Indirect inference and calibration of dynamic stochastic general equilibrium models

We advocate in this paper the use of a sequential partial indirect inference (SPII) approach, in order to account for calibration practice where dynamic stochastic general equilibrium models (DGSE) are studied only through their ability to reproduce some well-chosen moments. We stress that, despite a lack of statistical formalization, the controversial calibration methodology addresses a genuine issue on the consequences of misspecification in highly nonlinear and dynamic structural macro-models. We argue that a well-driven SPII strategy might be seen as a rigorous calibrationnist approach, that captures both the advantages of this approach (accounting for structural “a-statistical” ideas) and of the inferential approach (precise appraisal of loss functions and conditions of validity). This methodology should be useful for the empirical assessment of structural models such as those stemming from the real business cycle theory or the asset pricing literature.     

Methods for Handling Dropouts in Longitudinal Clinical Trials

This paper focuses on the monotone missing data patterns produced by dropouts and presents a review of the statistical literature on approaches for handling dropouts in longitudinal clinical trials. A variety of ad hoc procedures for handling dropouts are widely used. The rationale for many of these procedures is not well-founded and they can result in biased estimates of treatment comparisons. A fundamentally difficult problem arises when the probability of dropout is thought to be related to the specific value that in principle should have been obtained; this is often referred to as informative or non-ignorable dropout. Joint models for the longitudinal outcomes and the dropout times have been proposed in order to make corrections for non-ignorable dropouts. Two broad classes of joint models are reviewed: selection models and pattern-mixture models. Finally, when there are dropouts in a longitudinal clinical trial the goals of the analysis need to be clearly specified. In this paper we review the main distinctions between a 'pragmatic' and an 'explanatory' analysis. We note that many of the procedures for handling dropouts that are widely used in practice come closest to producing an explanatory rather than a pragmatic analysis.     

International Standardisation and Harmonisation: a New Measurement Technique

We argue in this paper that it is the availability of alternative accounting treatments and the use by individual firms of the appropriate method that produces financial statements which are comparable. Accordingly, international harmony in accounting should be seen as a state in which firms throughout the world are able to use an internationally-recognised accounting treatment that is appropriate to their circumstances without being constrained to do otherwise by local accounting regulations or other requirements to adopt practices confined to particular nations. The paper distinguishes harmonisation from standardisation and presents a method for measuring harmonisation which allows for choice between alternative accounting treatments. The statistical model also takes account of the stylised fact that accounting treatments are not mutually exclusive, and the method is illustrated by an analysis of goodwill accounting practices in Europe.     

A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder

Abstract

In this paper, we make multi-step forecasts of the annual growth rates of the real GDP for each of the 16 German Länder simultaneously. We apply dynamic panel models accounting for spatial dependence between regional GDP. We find that both pooling and accounting for spatial effects help to improve the forecast performance substantially. We demonstrate that the effect of accounting for spatial dependence is more pronounced for longer forecasting horizons (the forecast accuracy gain is about 9% for a 1-year horizon and exceeds 40% for a 5-year horizon). We recommend incorporating a spatial dependence structure into regional forecasting models, especially when long-term forecasts are made.     

Misspecification and Expectations Correction in New Keynesian DSGE Models

This paper focuses on the dynamic misspecification that characterizes the class of small‐scale New Keynesian models currently used in monetary and business cycle analysis, and provides a remedy for the typical difficulties these models have in accounting for the rich contemporaneous and dynamic correlation structure of the data. We suggest using a statistical model for the data as a device through which it is possible to adapt the econometric specification of the New Keynesian model such that the risk of omitting important propagation mechanisms is kept under control. A pseudo‐structural form is built from the baseline system of Euler equations by forcing the state vector of the system to have the same dimension as the state vector characterizing the statistical model. The pseudo‐structural form gives rise to a set of cross‐equation restrictions that do not penalize the autocorrelation structure and persistence of the data. Standard estimation and evaluation methods can be used. We provide an empirical illustration based on USA quarterly data and a small‐scale monetary New Keynesian model.     

Residual Income Valuation Models and Inflation

Existing empirical evidence suggests that residual income valuation models based on historical cost accounting considerably underestimate equity values. One possible explanation is the use of historical cost accounting under inflationary conditions. In this paper, we use a residual income framework to explore theoretically how historical cost accounting numbers need to be adjusted for inflation in forecasting and valuation. We demonstrate that even in a simple setting where inflation is running at a relatively low level, residual income models are likely to produce severe under-valuations if inflation is not properly taken into account. We use simulated data to reinforce our theoretical findings and to illustrate the difficulties that empirical investigators face working within the confines imposed by real data.     

An approach to the R&D value based upon real option method

This paper aims to build up feasible models based upon the real option method (ROM) to assess R&D. In this paper we built eight models integrating with three conditions to assimilate the real situation an R&D project may encounter. We utilized both numerical and statistical illustration based upon an empirical case to exhibit the correctness of our models. We diverged our discussion into either expense or capital viewpoint to react with current accounting debate. The results shed us the light: our models in both expense and capital viewpoint can correctly asses R&D; the selected conditions play essential roles to influence the correctness of our models.     

Neural network forecasting of quarterly accounting earnings

This study uses an artificial neural network model to forecast quarterly accounting earnings for a sample of 296 corporations trading on the New York stock exchange. The resulting forecast errors are shown to be significantly larger (smaller) than those generated by the parsimonious Brown-Rozeff and Griffin-Watts (Foster) linear time series models, bringing into question the potential usefulness of neural network models in forecasting quarterly accounting earnings. This study confirms the conjecture by Chatfield and Hill et al. that neural network models are context sensitive. In particular, this study shows that neural network models are not necessarily superior to linear time series models even when the data are financial, seasonal and non-linear.     

The Coverage Properties of Confidence Regions After Model Selection

It is very common in applied frequentist ("classical") statistics to carry out a preliminary statistical (i.e. data-based) model selection by, for example, using preliminary hypothesis tests or minimizing AIC. This is usually followed by the inference of interest, using the same data, based on the assumption that the selected model had been given to us  a priori . This assumption is false and it can lead to an inaccurate and misleading inference. We consider the important case that the inference of interest is a confidence region. We review the literature that shows that the resulting confidence regions typically have very poor coverage properties. We also briefly review the closely related literature that describes the coverage properties of prediction intervals after preliminary statistical model selection. A possible motivation for preliminary statistical model selection is a wish to utilize uncertain prior information in the inference of interest. We review the literature in which the aim is to utilize uncertain prior information directly in the construction of confidence regions, without requiring the intermediate step of a preliminary statistical model selection. We also point out this aim as a future direction for research.     

Returns to Schooling and Bayesian Model Averaging: A Union of Two Literatures

Abstract.  In this paper, we review and unite the literatures on returns to schooling and Bayesian model averaging. We observe that most studies seeking to estimate the returns to education have done so using particular (and often different across researchers) model specifications. Given this, we review Bayesian methods which formally account for uncertainty in the specification of the model itself, and apply these techniques to estimate the economic return to a college education. The approach described in this paper enables us to determine those model specifications which are most favored by the given data, and also enables us to use the predictions obtained from all of the competing regression models to estimate the returns to schooling. The reported precision of such estimates also account for the uncertainty inherent in the model specification. Using U.S. data from the National Longitudinal Survey of Youth (NLSY), we also revisit several 'stylized facts' in the returns to education literature and examine if they continue to hold after formally accounting for model uncertainty.     

Where Corporate Governance and Financial Analysts Affect Valuation

We examine whether corporate governance and financial analysts affect accounting-based valuation models for B and H shares traded by foreign investors in China and Hong Kong, respectively. We expect that better corporate governance and more effective analyst activity mitigate potential adverse effects on accounting valuation models generated by country-specific problems in accounting, auditing, and legal systems. We find that valuation models perform better for companies with a greater analyst following, smaller forecast errors, relatively high public ownership and a strong board structure. Valuation models and accounting numbers have only limited explanatory power and valuation role for companies with weak governance and less effective analyst performance. The findings are robust across various market value, return, unexpected return, and other accounting valuation models. The results are consistent with less informed foreign investor clienteles searching for signals of more effective analyst activity and better corporate governance mechanisms.     

THE ECONOMICS OF COUNTERTERRORISM: A SURVEY

We provide a review of theoretical and empirical contributions on the economic analysis of terrorism and counterterrorism. We argue that simple rational‐choice models of terrorist behavior – in the form of cost‐benefit models – already provide a well‐founded theoretical framework for the study of terrorism and counterterrorism. We also hint at their limitations which relate to the failure of accounting for the dynamics between terrorism and counterterrorism that may produce unintended second‐order effects as well as for the costs associated with counterterrorism and its international dimension. We reevaluate previously proposed counterterrorism strategies accordingly. Finally, in the light of our findings, we discuss interesting areas of future research.     

New opportunities for farm accounting

This paper starts out to observe that there is a gap between the importance given to accounting and the low level of bookkeeping and accounting practice in the agricultural sector. Reasons for this gap are that current general accounting rules do not adapt very well to the particularities of farming and are difficult and expensive to implement. We then suggest that the Farm Accountancy Data Network (FADN) and the recently issued International Accounting Standard on Agriculture (IAS 41) could be key elements to improve the use of accounting in European farms. We review the main contributions of IAS 41 and conclude that it provides a strong conceptual framework but might need further instruments for its implementation in practice, given the limitations of the agricultural sector. We continue to explain that FADN is an experienced network that has elaborated very detailed farm accounting procedures, and suggest that these procedures could be turned into a guide for implementing IAS 41. We report empirical data which indicate that current FADN reports are already considered useful by farmers for different purposes. Finally, we analyse in detail the compatibility of IAS 41 and FADN, identifying changes in the FADN procedures that would become necessary if the FADN procedures were to be used for implementing IAS 41 and some aspects of FADN that might have been worthwhile to consider for the final version of IAS 41.     

Model combinations through revised base rates

Standard selection criteria for forecasting models focus on information that is calculated for each series independently, disregarding the general tendencies and performance of the candidate models. In this paper, we propose a new way to perform statistical model selection and model combination that incorporates the base rates of the candidate forecasting models, which are then revised so that the per-series information is taken into account. We examine two schemes that are based on the precision and sensitivity information from the contingency table of the base rates. We apply our approach on pools of either exponential smoothing or ARMA models, considering both simulated and real time series, and show that our schemes work better than standard statistical benchmarks. We test the significance and sensitivity of our results, discuss the connection of our approach to other cross-learning approaches, and offer insights regarding implications for theory and practice.     

我院2000—2010年肺癌病325例资料统计分析

目的:肺癌是最常见的肺原发性恶性肿瘤。通过统计分析,了解我市肺癌的发病特点以及变化趋势,为我市肿瘤防治工作提供详细的参考数据。方法:收集本院2000～2010年10年间确诊的肺癌病例资料,录入到Access数据库中进行统计分析。结果10年间共检出肺癌病例325例,其中后5年肺癌病例224例,较前5年多101例;45岁以上278例,占全部病例的85.53%,高峰年龄段为50～65岁组。其中,男286例,占88%;女39例,占12%。入院进行手术的病例共126例,占全部病例的38.76%。结论:本市肺癌发病率呈上升状态,男性多于女性。     

The analysis of corporate governance policy and corporate financial performance

The main purpose of this study is to investigate the relationship between the quality of corporate governance policy and the firm financial performance and. Data were collected from Corporate Library. A sample of 3,068 firms from the database of 2010 Corporate Library was analyzed. Logistic regression models were employed and SPSS statistical package was utilized to perform the analysis. Our results show that when firms have better corporate governance policies, they are more likely to perform better. Specifically, when firms have a better board rating, compensation policy, takeover defense strategy, accounting practice, and a formal governance policy, they are more likely to perform better than their counterparts without such quality corporate governance policies.     

Accounting for financial instruments in the banking industry: conclusions from a simulation model

The paper analyses the effects of three sets of accounting rules for financial instruments – Old IAS before IAS 39 became effective, Current IAS or US GAAP, and the Full Fair Value (FFV) model proposed by the Joint Working Group (JWG) – on the financial statements of banks. We develop a simulation model that captures the essential characteristics of a modern universal bank with investment banking and commercial banking activities. We run simulations for different strategies (fully hedged, partially hedged) using historical data from periods with rising and falling interest rates.

We show that under Old IAS a fully hedged bank can portray its zero economic earnings in its financial statements. As Old IAS offer much discretion, this bank may also present income that is either positive or negative. We further show that because of the restrictive hedge accounting rules, banks cannot adequately portray their best-practice risk management activities under Current IAS or US GAAP. We demonstrate that – contrary to assertions from the banking industry – mandatory FFV accounting adequately reflects the economics of banking activities.

Our detailed analysis identifies, in addition, several critical issues of the accounting models that have not been covered in previous literature.     

The development of models of accounting information systems in Sweden

Traditionally, accounting has evolved as a technique. Practitioners and academics have striven to improve the technical features of accounting. Now, accounting is conceived increasingly as an organizational artefact which interacts with other organizational practices in shaping organizational reality. Taking a broad perspective of management control, this paper aims at contributing to our knowledge of accounting in this second sense and identifying some Swedish contributions to the tool-box of accounting. In reviewing the development of models of accounting information in Sweden mainly since World War II, different factors explaining the evolution of each model are identified. Analyses of these factors reveal that different groups of actors have been engaged in the development of different parts of the accounting information system (AIS). It is also noted that the introduction of models that are widely applied has delayed the emergence of new models better adapted to the current situation in business. Comparisons will be made with the development in some other countries.     

Realized volatility forecast with the Bayesian random compressed multivariate HAR model

We develop a Bayesian random compressed multivariate heterogeneous autoregressive (BRC-MHAR) model to forecast the realized covariance matrices of stock returns. The proposed model randomly compresses the predictors and reduces the number of parameters. We also construct several competing multivariate volatility models with the alternative shrinkage methods to compress the parameter’s dimensions. We compare the forecast performances of the proposed models with the competing models based on both statistical and economic evaluations. The results of statistical evaluation suggest that the BRC-MHAR models have the better forecast precision than the competing models for the short-term horizon. The results of economic evaluation suggest that the BRC-MHAR models are superior to the competing models in terms of the average return, the Shape ratio and the economic value.     

Review

In this study we extend prior research on the international analysis of accounting conservatism (Joos and Lang, 1994; Ball et al., 2000; Giner and Rees, 2001), by examining the level of accounting conservatism across eight European countries (United Kingdom, Germany, France, Switzerland, the Netherlands, Italy, Spain and Belgium), and assessing the statistical significance of the differences among them. The definitions of conservatism that we use are, on the one hand, the Feltham and Ohlson (1995) definition, which implies a persistent understatement of book value of shareholders' equity (balance sheet conservatism). On the other hand, we use the one proposed by Basu (1997), that is, a timelier recognition of bad news in earnings relative to good news (earnings conservatism). We also address the possible scale problems of the models used to measure balance sheet conservatism. Finally, we check whether our comparative results could be influenced by a different sample composition in each country. Our results show that there are both balance sheet and earnings conservatism practices in all countries under study. In addition, while continental countries show larger balance sheet conservatism, differences in earnings conservative practices between countries are not that pronounced, although they tend to be larger in the UK. We also find that the existence of balance sheet conservative practices is associated with reduced levels of earnings conservatism, which is consistent with the results in Pope and Walker (2003).