Identifying Conditional Conservatism

Abstract

This paper provides guidance for empiricists interested in measuring conditional conservatism and in interpreting associations of those measures with variables of interest. I begin by discussing the nature and importance of conditional conservatism and surveying the literature identifying conditional conservatism. I then describe and comment on the various limitations of asymmetric timeliness identified in the literature. Despite these limitations, I argue that asymmetric timeliness is the most direct implication of conditional conservatism, and that alternative measures that have been proposed need not capture any type of conservatism. Finally, I provide four specific suggestions for estimating asymmetric timeliness and for interpreting it as a measure of conditional conservatism.     

Conditional regression analysis

This article deals with the question of whether the inclusion of multiplicative terms to model conditional effects in multiple regression is legitimate. The major arguments in the controversy relating to this subject are reviewed. The main conclusion is that most of the objections against multiplicative terms are based on misinterpretations of the coefficients of conditional models. For the often-ignored possible numerical problems in the estimation of these models, due to multicollinearity, an indirect estimation technique is proposed. The potentials of conditional regression analysis are demonstrated on a concrete example.     

Conditional Probabilistic Population Forecasting

Since policy-makers often prefer to think in terms of alternative scenarios, the question has arisen as to whether it is possible to make conditional population forecasts in a probabilistic context. This paper shows that it is both possible and useful to make these forecasts. We do this with two different kinds of examples. The first is the probabilistic analog of deterministic scenario analysis. Conditional probabilistic scenario analysis is essential for policy-makers because it allows them to answer "what if" type questions properly when outcomes are uncertain. The second is a new category that we call "future jump-off date forecasts". Future jump-off date forecasts are valuable because they show policy-makers the likelihood that crucial features of today's forecasts will also be present in forecasts made in the future.     

On Conditional Density Estimation

With the aim of mitigating the possible problem of negativity in the estimation of the conditional density function, we introduce a so-called re-weighted Nadaraya-Watson (RNW) estimator. The proposed RNW estimator is constructed by a slight modification of the well-known Nadaraya-Watson smoother. With a detailed asymptotic analysis, we demonstrate that the RNW smoother preserves the superior large-sample bias property of the local linear smoother of the conditional density recently proposed in the literature. As a matter of independent statistical interest, the limit distribution of the RNW estimator is also derived.     

Adaptive Estimation of a Conditional Density

The authors consider the problem of estimating a conditional density by a conditional kernel density estimate when the error associated with the estimate is measured by the L₁‐norm. On the basis of the combinatorial method of Devroye and Lugosi ( 1996 ), they propose a method for selecting the bandwidths adaptively and for providing a theoretical justification of the approach. They use simulated data to illustrate the finite‐sample performance of their estimator.     

Conditional moment models under semi-strong identification

We consider conditional moment models under semi-strong identification. Identification strength is directly defined through the conditional moments that flatten as the sample size increases. Our new minimum distance estimator is consistent, asymptotically normal, robust to semi-strong identification, and does not rely on the choice of a user-chosen parameter, such as the number of instruments or some smoothing parameter. Heteroskedasticity-robust inference is possible through Wald testing without prior knowledge of the identification pattern. Simulations show that our estimator is competitive with alternative estimators based on many instruments, being well-centered with better coverage rates for confidence intervals.     

Conditional Demands and Marginal Tax Reform

This paper examines Irish demand patterns using conditional demand functions. This overcomes the problems faced by traditional demand analysis which neglects the influence of labour supply and thus assumes weak separability. The conditional approach allows for more exact tests of weak separability using more flexible functional forms than is possible when estimating an unconditional commodity demand–labour supply model. The impact of the conditioned demand responses and the relaxation of weak separability on measures of marginal tax reform is examined.     

Conditional empirical likelihood for quantile regression models

In this paper, we propose a new Bayesian quantile regression estimator using conditional empirical likelihood as the working likelihood function. We show that the proposed estimator is asymptotically efficient and the confidence interval constructed is asymptotically valid. Our estimator has low computation cost since the posterior distribution function has explicit form. The finite sample performance of the proposed estimator is evaluated through Monte Carlo studies.     

Time Series Concepts for Conditional Distributions*

The paper asks the question – as time series analysis moves from consideration of conditional mean values and variances to unconditional distributions, do some of the familiar concepts devised for the first two moments continue to be helpful in the more general area? Most seem to generalize fairly easy, such as the concepts of breaks, seasonality, trends and regime switching. Forecasting is more difficult, as forecasts become distributions, as do forecast errors. Persistence can be defined and also common factors by using the idea of a copula. Aggregation is more difficult but causality and controllability can be defined. The study of the time series of quantiles becomes more relevant.     

Conditional distributions and the bivariate normal distribution

Summary An alaysis of the extent to which conditional distributions of a bivariate vector characterize bivariate normality is given.     

Conditional Variances in UK Regional House Prices

Abstract

The returns of house price indices for the 13 UK regions are modelled using time series processes, including conditional variances. The first conclusion is that the UK follows the USA, with some regions displaying time-varying variances and others with constant variances. Secondly, there is limited evidence of an asymmetric component in six of the seven regions displaying autoregressive conditional heteroskedasticity. Thirdly, the results suggest that there are three distinct housing markets in the UK, based on common structures within their mean and variance processes, and that South West England is the region driving the other time-varying variances.

Variances conditionnelles dans les prix régionaux de l'immobilier au Royaume-Uni

Résumé Les résultats de l'indice des prix de l'immobilier pour les 13 régions du Royaume-Uni sont modélisés ici au moyen de procédés de séries chronologiques, y compris des variances conditionnelles. La première conclusion est que le Royaume-Uni suit les États-Unis, certaines régions présentant des variances temporelles, d'autres des variances constantes. Deuxièmement, on relève peu de traces d'un composant asymétrique dans six des sept régions présentant une hétéroscédasticité conditionnelle autorégressive. Troisièmement, les résultats indiquent qu'il y aurait trois marchés de l'immobilier distincts au Royaume-Uni, sur la base de structures communes dans le cadre de leurs procédés moyens et de variance, et que le sud-ouest de l'Angleterre est la région qui dynamise les autres variances temporelles.

Varianzas condicionales en los precios regionales de la vivienda en el Reino Unido

Extracto Las cifras de los índices de precios de la vivienda en 13 regiones del Reino Unido se modelan utilizando procesos de series temporales, incluyendo varianzas condicionales. La primera conclusión es que el Reino Unido sigue a los EE UU, con varias regiones que muestran varianzas fluctuantes con el tiempo y otras con varianzas constantes. En segundo lugar, existe evidencia limitada de un componente asimétrico en seis de las siete regiones que muestran una heteroesquedacidad condicional autorregresiva. En tercer lugar, los resultados sugieren que existen tres mercados distintivos de la vivienda en el Reino Unido, basados en estructuras comunes dentro de sus procesos de media y varianza, y que el sudoeste de Inglaterra es la región que dirige las otras varianzas fluctuantes con el tiempo.

     

条件自回归极差模型与波动率估计

无论是在理论研究领域还是在应用领域,波动率的预测已经成为现代金融经济学和金融工程的重要课题。Chou（2005）针对极差提出了条件自回归极差模型（CARR）。本文在Parkinson（1980）的基础上,对极差作出了一个简单的修正,使得相应的CARR模型成为标准差的动态模型;然后以上证指数2001年4月27日至2005年12月5日的周收益率数据为样本,采用滚动样本的方法,利用CARR模型和GARCH模型分析了样本数据,作出了上证指数波动率样本外1至8周的预测,在多种事后波动率的测度下比较了修正后的CARR模型与GARCH模型对上证指数波动率的预测能力,证实了CARR模型在理论上的有效性。     

Modelling Regional Welfare Efficiency Applying Conditional Full Frontiers

Abstract

This paper demonstrates an evaluation of welfare policies and regional allocation of public investment using the recent developments in efficiency analysis and statistical inference. Specifically, the efficiency of the welfare policies of the Greek prefectures for the census years of 1980, 1990 and 2000 are compared and analyzed. The paper, using bootstrap techniques on unconditional and conditional full frontier applications, indicates that there are major welfare inefficiencies among the prefectures over the three census years. The analysis reveals that the increase of population density over the years has a negative impact on the welfare efficiency levels of the Greek prefectures.

RÉSUMÉ Cette communication démontre une évaluation des politiques sociales et de l'affectation régionale d'investissements publics, sur la base de développements récents dans l'analyse du rendement et des conclusions statistiques. Plus spécifiquement, nous comparaisons et nous analysons l'efficacité des politiques sociales des préfectures grecques pour les années de recensement 1980, 1990 et 2000. En appliquant des techniques de rééchantillonnage à des applications à frontières intégrales inconditionnelles et conditionnelles, la communication démontre que les années de recensement ont été marquées par l'existence d'une grande inefficacité sur le plan social, parmi les préfectures. L'analyse révèle que l'augmentation de la densité de la population a, au fil des années, eu un effet négatif sur l'efficacité des préfectures de la Grèce sur le plan social.

R esumen Este estudio demuestra una evaluación de las políticas de beneficios sociales y la distribución regional de la inversión pública usando los recientes desarrollos en análisis de eficiencia e inferencia estadística. Específicamente, se compara y analiza la eficiencia de las políticas de beneficios sociales de las prefecturas griegas en los años de censo 1980, 1990 y 2000. El estudio que usa técnicas bootstrap en las aplicaciones de frontera completa incondicionales y condicionales indica que existen importantes ineficiencias en los beneficios sociales entre las prefecturas durante los tres años censados. El análisis revela que el aumento de la densidad de la población a través de los años tiene un impacto negativo en los niveles de eficiencia de los beneficios sociales en las prefecturas griegas.

     

On the Conditional Variance of Fuzzy Random Variables

The conditional variance of random variables plays an important role for well-known variance decomposition formulas. In this paper, the conditional variance is defined for fuzzy random variables and some properties are proved, which especially generalize to the mentioned variance decomposition. Moreover, results for two special types of fuzzy random variables and an outlook for possible applications are presented.     

Measuring Conditional Persistence in Nonlinear Time Series*

The persistence properties of economic time series have been a primary object of investigation in a variety of guises since the early days of econometrics. Recently, work on nonlinear modelling for time series has introduced the idea that persistence of a shock at a point in time may vary depending on the state of the process at that point in time. This article suggests investigating the persistence of processes conditioning on their history as a tool that may aid parametric nonlinear modelling. In particular, we suggest that examining the nonparametrically estimated derivatives of the conditional expectation of a variable with respect to its lag(s) may be a useful indicator of the variation in persistence with respect to its past history. We discuss in detail the implementation of the measure and present a Monte Carlo investigation. We further apply the persistence analysis to real exchange rates.     

Beta与收益的条件关系分析--基于上海股市的实证检验:1997-2001

许多实证研究表明,单个股票的Beta和其收益之间并没有显著的相关关系,由此否认传统CAPM模型所揭示的风险-收益关系.但在所用模型中的预期收益和实证检验中运用的已实现收益是两个不同的概念,本文运用Pettengill et al提出的条件CAPM模型对上海股市重新作检验,研究发现在牛市中Beta和收益呈现出显著的正相关关系,在熊市中Beta和收益呈现出显著的负相关关系,且两者显示出一定的对称性.研究结果表明,在上海股市中条件CAPM模型具有相当的应用空间.     

自回归条件异方差模型的研究分析

自回归条件异方差（ARCH）模型适用于对具有群集性和方差时变性特点的经济类时间序列数据的回归分析和预测。本文对ARCH模型中待定参数的确定进行了详细推导；探讨了对ARCH模型扰动影响的敏感性进行分析计算的方法；并实例应用ARCH模型对股票收盘价格的全年变动进行预测，研究分析其特点。     

Conditional predictive density evaluation in the presence of instabilities

We propose new methods for evaluating predictive densities. The methods include Kolmogorov–Smirnov and Cramér–von Mises-type tests for the correct specification of predictive densities robust to dynamic mis-specification. The novelty is that the tests can detect mis-specification in the predictive densities even if it appears only over a fraction of the sample, due to the presence of instabilities. Our results indicate that our tests are well sized and have good power in detecting mis-specification in predictive densities, even when it is time-varying. An application to density forecasts of the Survey of Professional Forecasters demonstrates the usefulness of the proposed methodologies.     

Conditional distributions of generalized order statistics and some characterizations

Generalized order statistics have been introduced in Kamps (1995a). They enable a unified approach to several models of ordered random variables, e.g. (ordinary) order statistics, record values, sequential order statistics, record values from non-identical distributions. The purpose of this paper is to develop conditional distributions of one generalized order statistic given another and to characterize the underlying continuous distribution by different conditional expectations. Well-known results for ordinary order statistics and record values are extended to generalized order statistics. Received: July 1997