Political forecast cycles

A moral hazard model is used to show why overly optimistic revenue forecasts prior to elections can be optimal: Opportunistic governments can increase spending and appear more competent; ex post deficits emerge in election years, thereby producing political forecast cycles – as also found for US states in the empirical literature. Additionally, we obtain three theoretical results which are tested with panel data for Portuguese municipalities. The extent of manipulations is reduced when (i) the winning margin is expected to widen; (ii) the incumbent is not re-running; and/or (iii) the share of informed voters (proxied by education) goes up.     

Why We Should Change Our Attitude Towards Distressed Firms

This article surveys ‘creditor‐friendly’ and ‘enterprise‐friendly’ bankruptcy regimes with a focus on the methodology underlying the filter test in distress, as reflected both in its academic treatment and in legal practice. I find that the test exhibits pro‐liquidation bias in designating liquidation of a firm with recovery potential as the Type II error, and in underplaying the benefits of a possible turnaround. Further influences militating against continuation include the power conferred on creditors through the balance sheet criterion and the undervaluation of intangible assets. I make the case for reversing such biases to establish a presumption in favour of continuation.     

Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.     

Combining the minimum-variance and equally-weighted portfolios: Can portfolio performance be improved?

It is documented in the literature that due to estimation errors, mean-variance efficient portfolios deliver no higher out-of-sample Sharpe ratios than does the naïve equally-weighted portfolio (EWP). This paper demonstrates how the out-of-sample performance of the minimum-variance portfolio (MVP) can be improved in the presence of estimation errors by combining the MVP and EWP. Our results indicate that an appropriate combination of the MVP and EWP can enhance Sharpe ratios under any scenarios considered, and can also reduce the portfolio risk if short-selling is allowed. However, the combination strategy is not able to generate a lower risk level than the MVP when a short-selling restriction is imposed. We find that the optimal combination coefficient depends on the factors that greatly impact estimation errors in the MVP, including sample size, estimation method, no-short-selling restriction, and length of the out-of-sample period under consideration.     

Misclassification errors in labor force statuses and the early identification of economic recessions

Accurate identification of economic recessions in a timely fashion is a major macroeconomic challenge. The so-called Sahm recession indicator (Sahm, 2019), one of the most reliable early detectors of the U.S. recessions, relies on changes in unemployment rates, and is thus subject to misclassification errors in labor force statuses based on survey data. We propose a novel misclassification-errors correction to improve the predictive timeliness and provide a proper threshold value. Using historical data, we show that the adjusted unemployment-based recession indicator offers earlier identification of economic recessions.     

Imperfect Accounting and Reporting Bias

Errors and bias are both inherent features of accounting. In theory, while errors discourage bias by lowering the value relevance of accounting, they can also facilitate bias by providing camouflage. Consistent with theory, we find a hump‐shaped relation between a firm's propensity to engage in intentional misstatement and the prevalence of unintentional misstatements in the firm's industry for the whole economy and a majority of the industries. The result is robust to using firms’ number of items in financial statements and exposure to complex accounting rules as alternative proxies for errors and to using the restatement amount in net income to quantify the magnitude of bias and errors. To directly test for the two effects of errors, we show that when errors are more prevalent, the market reacts less to firms’ earnings surprises and bias is more difficult to detect. Our results highlight the imperfectness of accounting, advance understanding of firms’ reporting incentives, and shed light on accounting standard setting.     

Statistical inference on restricted partial linear regression models with partial distortion measurement errors

We consider the estimation and hypothesis testing problems for the partial linear regression models when some variables are distorted with errors by some unknown functions of commonly observable confounding variable. The proposed estimation procedure is designed to accommodate undistorted as well as distorted variables. To test a hypothesis on the parametric components, a restricted least squares estimator is proposed under the null hypothesis. Asymptotic properties for the estimators are established. A test statistic based on the difference between the residual sums of squares under the null and alternative hypotheses is proposed, and we also obtain the asymptotic properties of the test statistic. A wild bootstrap procedure is proposed to calculate critical values. Simulation studies are conducted to demonstrate the performance of the proposed procedure, and a real example is analyzed for an illustration.     

基于微分演化算法的DCT脆弱水印优化

基于离散余弦变换的水印嵌入技术存在变换过程中浮点数-整数转换而引入的舍入误差问题。文章采用微分演化算法修正了这类舍入误差,提高了基于离散余弦变换的脆弱数字水印的性能。同时,对该算法进行了介绍和描述,比较了它的性能。最后,就提取出来的水印的标准互相关相似度、加入水印后的影像的峰值信噪比,以及算法结果的质量和收敛率作了详细的比较。     

数字示波器中插值算法误差的分析

本文主要介绍了插值算法在数字存储示波器中的应用,通过编程和仿真分析了利用正弦插值对几种不同波形的恢复结果,计算和比较了相关的均方误差。     

Earnings disaggregation and analysts＇ forecasts

Accounting concepts dictate that separately disclosed components should contain separate useful information. This paper examines the relations between income statement components and analysts＇ earnings forecasts and forecast errors. Regressions explaining earnings forecasts using earnings components provide a better fit than regression using just aggregate income to explain forecasts. We interpret this as consistent with the hypothesis that analysts use incremental information in components not available in aggregate income. However, additional tests based on predictability of forecast errors indicate that analysts do not incorporate all information available in components into earnings forecasts. In addition, this inefficiency appears to increase at longer forecast horizons.