametric models. The DVIF is then used to device a difference-based matrix perturbation method for solving the problem. The electricities distribution data set is analyzed, and numerical evidences validate the effectiveness of the proposed method.