成本粘性影响管理层自愿性盈利预测了吗？--来自中国A股上市公司的证据

文章研究成本粘性与管理层自愿性盈利预测之间的关联。结果发现：成本粘性增加了管理层发布自愿性盈利预测的倾向及频率,同时,成本粘性程度与乐观盈利预测呈正相关,与悲观盈利预测呈负相关。影响机制表明,成本粘性通过提高信息不对称程度和企业经营风险从而影响了管理层自愿性盈利预测披露。进一步研究发现,政府补助加强了成本粘性与管理层自愿性盈利预测披露之间的关系,企业社会责任与经济政策不确定性削弱了两者之间的关系;然而政府补助、企业社会责任与经济政策不确定性对成本粘性与管理层自愿性盈利预测性质之间的关系没有显著影响。此外,成本粘性对管理层自愿性盈利预测的影响在非国有企业、非国际四大审计的公司中更为显著。最后,管理层自愿性盈利预测披露的增加,降低了权益资本成本,提升了企业价值。本文的发现丰富了成本粘性经济后果和自愿披露信息影响因素的研究,并提供了关于管理层的自由裁量权和管理激励在影响企业成本结构和盈利预测信息披露方面的证据。     

Forecasting the US real house price index: Structural and non-structural models with and without fundamentals

We employ a 10-variable dynamic structural general equilibrium model to forecast the US real house price index as well as its downturn in 2006:Q2. We also examine various Bayesian and classical time-series models in our forecasting exercise to compare to the dynamic stochastic general equilibrium model, estimated using Bayesian methods. In addition to standard vector-autoregressive and Bayesian vector autoregressive models, we also include the information content of either 10 or 120 quarterly series in some models to capture the influence of fundamentals. We consider two approaches for including information from large data sets — extracting common factors (principle components) in factor-augmented vector autoregressive or Bayesian factor-augmented vector autoregressive models as well as Bayesian shrinkage in a large-scale Bayesian vector autoregressive model. We compare the out-of-sample forecast performance of the alternative models, using the average root mean squared error for the forecasts. We find that the small-scale Bayesian-shrinkage model (10 variables) outperforms the other models, including the large-scale Bayesian-shrinkage model (120 variables). In addition, when we use simple average forecast combinations, the combination forecast using the 10 best atheoretical models produces the minimum RMSEs compared to each of the individual models, followed closely by the combination forecast using the 10 atheoretical models and the DSGE model. Finally, we use each model to forecast the downturn point in 2006:Q2, using the estimated model through 2005:Q2. Only the dynamic stochastic general equilibrium model actually forecasts a downturn with any accuracy, suggesting that forward-looking microfounded dynamic stochastic general equilibrium models of the housing market may prove crucial in forecasting turning points.     

DSGE model-based forecasting of modelled and nonmodelled inflation variables in South Africa

Inflation forecasts are a key ingredient for monetary policy-making – especially in an inflation targeting country such as South Africa. Generally, a typical Dynamic Stochastic General Equilibrium (DSGE) only includes a core set of variables. As such, other variables, for example alternative measures of inflation that might be of interest to policy-makers, do not feature in the model. Given this, we implement a closed-economy New Keynesian DSGE model-based procedure which includes variables that do not explicitly appear in the model. We estimate such a model using an in-sample covering 1971Q2 to 1999Q4 and generate recursive forecasts over 2000Q1 to 2011Q4. The hybrid DSGE performs extremely well in forecasting inflation variables (both core and nonmodelled) in comparison with forecasts reported by other models such as AR(1). In addition, based on ex-ante forecasts over the period 2012Q1–2013Q4, we find that the DSGE model performs better than the AR(1) counterpart in forecasting actual GDP deflator inflation.     

Examining the Demand for Ecosystem Services: The Value of Stream Restoration for Drinking Water Treatment Managers in the Llobregat River,Spain

Ecosystem services would be incorporated into decision making more often if researchers were to focus more on the demand for these services rather than the supply. This implies examining the economic, decision making and technological context of the end-user before trying to attribute economic values to well known biological processes. This paper provides an example of how this research approach for ecosystems services could unfold. In the Llobregat River in northeastern Spain, higher stream temperatures require water treatment managers to switch on costly water treatment equipment especially during warm months. This creates an opportunity to align the economic interests of downstream water users with the environmental goals of river managers. A restored riparian forest or an increase in stream flow could reduce the need for this expensive equipment by reducing stream temperatures below critical thresholds. We used the Stream Network Temperature Model (SNTEMP) to test the impact of increasing shading and discharge on stream temperature at the intake of the drinking water treatment plant. The value of the stream temperature ecosystem services provided by existing forests is €79,000 per year for the water treatment facility, while additional riparian forest restoration along the Llobregat River could generate economic savings for water treatment managers in the range of €57,000–€156,000 per year. Stream restoration at higher elevations would yield greater benefits than restoration in the lower reaches. Moderate increases in stream discharge (25%) could generate savings of €40,000 per year.     

Do we need a global VAR model to forecast inflation and output in South Africa?

This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The article considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-of-sample forecasts for South African GDP and inflation from six types of models: a general 33 country (large) GVAR, a customized small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customized small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly, however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1–2013:Q4 to highlight their ability to track turning points in output and inflation, respectively.     

Ex ante forecasts of business-cycle turning points

This paper presents a logit model that accurately forecasts business-cycle turning points with a lead of one-quarter. The sample period consists of an initialization subset (1959:Q3–1975:Q4), and a subset for out-of-sample forecast evaluation (1976:Q1–2005:Q4). In contrast with the record of disappointing results in the literature, the model correctly forecasts all turning points in the test subset without forecasting recessions that did not occur.      

Evaluating the joint efficiency of German trade forecasts - a nonparametric multivariate approach

ABSTRACT

I analyse the joint efficiency of export and import forecasts by leading economic research institutes for the years 1970 to 2017 for Germany in a multivariate setting. To this end, I compute, in a first step, multivariate random forests in order to model links between forecast errors and a forecaster’s information set, consisting of several trade and other macroeconomic predictor variables. I use the Mahalanobis distance as performance criterion and, in a second step, permutation tests to check whether the Mahalanobis distance between the predicted forecast errors for the trade forecasts and actual forecast errors is significantly smaller than under the null hypothesis of forecast efficiency. I find evidence for joint forecast inefficiency for two forecasters, however, for one forecaster I cannot reject joint forecast efficiency. For the other forecasters, joint forecast efficiency depends on the examined forecast horizon. I find evidence that real macroeconomic variables as opposed to trade variables are inefficiently included in the analysed trade forecasts. Finally, I compile a joint efficiency ranking of the forecasters.     

SIZE MATTERS: THE ECONOMIC VALUE OF BEACH EROSION AND NOURISHMENT IN SOUTHERN CALIFORNIA

Despite the widespread use of nourishment in California, few studies estimate the welfare benefits of increased beach width. This paper relies on panel data funded by National Oceanic and Atmospheric Administration and other agencies. Beach choices of respondents were combined with beach attribute data to reveal how changes in width affect choice and the economic value of beach visits. We use a random‐utility approach to show that the value of beach width varies for different types of beach uses: water contact, sand‐, and pavement‐based activities. We also find that the marginal value of beach width depends on initial beach width. (JEL Q50)     

Do FOMC forecasts add value to staff forecasts?

This paper compares the economic forecasts of members of the Board of Governors and presidents of the Federal Reserve Banks, and then investigates the value of each group's forecasts in supplementing the forecasts of the Board of Governors' staff. We find that the presidents tend to forecast higher inflation and real GDP growth, and lower unemployment than the members of the Board of Governors. We also find that the presidents' real GDP and unemployment rate forecasts add value to the real economy forecasts of the staff, while the governors' inflation forecasts add value to the staff's inflation forecasts.     

Learning to Play Limited Forecast Equilibria

This paper provides a learning justification for limited forecast equilibria, i.e., strategy profiles such that (1) players choose their actions in order to maximize the discounted average payoff over their horizon of foresight as given by their forecasts and (2) forecasts are correct on and off the equilibrium path. The limited forecast equilibria appear to be the stochastically stable outcomes of a simple learning process involving (vanishing) trembles.Journal of Economic LiteratureClassification Numbers: C72, D83.     

Forecasting emerging technologies: Use of bibliometrics and patent analysis

It is rather difficult to forecast emerging technologies as there is no historical data available. In such cases, the use of bibliometrics and patent analysis have provided useful data. This paper presents the forecasts for three emerging technology areas by integrating the use of bibliometrics and patent analysis into well-known technology forecasting tools such as scenario planning, growth curves and analogies. System dynamics is also used to be able to model the dynamic ecosystem of the technologies and their diffusion. Technologies being forecasted are fuel cell, food safety and optical storage technologies. Results from these three applications help us to validate the proposed methods as appropriate tools to forecast emerging technologies.     

Forecasting Housing Approvals in Australia: Do Forecasters Herd?

Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find evidence of forecaster herding. On the contrary, forecasters anti‐herd and, thereby, tend to intentionally scatter their forecasts around the consensus forecast. The extent of anti‐herding seems to vary over time. We also found that more pronounced anti‐herding leads to less accurate forecasts.     

VALUE OF IMPROVED LONG-RANGE WEATHER INFORMATION

An important human welfare implication of climate involves effects of interannual variation in temperature and precipitation on agriculture. Year-to-year variations in U.S. climate result from El Niño-Southern Oscillation (ENSO), a quasi-periodic redistribution of heat and momentum in the tropical Pacific Ocean. The study described here represents a preliminary assessment of the value to the entire U.S. agricultural sector of improved ENSO forecasts in the southeastern United States. This interdisciplinary assessment combines data and models from meteorology, plant sciences, and economics under a value of information framework based on Bayesian decision theory. An economic model of the U.S. agricultural sector uses changes in yields for various ENSO phases to translate physical (yield) effects of ENSO changes into economic effects on producers and on domestic and foreign consumers. The value of perfect information to agriculture is approximately $145 million. The economic value of an imperfect forecast is $96 million. These results suggest that increases in forecast accuracy have substantial economic value to agriculture.     

Do corporate executives have accurate predictions for the economy? A directional analysis

Although many studies on the directional accuracy of forecasts by international organizations and professional forecasters have been scrutinized, little attention has been paid to forecasts by business leaders. In order to address this gap, we use directional tests to investigate whether forecasts of Gross Domestic Product by corporate executives are valuable to their users. Our findings indicate that all the forecasts with forecast horizons from 1 to 14 months are valuable, whereas established literature indicates that longer-term forecasts tend not to be valuable. This suggests that corporate executives are concerned with and focus on longer-term economic environments and can therefore serve as an important resource for policymakers. However, some of the useful forecasts with real-time data, in particular those in the Tankan survey, are not useful with historical data.     

Mandated disclosures under IAS 36 Impairment of Assets and IAS 38 Intangible Assets: value relevance and impact on analysts’ forecasts

Drawing on a large sample of European firms, we examine whether variant compliance levels with mandated disclosures under IAS 36 Impairment of Assets and IAS 38 Intangible Assets are value relevant and affect analysts’ forecasts. Our results indicate a mean (median) compliance level of about 84% (86%) but high variation among firms and disclosure levels regarding IAS 36 being much lower than those regarding IAS 38. In depth, analysis reveals that non-compliance relates mostly to proprietary information and information that reveals managers’ judgment and expectations. Furthermore, we find a positive (negative) relationship between average disclosure levels and market values (analysts’ forecast dispersion). Results, however, hold more specifically for disclosures related to IAS 36, and these also improve analysts’ forecast accuracy. Our findings add knowledge regarding the economic consequences of mandatory disclosures, have an appeal to regulators and financial statement preparers and reflect on the IASB’s concerns to increase the guidance and principles on presentation and disclosure.     

Do the OECD 24 month horizon growth forecasts for the G7–countries contain information?

The OECD produces two–year–ahead growth forecasts for the G7–countries since 1987; these forecasts have never been evaluated. A regression is developed that tests for the information content of the forecasts. The idea is that this content is the added value forecasters incorporate in their forecasts. The information content is defined relative to the forecast for the previous year. In the end, the added value contained in the current year forecast is calculated relative to the last observation. The test consists in checking whether the information content reduces the forecasts error.

The study begins with a calculation of the usual accuracy statistics. These indicate an extreme low quality for the forecasts. The regression tests support this conclusion although the forecasts for Japan do possess some information. Alarming for users of forecasts is that there are no obvious alternatives.     

SNAKE RIVER SALMON RECOVERY: QUANTIFYING THE COSTS

Economic costs are relevant to endangered species protection in both theory and practice. Recovering endangered Snake River salmon will require modifying public land use, restricting fishing and hatchery production, reducing water for agriculture, and altering the operation of hydroelectric dams. The economic costs are estimated to range from $246 million to $359 million per year. While the estimated cost is not matched by the estimated increased value of fishing, the nonuse value of salmon recovery may be very large. Better economic and biological information is needed to assure that decisions for salmon species protection are reasonably cost effective. ( JEL Q28, H43)     

A variable P value rolling Grey forecasting model for Taiwan semiconductor industry production

The semiconductor industry plays an important role in Taiwan's economy. In this paper, we constructed a rolling Grey forecasting model (RGM) to predict Taiwan's annual semiconductor production. The univariate Grey forecasting model (GM) makes forecast of a time series of data without considering possible correlation with any leading indicators. Interestingly, within the RGM there is a constant, P value, which was customarily set to 0.5. We hypothesized that making the P value a variable of time could generate more accurate forecasts. It was expected that the annual semiconductor production in Taiwan should be closely tied with U.S. demand. Hence, we let the P value be determined by the yearly percent change in real gross domestic product (GDP) by U.S. manufacturing industry. This variable P value RGM generated better forecasts than the fixed P value RGM. Nevertheless, the yearly percent change in real GDP by U.S. manufacturing industry is reported after a year ends. It cannot serve as a leading indicator for the same year's U.S. demand. We found out that the correlation between the yearly survey of anticipated industrial production growth rates in Taiwan and the yearly percent changes in real GDP by U.S. manufacturing industry has a correlation coefficient of 0.96. Therefore, we used the former to determine the P value in the RGM, which generated very accurate forecasts.     

Managing price risks using and local polynomial kernel forecasts

This study contributes to understanding price risk management through hedging strategies in a forecasting context. A relatively new forecasting method, nonparametric local polynomial kernel (LPK), is used to forecast prices and to generate ex ante hedge ratios. The selective multiproduct hedge based on the LPK price and hedge ratio forecasts is in general found to be better than continuous hedging, no hedging and alternative forecasting procedures. Selective multivariate hedging using the LPK is found to improve hog producer's expected returns. The findings indicate that combining hedging with forecasts, especially when using the LPK procedure, can improve price risk management.