Measurement error and panel data1

The underidentification of linear models with measurement error does not necessarily extend to panel data models, as has been shown by GAiliches and Hausman (1986). We discuss and extend some of their results for a simple case and address particular issues concerning identification and asymptotic variances.     

Nonresponse in Contingent Valuation-reducing uncertainty in value inference

This paper focuses on problems associated with nonresponse in Contingent Valuation surveys. The results from a telephone follow-up survey show that value inference can be considerably improved by information on nonrespondents' attitudes.     

Estimation methods for stochastic volatility models: a survey

Abstract.  Although stochastic volatility (SV) models have an intuitive appeal, their empirical application has been limited mainly due to difficulties involved in their estimation. The main problem is that the likelihood function is hard to evaluate. However, recently, several new estimation methods have been introduced and the literature on SV models has grown substantially. In this article, we review this literature. We describe the main estimators of the parameters and the underlying volatilities focusing on their advantages and limitations both from the theoretical and empirical point of view. We complete the survey with an application of the most important procedures to the S&P 500 stock price index.     

Pricing-to-market tests in instrumental regressions: Case of the transportation equipment industry

Pricing-to-market (PTM) theory suggests that exporting monopolistic firms adjust their destination-specific mark-ups in the face of exchange rate shocks. A large proportion of the existing evidence for PTM comes from Wald tests applied to OLS- and IV-estimated parameters of single-equation models. Such tests can seriously over-reject in the presence of endogeneity and weak instruments so that some of the available results supporting PTM could be spurious. In this paper we revisit the PTM evidence for Japanese and German exporting firms in the transportation equipment industry. Using the model of Marston (1990), we apply exogeneity and LR-LIML-based tests for which the error probability is controlled irrespective of the quality of the available instruments. Our results show right-hand-side endogeneity in almost all of the examined PTM equations. In addition, we find that statistical decisions often differ depending on whether they are based on the traditional Wald test or on our proposed test.The authors would like to thank Philippe Barla, Jean-Thomas Bernard, Christos Constantatos, Larry Schembri, seminar participants at the Bank of Canada, and two anonymous referees for useful comments and suggestions. Thanks also to Richard Marston for providing data. Finally, a special thanks to Marjorie Santos for excellent research assistance. All remaining errors are our own and should not be attributed to the Bank of Canada.First version received: May 2002/Final version received: December 2002     

1998年中国上市公司并购实践的效应分析

对上市公司的收购是中国股市近些年出现的新现象 ,本文以 1 998年深沪两市发生的全部 67家公司的并购为样本 ,以公司净资产收益率 (NROA)和主业利润率(CROA)为指标 ,将所有样本根据并购原因划分为 6类 ,以并购前 2年和并购后 3年的数据为基础 ,系统分析了并购的效应与得失。在此基础上 ,还讨论了国有企业与民营企业作为收购方的并购效应和并购的有偿转让与无偿划拨方式的不同结果。文章的结论是 :业绩较差的公司较愿出让控股权 ;多数并购是战略性的 ,获上市地位是主要的并购动力 ;并购后主业得到明显加强 ;市场化的战略性并购效果较好 ,有偿并购的效果也较好。     

On estimating long-run effects in models with lagged dependent variables

A common procedure in economics is to estimate long-run effects from models with lagged dependent variables. For example, macro panel studies frequently are concerned with estimating the long-run impacts of fiscal policy, international aid, or foreign investment.Our analysis points out the hazards of this practice. We use Monte Carlo experiments to demonstrate that estimating long-run impacts from dynamic models produces unreliable results.Biases can be substantial, sample ranges very wide, and hypothesis tests can be rendered useless in realistic data environments. There are three reasons for this poor performance. First, OLS estimates of the coefficient of a lagged dependent variable are downwardly biased in finite samples. Second, small biases in the estimate of the lagged, dependent variable coefficient are magnified in the calculation of long-run effects. And third, and perhaps most importantly, the statistical distribution associated with estimates of the LRP is complicated, heavy-tailed, and difficult to use for hypothesis testing. While many of the underlying problems have been long-known in the literature, the continued widespread use of the associated empirical procedures suggests that researchers are unaware of the extent and severity of the estimation problems. This study aims to illustrate their practical importance for applied research.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Small Area Estimation for Disease Prevalence Mapping

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area level and at the unit level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse, and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015–2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to coronavirus disease 2019 is discussed.     

Inference for large financial systems

We treat the parameter estimation problem for mean‐field models of large interacting financial systems such as the banking system and a pool of assets held by an institution or backing a security. We develop an asymptotic inference approach that addresses the scale and complexity of such systems. Harnessing the weak convergence results developed for mean‐field financial systems in the literature, we construct an approximate likelihood for large systems. The approximate likelihood has a conditionally Gaussian structure, enabling us to design an efficient numerical method for its evaluation. We provide a representation of the corresponding approximate estimator in terms of a weighted least‐squares estimator, and use it to analyze the large‐system and large‐sample behavior of the estimator. Numerical results for a mean‐field model of systemic financial risk highlight the efficiency and accuracy of our estimator.