Permanent Income,Current Income,and Consumption

This article reexamines the consistency of the permanent-income hypothesis with aggregate postwar U.S. data. The permanent-income hypothesis is nested within a more general model in which a fraction of income accrues to individuals who consume their current income rather than their permanent income. This fraction is estimated to be about 50%, indicating a substantial departure from the permanent-income hypothesis. Our results cannot be easily explained by time aggregation or small-sample bias, by changes in the real interest rate, or by nonseparabilities in the utility function of consumers.     

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Many of the recently developed alternative ecocometric approaches to the construction and estimation of life-cycle consistent models using individual data can be viewed as alternative choices for conditioning variables that summarise past decisions and future anticipations. By ingenious choice of this conditioning variable and by exploitation of the duality relationships between the alternative specifications, many currently available micro-data sets can be used for the estimation of life-cycle consistent models. In reviewing the alternative approaches their stochastic properties and implicit preference restrictions are highlighted. Indeed, empirical specifications that are parameterised in a form of direct theoretical interest often can be shown to be unnecessarily restrictive while dual representations may provide more flexible econometric models. These results indicate the particular advantages of different types of data in retrieving life-cycle consistent preference parameters and the appropriate, most flexible, econometric approach for each type of data. A methodology for relaxing the intertemporal separability assumption is developed and the advantages and disadvantages of alternative approaches in this framework are considered.     

Econometric approaches to the specification of life-cycle labour supply and commodity demand behaviour

Many of the recently developed alternative ecocometric approaches to the construction and estimation of life-cycle consistent models using individual data can be viewed as alternative choices for conditioning variables that summarise past decisions and future anticipations. By ingenious choice of this conditioning variable and by exploitation of the duality relationships between the alternative specifications, many currently available micro-data sets can be used for the estimation of life-cycle consistent models. In reviewing the alternative approaches their stochastic properties and implicit preference restrictions are highlighted. Indeed, empirical specifications that are parameterised in a form of direct theoretical interest often can be shown to be unnecessarily restrictive while dual representations may provide more flexible econometric models. These results indicate the particular advantages of different types of data in retrieving life-cycle consistent preference parameters and the appropriate, most flexible, econometric approach for each type of data. A methodology for relaxing the intertemporal separability assumption is developed and the advantages and disadvantages of alternative approaches in this framework are considered.     

Posterior consistency for the spectral density of non-Gaussian stationary time series

Various nonparametric approaches for Bayesian spectral density estimation of stationary time series have been suggested in the literature, mostly based on the Whittle likelihood approximation. A generalization of this approximation involving a nonparametric correction of a parametric likelihood has been proposed in the literature with a proof of posterior consistency for spectral density estimation in combination with the Bernstein–Dirichlet process prior for Gaussian time series. In this article, we will extend the posterior consistency result to non-Gaussian time series by employing a general consistency theorem for dependent data and misspecified models. As a special case, posterior consistency for the spectral density under the Whittle likelihood is also extended to non-Gaussian time series. Small sample properties of this approach are illustrated with several examples of non-Gaussian time series.     

经济增速放缓与经济波动对居民福利影响的阶段差异分析

 本文基于中国1952-2007年时序数据定量研究经济增速放缓的福利损失和经济波动的福利损失,并侧重考察两种福利损失的大小关系在改革开放和经济体制改革目标确立前后的阶段差异。研究发现,无论是总体而言,还是在中国经济发展的不同阶段,经济波动的福利损失并不必然远小于经济增速放缓的福利损失,在相关参数的合理取值范围内,经济波动的福利损失大于经济增速放缓的福利损失是相当普遍的情形。因此,中国政府部门在重视长期经济增长的同时,不能草率否定短期经济稳定的重要性。     

Estimating Intertemporal and Intratemporal Substitutions When Both Income and Substitution Effects Are Present: The Role of Durable Goods

Homotheticity induces a dramatic statistical bias in the estimates of the intratemporal and intertemporal substitutions. I find potent support in favor of nonhomotheticity in aggregate consumption data, with nondurable goods being necessities and durable goods luxuries. I obtain the intertemporal substitutability negligible (0.04), a magnitude close to Hall’s (1988) original estimate, and the intratemporal substitutability between nondurable goods and service flow from the stock of durable goods small as well (0.18). Despite that, due to the secular decline of the rental cost, the budget share of durable goods appears trendless.     

Yule–Walker type estimators in periodic bilinear models: strong consistency and asymptotic normality

This paper studies the covariance structure and the asymptotic properties of Yule–Walker (YW) type estimators for a bilinear time series model with periodically time-varying coefficients. We give necessary and sufficient conditions ensuring the existence of moments up to eighth order. Expressions of second and third order joint moments, as well as the limiting covariance matrix of the sample moments are given. Strong consistency and asymptotic normality of the YW estimator as well as hypotheses testing via Wald’s procedure are derived. We use a residual bootstrap version to construct bootstrap estimators of the YW estimates. Some simulation results will demonstrate the large sample behavior of the bootstrap procedure.     

Modeling nonlinear time series with local mixtures of generalized linear models

The authors consider a novel class of nonlinear time series models based on local mixtures of regressions of exponential family models, where the covariates include functions of lags of the dependent variable. They give conditions to guarantee consistency of the maximum likelihood estimator for correctly specified models, with stationary and nonstationary predictors. They show that consistency of the maximum likelihood estimator still holds under model misspecification. They also provide probabilistic results for the proposed model when the vector of predictors contains only lags of transformations of the modeled time series. They illustrate the consistency of the maximum likelihood estimator and the probabilistic properties via Monte Carlo simulations. Finally, they present an application using real data.     

能源消费与经济增长协整分析：基于宏观数据的比较分析

在综合比较分析关于中国GDP和能源消费的各种宏观统计数据的基础上,选定以Maddison估算的以1987年不变价格衡量的GDP和以IEA估算的能源消费为分析的基础数据,通过单位根检验、协整检验、格兰杰因果分析等分析技术发现：中国GDP和能源消费数据是一阶单整的,存在协整关系,并表现为从经济增长到能源消费的单向因果关系。而进一步的误差修正模型分析结果显示：经济增长和能源消费存在长期的均衡关系,GDP每增加1个百分点,能源消费将提高0．811个百分点;相反,能源消费增加一个百分点,GDP将提高0．194个百分点。这表明中国经济增长与能源消费之间存在密切的关系,能源消费并不是经济增长的一个强外生变量。     

Humps and Bumps in Lifetime Consumption

In this article we argue that the life-cycle model that allows demographics to affect household preferences and relaxes the assumption of certainty equivalence can generate hump-shaped consumption profiles over age that are very similar to those observed in household-level data sources and, in particular, match the differences in shape across different education groups. Liquidity constraints or myopia are not required to explain the empirical features of observed life-cycle patterns.     

Nonparametric estimation of the hazard function under dependence conditions

The estimation of the hazard rate has a great number of practical appli¬cations in dependence situations (seismicity analysis, reliability, economics), Based on kernel estimates of the density and the distribution function, we study the properties of the nonparametric estimator of the hazard function as-sociated with a strongly mixing time series. We prove consistency and asymp¬totic normality properties, and a cross-validation method for the smoothing parameter selection is studied. Some simulations and a practical application to real data are also shown.     

Quantile Regression for Location‐Scale Time Series Models with Conditional Heteroscedasticity

This paper considers quantile regression for a wide class of time series models including autoregressive and moving average (ARMA) models with asymmetric generalized autoregressive conditional heteroscedasticity errors. The classical mean‐variance models are reinterpreted as conditional location‐scale models so that the quantile regression method can be naturally geared into the considered models. The consistency and asymptotic normality of the quantile regression estimator is established in location‐scale time series models under mild conditions. In the application of this result to ARMA‐generalized autoregressive conditional heteroscedasticity models, more primitive conditions are deduced to obtain the asymptotic properties. For illustration, a simulation study and a real data analysis are provided.     

Robust estimation of AR coefficients under simultaneously influencing outliers and missing values

A family of robust estimators for coefficients of Gaussian AR(p) time series under simultaneously influencing distortions of two types: outliers and missing values, is proposed. The estimators are based on special properties of the Cauchy probability distribution; consistency and the asymptotic normality of these estimators are proven. An approximate solution of the problem of minimization of the asymptotic variance within the proposed family of estimators is found. Performance of the proposed estimators is illustrated for simulated time series and for real data sets.     

Robust recursive estimation in nonlinear time series

Aase (1983) has dealt with recursive estimation in nonlinear time series of autoregressive type including its asymptotic properties. This contribution modifies the results for the case of nonlinear time series with outliers using the principle of M-estimation from robust statistics. Strong consistency of the robust recursive estimates is preserved under corresponding assumptions. Several types of such estimates are compared by means of a numerical simulation.     

生命不确定性的跨期最优消费行为研究

 本文首先根据中国收入分配制度的特点将消费者的生命周期区分为退休前和退休后，分别设置新古典经济学生命周期消费(Life-Cycles)理论的跨期预算约束；在生命不确定性的假设下，推导出了消费者的最优消费路径。而且，依据1988年1月-2008年12月天津市的城市住户调查数据，利用动态伪面板数据模型的实证分析为理论结果提供了经验证据。另外，实证研究发现，（1）对于户主出生于1965年前的家庭，他们依然延续了传统的消费习惯，完善社会保障机制、转变他们的消费观念是拉动内需的必由之路；（2）跨期替代弹性接近于零（0.008），即城镇居民更倾向于即期消费；（3）期望通过货币政策的利率工具刺激城镇居民消费的作用很有限。     

Testing for spurious and cointegrated regressions: A wavelet approach

This paper proposes a wavelet-based approach to analyze spurious and cointegrated regressions in time series. The approach is based on the properties of the wavelet covariance and correlation in Monte Carlo studies of spurious and cointegrated regression. In the case of the spurious regression, the null hypotheses of zero wavelet covariance and correlation for these series across the scales fail to be rejected. Conversely, these null hypotheses across the scales are rejected for the cointegrated bivariate time series. These nonresidual-based tests are then applied to analyze if any relationship exists between the extraterrestrial phenomenon of sunspots and the earthly economic time series of oil prices. Conventional residual-based tests appear sensitive to the specification in both the cointegrating regression and the lag order in the augmented Dickey–Fuller tests on the residuals. In contrast, the wavelet tests, with their bootstrap t-statistics and confidence intervals, detect the spuriousness of this relationship.     

A SCORE TEST FOR SEASONAL FRACTIONAL INTEGRATION AND COINTEGRATION

This paper develops a time domain score statistic for testing fractional integration at zero and seasonal frequencies in quarterly time series models. Further, it introduces the notion of fractional cointegration at different frequencies between two seasonally integrated, I(1) series. In testing problems involving seasonal fractional cointegration, it is argued that the alternative hypothesis is one-sided for which the usual score test may not be appropriate. Therefore, based on ideas in Silvapulle and Silvapulle (1995), a one-sided score statistic is constructed. A simulation study finds that the score statistic generally has desirable size and power properties in moderately sized samples. The score test is applied to the quarterly Australian consumption function. The income and consumption series are found to be I(1) at zero and seasonal frequencies and these two series are not cointegrated at any frequency.     

Testing for a Unit Root in a Time Series With a Changing Mean

This study considers testing for a unit root in a time series characterized by a structural change in its mean level. My approach follows the “intervention analysis” of Box and Tiao (1975) in the sense that I consider the change as being exogenous and as occurring at a known date. Standard unit-root tests are shown to be biased toward nonrejection of the hypothesis of a unit root when the full sample is used. Since tests using split sample regressions usually have low power, I design test statistics that allow the presence of a change in the mean of the series under both the null and alternative hypotheses. The limiting distribution of the statistics is derived and tabulated under the null hypothesis of a unit root. My analysis is illustrated by considering the behavior of various univariate time series for which the unit-root hypothesis has been advanced in the literature. This study complements that of Perron (1989), which considered time series with trends.     

Resampling Methods for Time Series Level Crossings

We present an application of subsampling and bootstrap methods for time series to determine the distribution of the estimator of zero crossings. The zero crossings method provides an alternative estimator of the lag-1 autocorrelation coefficient that is reducing the data storage requirements and is more robust with respect to outliers when compared to the classical estimator. The main results here are showing the consistency of subsampling, the consistency of moving block bootstrap, the consistency of non overlapping block bootstrap and the consistency of stationary bootstrap for this estimator. Theorems are formulated for Gaussian processes, elliptically symmetric processes and processes which are transformed Gaussian processes. Theoretical results are illustrated by simulations and practical data analysis. We have also shown that in practice the MBB method behaves better than the subsampling method.     

Nonparametric estimation of density derivatives of dependent data

We study here the kernel type, nonparametric estimation of the derivatives of the density function associated with a strongly mixing time series. The consistency and asymptotic normality properties are studied and a method for the selection of the smoothing parameter by means of the modification of the least-squares cross-validation procedure is proposed.