Improving footprint calculations of small open economies: combining local with multi-regional input–output tables

In a small, open and resource-poor economy, import and export dependency have an ever-growing impact on local policy decisions, which makes local (environmental) policy-makers increasingly depend on global data. This increases the interest in models that link local production and consumption data to global production, trade and environmental data. The recent increase in availability of global environmentally extended multi-regional input-output tables (EE-MRIO tables) provides an opportunity to link them with existing local environmentally extended input-output tables (EE-RIO tables). These combined tables make it possible (1) to analyse the links between local and global production and consumption and (2) to study global value chains, material use and environmental impacts simultaneously. However, estimations using input-output (I–O) analyses contain errors due to imperfect databases. In this article the magnitude of specification, aggregation and time errors are estimated and compared. The results show the need to combine local datasets with multi-regional ones and show that highest detailed (country and sector levels) as well as time series of I–O tables are the way forward for using I–O analyses in local policy-making. The paper provides guidance on trading off investments in model adoption and/or extension and the reliability of estimation results.     

Engineering robust instruments for GMM estimation of panel data regression models with errors in variables: a note

Econometricians have long recognized the need to account in some way for measurement errors, specification errors and endogeneity to ensure that the ordinary least squares estimator is consistent. This article introduces a new generalized method of moments estimator that relies on robust instruments to estimate panel data regression models containing errors in variables. We show how this GMM approach can be generalized for the panel data framework using higher moments and cumulants as instruments. The new instruments, engineered for greater robustness, are proposed to tackle the pervasive problem of weak instruments.     

Productivity Dispersion and Measurement Error

Several reasons have been put forward to explain the high dispersion of productivity across establishments: quality of management, different input usage and market distortions, to name but a few. Although it is acknowledged that a sizable portion of productivity dispersion may also be due to measurement error, little research has been devoted to identifying how much they contribute. We outline a novel procedure for identifying the role of measurement error in explaining the empirical dispersion of productivity across establishments. The starting point of our framework is the errors-in-variable model consisting of a measurement equation and a structural equation for latent productivity. We estimate the variance of the measurement error and subsequently estimate the variance of the latent productivity variable, which is not contaminated by measurement error. Using Norwegian data on the manufacture of food products, we find that about one percent of the measured dispersion stems from measurement error.     

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.     

Capitalized intangibles and financial analysts

We examine whether firms that capitalize a higher proportion of their underlying intangible assets have higher analyst following, lower dispersion of analysts’ earnings forecasts and more accurate earnings forecasts relative to firms that capitalize a lower proportion. Under Australian generally accepted accounting principles, capitalization of intangible assets has become increasingly ‘routine’ since the late 1980s. It is predicted that this experience leads Australian analysts to expect firms with relatively more certain intangible investments to signal this fact by capitalizing intangible assets. Our results are consistent with this. We find that capitalization of intangible assets is associated with higher analyst following and lower absolute earnings forecast error for firms with a stock of underlying intangible assets. Our tests suggest a weaker association between capitalization and lower earnings forecast dispersion. We conclude that there are benefits for analysts, for management to have the option to capitalize intangible assets. These findings suggest that IAS 38 Intangible Assets and AASB 138 Intangible Assets reduce the usefulness of financial statements.     

Behavior in one-shot traveler’s dilemma games: model and experiments with advice

We report results of one-shot traveler’s dilemma game experiments to test the predictions of a model of introspection. The model describes a noisy out-of-equilibrium process by which players reach a decision of what to do in one-shot games. To test the robustness of the model and to compare it to other models of introspection without noise, we introduce non-binding advice. Advice has the effect of coordinating all players’ beliefs onto a common strategy. Experimentally, advice is implemented by asking subjects who participated in a repeated traveler’s dilemma game to recommend an action to subjects playing one-shot games with identical parameters. In contrast to observations, models based on best-response dynamics would predict lower claims than the advised. We show that our model’s predictions with and without advice are consistent with the data.      

质量流量计在原油集输系统中的应用

对油田企业分区块计量问题的现状进行了分析,指出了目前存在计量误差的主要原因,针对费希尔Ex8300质量流量计在使用中存在的缺陷,重新引进了新型CMF纯油质量流量计,报告了在多个原油集输站的试验情况,通过试验比对,找出了产生系统误差的根本原因,提出了在分队、分区块计量中质量流量计的应用是可行的,并告戒仪表管理人员对质量流量计的输入参数的动态变化要进行适时调整,以保证仪表的计量准确。     

Conditional Persistence of Earnings Components and Accounting Anomalies

We suggest that the failure of investors to distinguish between an earnings component's autocorrelation coefficient (unconditional persistence) and the marginal contribution of that component's persistence to the persistence of earnings (conditional persistence) provides a partial explanation of post‐earnings‐announcement drift, post‐revenue‐announcement drift, and the accrual anomaly. When the conditional persistence of revenue surprises is high (low) relative to its unconditional persistence, both the post‐earnings‐announcement drift and the post‐revenue‐announcement drift are high (low), because investors’ under‐reaction to revenues and earnings is stronger when the persistence of revenue surprises is more strongly associated with the persistence of earnings surprises. Also, the mispricing of accruals decreases substantially when the conditional persistence of accruals is high relative to its unconditional persistence, because investors’ over‐reaction to accruals is mitigated when the persistence of accruals is indeed more strongly associated with the persistence of earnings. Our findings also suggest that financial analysts’ failure to distinguish between unconditional and conditional persistence of revenues and accruals results in more biased revenue and earnings predictions.     

Does Accrual Management Impair the Performance of Earnings‐Based Valuation Models?

This study examines how accrual manipulations affect firm valuation in the years surrounding the passage of the Sarbanes‐Oxley Act (SOX). We compare the absolute percentage pricing errors of RIM and DCF valuation models for a group of US firms suspected to have engaged in accrual manipulations to avoid a small loss or a small earnings decline vs. ‘Normal’ firms matched on industry, year and size. We find that RIM can better estimate intrinsic value than DCF for the matched Normal firms in the pre‐SOX period, but not so for accrual manipulators, and that SOX mitigates the harmful effect of accrual manipulations, completely eliminating the difference in RIM's accuracy advantage over DCF between Normal firms and accrual manipulators. As a further analysis, we redefine Suspect firms as real‐activity manipulators and find a significant across‐group difference in accuracy wedge in both sample periods, implying that SOX has prompted firms to favor real‐activity manipulations over accrual manipulations.     

Blocked-Error-R 2: A Conceptually Improved Definition of the Proportion of Explained Variance in Models Containing Loops or Correlated Residuals

Bentler and Raykov (2000, Journal of Applied Psychology 85: 125–131), and Jöreskog (1999a, http://www.ssicentral.com/lisrel/column3.htm, 1999b http://www.ssicentral. com/lisrel/column5.htm) proposed procedures for calculating R ² for dependent variables involved in loops or possessing correlated errors. This article demonstrates that Bentler and Raykov’s procedure can not be routinely interpreted as a “proportion” of explained variance, while Jöreskog’s reduced-form calculation is unnecessarily restrictive. The new blocked-error-R ² (beR ²) uses a minimal hypothetical causal intervention to resolve the variance-partitioning ambiguities created by loops and correlated errors. Hayduk (1996) discussed how stabilising feedback models – models capable of counteracting external perturbations – can result in an acceptable error variance which exceeds the variance of the dependent variable to which that error is attached. For variables included within loops, whether stabilising or not, beR ² provides the same value as Hayduk’s (1996) loop-adjusted-R ². For variables not involved in loops and not displaying correlated residuals, beR ² reports the same value as the traditional regression R ². Thus, beR ² provides a conceptualisation of the proportion of explained variance that spans both recursive and nonrecursive structural equation models. A procedure for calculating beR ² in any SEM program is provided.