Health expenditure models: A comparison using panel data

In this article, we focus on the estimation of outpatient expenditures with panel data. We model the logarithm of expenditures and consider five different models. The first two are two-part and sample selection cross-section models. Two-part panel data models turn out to be inappropriate for dealing with expenditures. We thus estimate sample selection models with panel data: one without a lagged dependent variable and two with a lagged dependent variable. These two latter models differ in their assumptions on the variance of the residuals. Modelling heteroscedasticity may indeed be important to avoid the bias due to the retransformation problem. We show that lagged dependent variables are important factors for heteroscedasticity. For the models with state dependence, we provide a new solution to the initial conditions problem by controlling for generalised residuals. We establish that panel data models highly improve the correlation explained by the model in the time-series dimension without damaging the fit in the cross-section dimension. For all indicators of fit, the model with state dependence and heteroscedasticity seems to dominate the others.     

Catching the curl: Wavelet thresholding improves forward curve modelling

Modelling futures term structures (price forward curves) is essential for commodity-related investments, portfolios, risk management, and capital budgeting decisions. This paper uses a novel strategy, wavelet thresholding, to de-noise futures price data prior to estimation in a state-space framework in order to improve model fit and prediction. Rather than de-noise the raw data, this method de-noises only wavelet coefficients linked to specific timescales, minimizing the amount of information that is accidentally removed. Our findings are that, for the first five futures maturities in our sample data, in-sample (tracking) and 5-day-ahead out-of-sample (forecasting) Root Mean Squared Errors (RMSEs) are smaller both (i) when we increase the number of factors from one to four, and (ii) when we de-noise the data using wavelet thresholding. The improvement due to wavelet thresholding is often greater than the improvement from adding one more factor to the model, which is important because going beyond four factors does not improve model fit. Wavelet-based de-noising thus has the potential to improve considerably the estimation of various economic time series models, helping practitioners and policymakers with better forecasting and risk management.     

信用担保公司成长能力评价指标体系的构建及运用

针对担保行业的特点,首先构建了包括基本指标、修正指标与评议指标的信用担保公司成长能力评价指标体系,该指标体系涵盖了影响公司成长性的经营规模、效率与效益、扩张惯性、经营风险等一级指标。然后,以四川省内10家担保公司为样本,利用该评价指标体系,分别采用功效系数综合评价法和因子分析法,对其成长能力进行了评价,并对基于两种方法得到的评价结果进行比较。结果显示：虽然功效系数法在成长能力综合评价方面较单因素分析更具优势,但其权重设定采用的是专家打分法,具有一定的主观性;因子分析法可通过使用旋转技术来解释因子,使复杂矩阵变得简洁,在解释影响因素方面更有优势。最后提出：在实证分析过程中,将两种方法相结合能得到更理想的评价结果。     

Modelling mortality: are we heading in the right direction?

Predicting life expectancy has become of upmost importance in society. Pension providers, insurance companies, government bodies and individuals in the developed world have a vested interest in understanding how long people will live for. This desire to better understand life expectancy has resulted in an explosion of stochastic mortality models many of which identify linear trends in mortality rates by time. In making use of such models for forecasting purposes, we rely on the assumption that the direction of the linear trend (determined from the data used for fitting purposes) will not change in the future, recent literature has started to question this assumption. In this article, we carry out a comprehensive investigation of these types of models using male and female data from 30 countries and using the theory of structural breaks to identify changes in the extracted trends by time. We find that structural breaks are present in a substantial number of cases, that they are more prevalent in male data than in female data, that the introduction of additional period factors into the model reduces their presence, and that allowing for changes in the trend improves the fit and forecast substantially.     

Impact of the dedicated infrastructure on bus service quality: an empirical analysis

An econometric analysis is conducted to quantify the impact of different variables on the service quality of the Santiago, Chile bus system. The indicators tested as measures of service quality are the average bus speed, the bus trip time coefficient of variation and the bus headway coefficient of variation. The analysis uses peak hour data obtained for all the routes served by the system’s various concessionaire operators. A separate multiple linear regression model is estimated for each indicator, with the latter as the explained variable. The main explanatory variables are a series of design factors representing different types of dedicated route infrastructure and the incorporation in some routes of segments of urban motorway. The results of the models show that the existence of dedicated bus route infrastructure positively impacts all three service quality indicators. The use of motorway segments in particular has a major positive effect on average speed. The model estimates also reveal that the main explanatory factor in headway variability at the end of a route is the headway variability at the start of it, the latter factor determined by operator management decisions regarding bus despatches.     

Forecasting monthly industrial production in real-time: from single equations to factor-based models

The aim of this paper is to analyze the performance of alternative forecasting methods to predict the index of industrial production in Italy from 1 to 3 months ahead. We use twelve different models, from simple ARIMA to dynamic factor models exploiting the timely information of up to 110 short-term indicators, both qualitative and quantitative. This allows to assess the relevance for the forecasting practice of alternative combinations of types of data (real-time and latest available), estimation methods and periods. Out-of-sample predictive ability tests stress the relevance of more indicators in disaggregate models over sample periods covering a complete business cycle (about 7 years in Italy). Our findings downgrade the emphasis on both the estimation method and data revision issues. In line with the classical “average puzzle”, the use of simple averages of alternative forecasts often improves the predictive ability of their single components, mainly over short horizons. Finally, selected indicators and factor-based models always perform significantly better than ARIMA models, suggesting that the short-run indicator signal always dominates the noise component. On this regard, selected indicators models can further increase the amount of signal extracted to improve up to 30–40% the short-run predictive ability of factor-based models and to forecast-encompass them.     

Uniform testing and portfolio strategies for single and multifactor asset pricing models in the Pacific Basin markets

We test and implement portfolio strategies for three major asset pricing models, under uniform diagnostic measures using the PACAP data set containing all current listing and de-listing of firms for the local stock exchange in several Pacific Basin countries. Compared to the often used MSCI database that include only a subset of the (large) firms in the local markets, the more complete coverage of our database allows for more robust testing of current multifactor asset pricing models since the possible effects of additional factors such as size and book to market may not show up correctly using less comprehensive data sets. Our data set also provides a natural packet of nonUS data for addressing the issue of whether the results of recent asset pricing research are sample specific. Our overall results provide multi-country (sample nonspecific) support for the additional asset pricing risk factors of the Fama-French three-factor model but not for the momentum factor of the Carhart model. We additionally find that the size risk factor is more prominent than value risk factor in the Pacific Basin markets. Finally, we find strong evidence that portfolio strategies implemented to capture value and size effects are profitable in the Pacific Basin stock markets.     

Discount rates and credit markets: Theory and evidence from rural india

Three models of credit markets - (1) the permanent income model, (2) upward sloping credit supply to individual borrowers, and (3) constrained credit due to imperfect enforcement - are tested using credit market data and an experimental study of individuals' discount rates in south India. The permanent income model is rejected by both the discount rate and the credit market data. The discount rate data are consistent with either of the other two models, while the credit market data are consistent with a combination of these two models. Other explanations are found to be insufficient to explain the results of this study.     

Narcissism,political tenure,financial indicators and the effectiveness of environmental regulation

This article explores the factors that determine the effectiveness of environmental regulation in the United States and Australia. Unlike prior literature, in which lagging performance measures (such as carbon emissions) are used, we use financial data to develop effectiveness scores and identify the determinants of effectiveness, including narcissistic behaviour, tenure of political leaders and financial indicators. Consistent with the emerging literature on environmental finance, we find that abnormal returns are associated with environmental regulation and that effectiveness is adversely affected when narcissistic leaders are in power. Our results remain robust when we control for various event windows and models.     

Measures of underlying inflation in the euro area: assessment and role for informing monetary policy

The paper evaluates the 24-month-ahead inflation forecasting performance of various indicators of underlying inflation and structural models. Measures derived using the generalized dynamic factor model (GDFM) overperform other measures over the monetary policy horizon and are leading indicators of headline inflation. Trimmed means, although weaker than GDFM indicators, have good forecasting performance, while indicators by permanent exclusion underperform but provide useful information about short-term dynamics. The forecasting performance of theoretically-founded models that relate monetary aggregates, the output gap, and inflation improves with the time horizon but generally falls short of that of the GDFM. A composite measure of underlying inflation, derived by averaging the statistical indicators and the model-based estimates, improves forecast accuracy by eliminating bias and offers valuable insight about the distribution of risks.     

Mixed logit estimation of willingness to pay distributions: a comparison of models in preference and WTP space using data from a health-related choice experiment

Different approaches to modelling the distribution of WTP are compared using stated preference data on Tanzanian Clinical Officers’ job choices and mixed logit models. The standard approach of specifying the distributions of the coefficients and deriving WTP as the ratio of two coefficients (estimation in preference space) is compared to specifying the distributions for WTP directly at the estimation stage (estimation in WTP space). The models in preference space fit the data better than the corresponding models in WTP space although the difference between the best fitting models in the two estimation regimes is minimal. Moreover, the willingness to pay estimates derived from the preference space models turn out to be very high for many of the job attributes. The results suggest that sensitivity testing using a variety of model specifications, including estimation in WTP space, is recommended when using mixed logit models to estimate willingness to pay distributions.     

Inflation forecast uncertainty

We study the inflation uncertainty reported by individual forecasters in the Survey of Professional Forecasters 1969-2001. Three popular measures of uncertainty built from survey data are analyzed in the context of models for forecasting and asset pricing, and improved estimation methods are suggested. Popular time series models are evaluated for their ability to reproduce survey measures of uncertainty. The results show that disagreement is a better proxy of inflation uncertainty than what previous literature has indicated, and that forecasters underestimate inflation uncertainty. We obtain similar results for output growth uncertainty.     

小企业信用评估的模型构建与实证分析

文章采用我国东部某银行小企业信贷数据,使用Logistic模型与因子分析相结合的方法提出了构建小企业信用评估体系的思路。研究结果表明,财务因素中资产负债率与违约概率正相关,非财务指标中对违约概率影响最大的是基本存款账户是否在本银行和银行客户类型。与以往研究相比,文章综合考虑了财务与非财务指标,研究结果对构建小企业信用评估指标体系具有重要的参考价值。     

An analysis of regime shifts in the Turkish economy

We use a time-varying dynamic factor model with regime switching to construct and estimate the leading indicators of the currency crises in Turkey. After that, we analyze the business cycles of the Turkish economy, by using a three-state univariate Markov-switching model. Both models capture the observed dynamics of the Turkish economy over the period 1987–2002.     

Comparing China’s GDP statistics with coincident indicators

We use factor analysis to summarize information from various macroeconomic indicators, effectively producing coincident indicators for the Chinese economy. We compare the dynamics of the estimated factors with GDP, and compare our factors with other published indicators for the Chinese economy. The estimated factors and the published coincident indicators match the GDP dynamics well and discrepancies are very short. The largest discrepancies may correspond to shocks affecting the growth process.     

上市公司会计信息和股价变动相关性研究

文章以近三年的沪深能源类上市公司的财务数据为样本,将财务指标分为三类,运用皮尔逊相关性系数分析了上市公司财务指标与其股价的关系。结果发现：上市公司财务指标对股价的解释力呈减弱的趋势;每股收益和总资产报酬率是影响股价的主要因素;公司偿债能力与股价呈负相关,对股价变动不具有统计意义上的相关性;公司盈利能力是影响上市公司股价的主要因素。     

Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non-nested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail.     

Do disaggregated CPI data improve the accuracy of inflation forecasts?

In this paper, we evaluate the role of using consumer price index (CPI) disaggregated data to improve the accuracy of inflation forecasts. Our forecasting approach is based on extracting the factors from the subcomponents of the CPI at the highest degree of disaggregation. The data set contains 54 macroeconomic series and 243 CPI subcomponents from 1992 to 2009 for Mexico. We find that the factor models that include disaggregated data outperform the benchmark autoregressive model and the factor models containing alternative groups of macroeconomic variables. We provide evidence that using disaggregated price data improves forecasting performance. The forecasts of the factor models that extract the information from the CPI disaggregated data are as accurate as the forecasts from the survey of experts.     

I (Could Have) Told You! Early warning indicators and crisis performance of the 12 “old” Euro-countries under the economic crisis 2008–09

The impact of the economic crisis 2008-09 was remarkably different in the 12 “old” member states in the Euro-zone. Five of them were hit especially badly; four of them even had to be bailed out by the rest in one way or another. This paper asks if one could have foretold, based solely on information available prior to 1999, which of the countries then about to enter the Euro-zone would run into economic trouble once a serious economic crisis occurred. The focus is on the (post)predictive power of three kinds of leading indicators: economic indicators, political indicators (indicating quality of governance), and indicators derived from the theory of optimal currency areas (OCA). Since there are more indicators than cases, PLS-regression is used to gauge the (post)predictive strength of the indicators examined. The results show that political indicators have quite some (post)predictive power in this case, whereas indicators derived from OCA-theory do not do too well. Economic indicators perform better than indicators derived from OCA-theory, but generally less well than the political indicators. Thus, the experience from the latest economic crisis in the Euro-zone suggests that more emphasis should be placed on the quality of governance record of a country when deciding if it should be deemed fit to become a member.     

Globalization and labor market institutions: International empirical evidence

A widespread concern is that labor market institutions erode in the course of globalization, which, in turn, decreases employment and wages. By using panel data and cross-sectional data, I investigate the influence of globalization on labor market regulation. I use the indicators of labor market institutions by Gwartney et al. (2012) and the KOF indices of globalization. To deal with potential reverse causality, I employ a system GMM panel estimator and use a constructed trade share as proposed by Frankel and Romer (1999) as an instrumental variable for globalization in cross-sectional models. The results do not show that globalization induced labor market deregulation.