A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales

We present our solution for the M5 Uncertainty competition. Our solution ranked sixth out of 909 submissions across all hierarchical levels and ranked first for prediction at the finest level of granularity (product-store sales, i.e. SKUs). The model combines a multi-stage state-space model and Monte Carlo simulations to generate the forecasting scenarios (trajectories). Observed sales are modelled with negative binomial distributions to represent discrete over-dispersed sales. Seasonal factors are handcrafted and modelled with linear coefficients that are calculated at the store-department level.     

我国城镇居民消费的风险分担与跨期平滑——基于1985~2011年的实证研究

应用我国1985~2011年的省级数据,构建面板数据模型实证分析了城镇家庭消费的风险分担和跨期平滑情况。研究结果显示：我国城镇家庭消费的跨期平滑是不完全的。无论是从我国整体情况来看,还是从不同地区或者不同收入组家庭来考察,城镇家庭消费的跨期平滑系数γ均介于0~1之间。此外,城镇家庭消费的风险分担程度很低,消费风险分担机制很不完善。进一步的分析显示：各个地区内部和不同收入组内部的风险分担系数要高于全国总体的风险分担系数,这说明我国城镇家庭在进行消费风险分担时具有显著的“本地偏好”和“阶层效应”。     

Are Revisions to USDA Crop Production Forecasts Smoothed?

This article uses Nordhaus' framework to determine the efficiency of the revision process for USDA corn and soybean production forecasts over the 1970/1971 through 2004/2005 marketing years. Positive autocorrelation and consistency of directional changes in forecast revisions suggest these forecasts are "smoothed." Evidence is provided that the loss in forecast accuracy due to smoothing is statistically and economically significant in several cases. A conservative bias in farm operators' assessments of yield potential and in the procedure for translating enumerator's information about plant fruit counts into objective yield estimates are identified as plausible sources of smoothing.     

Bayesian estimation of an extended local scale stochastic volatility model

A new version of the local scale model of Shephard (1994) is presented. Its features are identically distributed evolution equation disturbances, the incorporation of in-the-mean effects, and the incorporation of variance regressors. A Bayesian posterior simulator and a new simulation smoother are presented. The model is applied to publicly available daily exchange rate and asset return series, and is compared with t-GARCH and Lognormal stochastic volatility formulations using Bayes factors.     

Holt’s exponential smoothing and neural network models for forecasting interval-valued time series

Interval-valued time series are interval-valued data that are collected in a chronological sequence over time. This paper introduces three approaches to forecasting interval-valued time series. The first two approaches are based on multilayer perceptron (MLP) neural networks and Holt’s exponential smoothing methods, respectively. In Holt’s method for interval-valued time series, the smoothing parameters are estimated by using techniques for non-linear optimization problems with bound constraints. The third approach is based on a hybrid methodology that combines the MLP and Holt models. The practicality of the methods is demonstrated through simulation studies and applications using real interval-valued stock market time series.     

The Effect of Analyst Coverage on the Informativeness of Income Smoothing

This study examines whether analyst coverage affects the informativeness of income smoothing. I find that income smoothing enhances earnings informativeness more greatly for firms with high analyst coverage than for firms with low analyst coverage. The results suggest that income smoothing more efficiently communicates private information to investors when firms are followed by more analysts, consistent with the notion that analysts play an important information intermediary role in enhancing the informativeness of income smoothing.     

Exponential smoothing with a damped multiplicative trend

Multiplicative trend exponential smoothing has received very little attention in the literature. It involves modelling the local slope by smoothing successive ratios of the local level, and this leads to a forecast function that is the product of level and growth rate. By contrast, the popular Holt method uses an additive trend formulation. It has been argued that more real series have multiplicative trends than additive. However, even if this is true, it seems likely that the more conservative forecast function of the Holt method will be more robust when applied in an automated way to a large batch of series with different types of trend. In view of the improvements in accuracy seen in dampening the Holt method, in this paper we investigate a new damped multiplicative trend approach. An empirical study, using the monthly time series from the M3-Competition, gave encouraging results for the new approach at a range of forecast horizons, when compared to the established exponential smoothing methods.     

The Timing of Asset Sales: Evidence of Earnings Management?

This paper presents empirical evidence from a sample of publicly traded Singaporean firms on the question: to what extent do firms manage earnings through the timing of asset sales? Previous studies have focused on accounting motives behind asset sales, ignoring the need to also consider economic motives. Some empirical evidence is provided to support the hypothesis that managers of firms with decreasing net earnings–per–share smooth earnings upwards using asset sales.     

A simple estimator for smooth local projections

In this paper, we propose a simple approach to estimate impulse response function through smoothed local projections, thereby utilizing the flexibility of local projections, while creating smooth and economically plausible impulse response functions as provided by VARs. The approach allows to determine the appropriate degree of smoothing endogenously through a standard information criterion. This also avoids oversmoothing and provides an estimator that is generally more efficient than standard local projections.     

Non-parametric estimation of operational risk losses adjusted for under-reporting

Not all claims are reported when a database for financial operational risk is created. The probability of reporting increases with the size of the operational risk loss, and converges towards one for big losses. Losses in operational risk have different causes, and usually follow a wide variety of distributional shapes. Therefore, a method for modelling operational risk based on one or two parametric models is deemed to fail. In this paper, we introduce a semi-parametric method for modelling operational risk that is capable of taking under-reporting into account and being guided by prior knowledge of the distributional shape.