Monetary policy inertia reconsidered: Evidence from endogenous interest rate trajectory

By finding small forecastable variability of future policy rates by highly smoothed central bank's endogenous interest rate trajectory, I suggest that market's failure to predict large portion of variability in future rates does not always imply limited policy inertia.     

需求预测模型的应用研究

需求预测是供应链管理中重要的环节,可帮助企业提高客户满意度。文中通过对需求预测模型的研究,阐述了定量预测法的适用范围,结合历史销售数据和预测模型模拟验证,为企业选择适合的预测模型提供方法。     

Improving forecasting by estimating time series structural components across multiple frequencies

Identifying the most appropriate time series model to achieve a good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy. Multiple time series are constructed from the original time series, using temporal aggregation. These derivative series highlight different aspects of the original data, as temporal aggregation helps in strengthening or attenuating the signals of different time series components. In each series, the appropriate exponential smoothing method is fitted and its respective time series components are forecast. Subsequently, the time series components from each aggregation level are combined, then used to construct the final forecast. This approach achieves a better estimation of the different time series components, through temporal aggregation, and reduces the importance of model selection through forecast combination. An empirical evaluation of the proposed framework demonstrates significant improvements in forecasting accuracy, especially for long-term forecasts.     

Income smoothing from a Census X-12 perspective

Corporate income smoothing has been the focus of much attention, yet relatively little is known about the key characteristics of income-smoothing firms. To address this issue, the current study uses quarterly data with Census X-12 analysis in a novel way to identify firms where the degree of random variability in earnings is less than the degree of random variability in sales (EVAR < SVAR). Prior research views such firms as effective smoothers, since most firms have scale-free variability profiles in the opposite direction (EVAR > SVAR). Large-sample US results identify these exceptions throughout a broad cross section of firms, but smaller and less profitable firms tended to have a higher incidence rate. Results also indicate that effective smoothers exhibited higher earnings persistence.     

Bank Managers' Heterogeneous Decisions on Discretionary Loan Loss Provisions

This study examines bank managers' three major motivations for discretionary behavior with respect to loan loss provisions: signaling, income smoothing, and capital management. To do so, it utilizes a bank-specific time-series regression approach that captures heterogeneity in the banks' priorities and strategies for alternative motives and compares the results to those from alternative model specifications. The statistical tests and results presented in this study lead to three conclusions. First, significant results for the income smoothing hypothesis are robust to the various model specifications. Second, average signaling coefficients estimated from bank-specific regressions are systematically larger than corresponding coefficients from pooled time-series cross-sectional regressions and are statistically significant. Finally, bank managers appear to use loan loss provisions to manage their regulatory capital levels by comparing them with the minimum ratios specified by regulators rather than with a time-series bank-specific ratio or pooled time-series cross-sectional mean ratio.     

Monetary Policy with Uncertain Parameters

This paper shows that—in contrast to the received wisdom—uncertainty about the parameters in a dynamic macroeconomic model may lead to more aggressive monetary policy. In particular, when there is uncertainty about the persistence of inflation, it may be optimal for the central bank to respond to shocks more aggressively in order to reduce uncertainty about the future development of inflation. Uncertainty about other parameters, on the other hand, dampens the policy response.
JEL classification : E 43; E 52     

Nonparametric estimation of distribution functions

Summary Using Silverman and Young’s (1987) idea of rescaling a rescaled smoothed empirical distribution function is defined and investigated when the smoothing parameter depends on the data. The rescaled smoothed estimator is shown to be often better than the commonly used ordinary smoothed estimator.     

Semi-parametric discrete choice measures of willingness to pay

A semi-parametric discrete choice method is proposed to recover welfare measures from individual choice data. The proposed method is compared with the traditional binary choice models in an application to measure benefits of recreation trips.     

中国改革开放以来财政政策平滑经济波动的能力——基于传统IS-LM模型的实证分析

文章通过借助传统IS-LM模型和比较静态分析方法对中国改革开放以来财政政策平滑经济波动的能力进行了实证分析,得出:(1)财政政策对经济波动的整体平滑能力较低,平均只有6.35%,财政政策的平滑能力在经济波动的不同状态之间存在明显差异;(2)财政政策工具之间的平滑能力存在较大差异,其中财政购买性支出能平滑掉经济初始冲击的11.48%,而财政转移性支出和收入政策整体上反而使经济波动上升近3%;(3)内生性检验表明,中国只有相机性支出政策与经济波动之间存在显著的双向因果关系,不仅如此,相机性支出政策的内外时滞都较短。文章结论的政策含义是,提高中国财政政策稳定效果的关键在于提高政策工具的有效性与相互之间的协同效应,不仅如此,对经济高涨时期政府财政行为进行有效约束也至关重要。     

Modified efficient importance sampling for partially non‐Gaussian state space models

The construction of an importance density for partially non‐Gaussian state space models is crucial when simulation methods are used for likelihood evaluation, signal extraction, and forecasting. The method of efficient importance sampling is successful in this respect, but we show that it can be implemented in a computationally more efficient manner using standard Kalman filter and smoothing methods. Efficient importance sampling is generally applicable for a wide range of models, but it is typically a custom‐built procedure. For the class of partially non‐Gaussian state space models, we present a general method for efficient importance sampling. Our novel method makes the efficient importance sampling methodology more accessible because it does not require the computation of a (possibly) complicated density kernel that needs to be tracked for each time period. The new method is illustrated for a stochastic volatility model with a Student's t distribution.