GMM estimation of a maximum entropy distribution with interval data

We develop a generalized method of moments (GMM) estimator for the distribution of a variable where summary statistics are available only for intervals of the random variable. Without individual data, one cannot calculate the weighting matrix for the GMM estimator. Instead, we propose a simulated weighting matrix based on a first-step consistent estimate. When the functional form of the underlying distribution is unknown, we estimate it using a simple yet flexible maximum entropy density. Our Monte Carlo simulations show that the proposed maximum entropy density is able to approximate various distributions extremely well. The two-step GMM estimator with a simulated weighting matrix improves the efficiency of the one-step GMM considerably. We use this method to estimate the U.S. income distribution and compare these results with those based on the underlying raw income data.     

Analyzing the effect of derivatives on the financial soundness of commercial banks in Italy: An approach based on the CAMELS framework

Italian economy is among the biggest economies in the Europe which suffered from the repercussions of the global financial crisis during this last decade. The weakness of Italian banking system coincides with the common debate about the implication of derivatives in the distress of banks’ soundness. Thus, the aim of our research is to examine the effect of derivative instruments on the banks’ soundness in Italy. To reach our goal, the CAMELS approach is employed to define the soundness of Italian commercial banks. To overcome the endogeneity issue of variables, an appropriate econometric procedure, namely the dynamic Generalized Method of Moments (GMM system) is applied using data from 22 commercial banks in Italy over the period 2005–2015. Explanatory variables are defined by derivative instruments (forwards, swaps, options, and futures), bank‐specific variable (bank's size as non‐CAMELS variable), industry‐specific variables (CR3, CR5, and HHI as indicators of bank's sector and market concentrations), and country‐specific variables (GDP and inflation). The main results reveal that the majority of the CAMELS indicators are favorably affected by derivative instruments especially forwards and options. The most important conclusion is that using derivative instruments does not threaten the financial soundness of commercial banks in Italy. As major implication decision‐makers and experts—after the global financial crisis—should not consider derivatives in part as responsible of the fragility of the Italian banking system.     

The long-run Phillips curve and non-stationary inflation

Modern theories of inflation incorporate a vertical long-run Phillips curve and are usually estimated using techniques that ignore the non-stationary behaviour of inflation. Consequently, the estimates obtained are imprecise and unable to test the veracity of a vertical long-run Phillips curve. We estimate a Phillips curve model taking into account the non-stationary properties in inflation and identify a small but significant positive relationship between inflation and unemployment. The results also provide some evidence that the trade-off between inflation and the rate of unemployment in the short-run worsens as the mean rate of inflation increases.     

Testing the null hypothesis of no regime switching with an application to GDP growth rates

This paper presents tests for the null hypothesis of no regime switching in Hamilton’s (Econometrica 57:357–384, 1989) regime switching model. The test procedures exploit similarities between regime switching models, autoregressions with measurement errors, and finite mixture models. The proposed tests are computationally simple and, contrary to likelihood based tests, have a standard distribution under the null. When the methodology is applied to US GDP growth rates, no strong evidence of regime switching is found. I thank Don Andrews, Peter Phillips, Yuichi Kitamura, Anat Bracha, Patrik Guggenberger, Orit Whiteman and three anonymous referees for useful comments and suggestions.     

中国工业部门要素分配份额决定因素研究

本文讨论我国工业部门要素分配份额的决定因素问题。在标准的新古典要素分配份额模型中,本文引入Dixit-Stiglitz垄断竞争以及企业目标函数的差异,建立了要素分配份额的决定模型。根据这个理论模型,本文建立了中国工业部门要素分配份额的计量模型,并利用系统GMM方法进行估计。回归结果表明,垄断能力越高,资本收入份额越高,国有和非国有企业的资本收入份额存在明显差异,国有企业的资本收入份额明显低于非国有企业,各类经济性质企业的资本收入份额从高到低依次为外商投资企业、港澳台企业、法人投资企业、集体企业、私有企业和国有企业。传统新古典分配模型考虑的技术因素,包括要素投入比的变化和技术进步,对要素分配份额的变化没有显著影响,表明我国工业部门要素替代弹性为1,因而劳动与资本相对价格的变化对要素分配份额没有显著影响。我们的结论是,工业部门要素分配份额变化的主要原因是产品市场垄断增加和国有部门改制引起的劳动力市场环境改变。     

随机折现因子方法与CAPM关于风险溢价的实证比较

本文根据随机折现因子方法的基本理论，结合广义矩阵法和蒙特卡罗模拟，对随机折现因子方法和传统的CAPM对风险溢价的计算进行实证比较研究。实证结果表明，对于中小样本，随机折现因子方法比传统的CAPM方法优越。估计量较为精确，误差小；对于大容量样本，这两种方法性能接近。另外，随机折现因子方法得到Jensen＇sα均值比CAPM方法得到Jensen＇sα均值小，而且标准偏差明显较小，也从另一角度说明了随机折现因子方法的优越性。     

Estimation methods for stochastic volatility models: a survey

Abstract.  Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been introduced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.     

开放经济下中国新凯恩斯混合菲利普斯曲线

本文采用广义矩方法估计开放经济下中国新凯恩斯混合菲利普斯曲线，结合中国现实选择工具变量。结果表明，使用混合成本作为通货膨胀的驱动因素在统计和经济意义上具有显著性，影响我国通货膨胀主要是资本成本与进口中间品成本；厂商定价行为既有前瞻性又有后顾性，但前瞻性处于主导地位，且价格具有较强的灵活性。     

Convenient estimators for the panel probit model: Further results

Bertschek and Lechner (1998) propose several variants of a GMM estimator based on the period specific regression functions for the panel probit model. The analysis is motivated by the complexity of maximum likelihood estimation and the possibly excessive amount of time involved in maximum simulated likelihood estimation. But, for applications of the size considered in their study, full likelihood estimation is actually straightforward, and resort to GMM estimation for convenience is unnecessary. In this note, we reconsider maximum likelihood based estimation of their panel probit model then examine some extensions which can exploit the heterogeneity contained in their panel data set. Empirical results are obtained using the data set employed in the earlier study. Helpful comments and suggestions by Irene Bertschek and Michael Lechner are gratefully acknowledged. This paper has also benefited from comments by two anonymous referees and from seminar participants at the Center for Health Economics at the University of York. Any remaining errors are the responsibility of the author.     

Monetary and Fiscal Policy Interactions in the Euro Area

The problem of monetary and fiscal policy interactions is an important issue for the euro area, since the individual member states of the EMU are responsible for their fiscal policies but monetary policy is pursued by a single monetary authority, the ECB. This paper is concerned with empirical evidence on monetary and fiscal policy interactions in the euro area. We first explore fiscal regimes with a VAR model and find empirical evidence that a non-Ricardian fiscal policy has been pursued in both France and Germany. As an example, we then study how one member state of the EMU, namely, Italy, is responding to the common monetary policy with its fiscal policy and find that Italian fiscal policy seemed to be counteractive to the common monetary policy between 1979 and 1998. In order to study monetary and fiscal policy interactions in a more general way, we explore time-varying interactions by estimating a State-Space model with Markov-switching for some Euro-area countries. There appear to be some regime changes in monetary and fiscal policy interactions in France and Germany, but the interactions between the two policies are not strong. Moreover, the two policies have not been accommodative but counteractive to each other. Finally we explore forward-looking behavior in policy interactions and find that expectations do not seem to have played an important role in the policy designs.