A structural time series approach to modelling multiple and resurgent meat scares in Italy

This paper exploits a structural time series approach to model the time pattern of multiple and resurgent food scares and their direct and cross-product impacts on consumer response. A structural time series Almost Ideal Demand System (STS-AIDS) is embedded in a vector error correction framework to allow for dynamic effects (VEC-STS-AIDS). Italian aggregate household data on meat demand is used to assess the time-varying impact of a resurgent BSE crisis (1996 and 2000) and the 1999 Dioxin crisis. The VEC-STS-AIDS model monitors the short-run impacts and performs satisfactorily in terms of residuals diagnostics, overcoming the major problems encountered by the customary vector error correction approach.     

An Evaluation of Alternative Methods of Forecasting Australian Inflation

The purpose of this paper is to evaluate the forecast of Australian inflation based on four alternative procedures: a univariate time series model, an interest rate model, an error correction model and a public survey of inflation forecasts. We derive estimates of expected and unexpected inflation from each of the methods and compare the out-of-sample forecasting results. Based on a range of evaluation criteria, the time series model dominates the other models, with the interest rate model, the error correction model and the survey forecasts following in that order.     

Estimating the Cox,ingersoll and Ross model of the term structure: a multivariate approach

In this paper we suggest a new methodology to estimate the Cox, Ingersoll and Ross model of the term structure. The approach is based on a multivariate non-linear least squares procedure, which allows us to simultaneously take into account the cross-sectional relations which exist among bond prices at each instant of time and the dynamics of each bond price over time. The methodology involves the use of a fairly simple econometric specification and is developed to deal with both the case of independently and identically distributed error terms and the case of autocorrelated error terms. We estimate and test the model using nominal prices of Italian Treasury bonds.     

Testing for individual and time effects in the two-way error component model with time-invariant regressors

In this paper, we focus on testing for individual and time effects in the two-way error component model with time-invariant regressors. We present the so-called FEF estimators when time-invariant regressors are exogenous and the FEF-IV estimators when one or more of time-invariant variables are endogenous, and obtain their asymptotic properties under some mild conditions. In the light of the moment-based test methods of Wu and Li (2014), we construct several tests for the existence of individual and time effects in the two-way error component model with time-invariant regressors. The resulting tests can be shown to have some desired properties as follows: they do not need any distributional assumptions on the error components; they do not require any assumptions on the correlation among the two random effects and the time-varying regressors; they are robust to the presence of one effect when the other one is tested. Simulation study and real data analysis are carried out for illustration of the above.     

The bias of some estimators for panel data models with measurement errors

The paper deals with the (asymptotic) bias in the estimation of regression slope coefficients from panel data observed with error. Unobserved individual and time specific heterogeneity is also assumed. The estimators considered include: the standard ‘within’ and ‘between’ estimators, and estimators based on differences over time. It is shown that in terms of bias, there may be a trade-off between the effect of heterogeneity and of measurement errors. The paper also shows that in situations where the number of observations of each individual is finite (and in practice often small), changes in the correlograms of the measurement error and of the latent exogenous variable may substantially affect the relative bias of the different estimators of the slope coefficient.     

Prediction mean square error for non-stationary multivariate time series using estimated parameters

Previous results concerning the effects of parameter estimation errors on prediction mean square error for stationary time series are extended to the case of non-stationary multivariate time series models. Numerical examples are presented to illustrate the results.     

Rationality and the Risk Premium on the Australian dollar

A Model of the forward rate error of the USD/AUD spot exchange rate is fitted to daily data for the period 15th December 1983 to 31st December 1991. This provides a data set of 2034 daily trading observations. Explanations of the forecast error include a risk premium represented by a constant plus the conditional variance generated from a GARCH (1,1)-M analysis of the error process and information variables in the form of lagged forward rate errors. The following conclusions are drawn form estimates for the full sample: the USD/AUD spot rate is subject to a constant premium: there is little evidence to support a time varying component and the market is influenced by lagged forward errors. Sub period estimation confirms these results, although a time varying premium is evident prior to the February 1985 depreciation. The economic implications of these findings are discussed. [F31]     

A spectral decomposition for structural VAR models

Based on structural VARs, this paper proposes a spectral decomposition which allows to infer the effects of changes in one variable on the other variables in the frequency domain. It is shown that there is a close relationship between this concept and conventional forecast error variance decomposition techniques for VARs. An empirical example demonstrates the usefulness of this additional tool in analyzing the relationships among time series.     

Instrumental variables and wavelet decompositions

The application of wavelet analysis provides an orthogonal decomposition of a time series by time scale, thereby facilitating the decomposition of a data series into the sum of a structural component and a random error component. The structural components revealed by the wavelet analysis yield nearly ideal instrumental variables for variables observed with error and for co-endogenous variables in simultaneous equation models. Wavelets also provide an efficient way to explore the path of the structural component of the series to be analyzed and can be used to detect some specification errors. The methodology described in this paper is applied to the errors in variables problem and simultaneous equations case using some simulation exercises and to the analysis of a version of the Phillips curve with interesting results.     

Robust random effects tests for two-way error component models with panel data

In this paper, two test statistics are constructed respectively for individual and time effects in linear panel data models by comparing estimators of the variance of the idiosyncratic error at different robust levels. The resultant tests are one-sided, and asymptotically normally distributed under the null hypothesis. Power study shows that the tests can detect local alternatives that differ from the null hypothesis at the parametric rate. Due to the first difference and orthogonal transformations used in the construction of variance estimators of the idiosyncratic error, the two proposed tests are robust to the presence of one effect and the possible correlation between the covariates and the error components when the other one is tested. Monte Carlo simulations are carried out to provide evidence on the finite sample properties of the tests.     

员工优势使用如何影响创新行为——组织自尊与积极情绪的链式中介作用

基于自我一致性理论和情感事件理论,以认知—情感系统理论作为影响路径的逻辑脉络,构建员工优势使用通过组织自尊与积极情绪影响员工创新行为的链式中介模型,探讨差错反感文化的边界作用。采用问卷调查法在两个时间点进行数据收集,通过配对得到302份有效数据,相关分析结果表明：员工优势使用正向影响创新行为;组织自尊和积极情绪不仅分别在员工优势使用与创新行为间发挥部分中介作用,而且共同发挥链式中介作用;差错反感文化负向调节员工优势使用与创新行为、组织自尊与创新行为、积极情绪与创新行为的关系。     

指数平滑预测法及其在经济预测中的应用

以1978—2008年的某省职工年平均工资作为样本数据、2009—2010年的数据作为模型检验数据,建立基于时间序列分析的指数平滑预测模型。检验结果表明,指数平滑预测模型对2009年和2010年的预测值与检验样本的实际值之间的相对误差很小（仅为0.015032和0.02207）,预测结果理想。随后,以此模型预测时间序列2012—2015年职工平均工资数据。     

A study of the relationships between the time charter and spot freight rates

A time charter contract is a shipping contract that allows for freight rate risk avoidance and hedging. Defining the relationship between time charter and spot freight rates will illuminate the fluctuation mechanism of the spot freight market. In this article, three types of dry bulk ships – Capsize, Panamax and Supramax – are chosen to investigate the relationship between time charter and spot freight rates and to analyse the price discovery function of time charter contracts. A vector error correction model is developed, and an impulse response function is used to analyse the influence of time charter rates on spot freight rates. Empirical studies indicate that there are two-way lead–lag relationships between the time charter and spot freight rates and that a time charter contract has a price discovery function. Smaller ship sizes and longer durations lead to a stronger price discovery function.     

Fuzzy Chronic Poverty: A Proposed Response to Measurement Error for Intertemporal Poverty Measurement

A number of chronic poverty measures are now empirically applied to quantify the prevalence and intensity of chronic poverty, vis‐à‐vis transient experiences, using panel data. Welfare trajectories over time are assessed in order to identify the chronically poor and distinguish them from the non‐poor, or the transiently poor, and assess the extent and intensity of intertemporal poverty. We examine the implications of measurement error in the welfare outcome for some popular discontinuous chronic poverty measures, and propose corrections to these measures that seeks to minimize the consequences of measurement error. The approach is based on a novel criterion for the identification of chronic poverty that draws on fuzzy set theory. We illustrate the empirical relevance of the approach with a panel dataset from rural Ethiopia and some simulations.     

城市化与能源消费的动态相关性及其传导机制——基于1978年～2008年的实证研究

笔者依据1978年~2008年时间序列数据,实证分析了我国城市化与能源消费之间的动态相关性,并对城市化与能源消费之间的关联效应及其传导机制进行了考察。结果表明,我国城市化与能源消费存在协整关系,短期内存在误差修正机制;城市化对能源消费的影响主要是通过其规模效应和技术效应表现出来,而第三产业的节能效应尚不明显。     

Forecasting with the almost ideal demand system: evidence from some alternative dynamic specifications

The almost ideal demand system is used as a representation of long run demands in discrete time and continuous time error correction models to produce forecasts of budget shares beyond the sample period. The estimated models are subjected to a battery of tests, and an analysis of the forecasts indicates that continuous time adjustment mechanisms, based around fully modified estimates of the long run preference parameters, provide a remarkably accurate method of forecasting budget shares.     

Welfare Measurement and Measurement Error

The approximate effects of measurement error on a variety of measures of inequality and poverty are derived. They are shown to depend on the measurement error variance and functionals of the error–contaminated income distribution, but not on the form of the measurement error distribution, and to be accurate within a rich class of error–free income distributions and measurement error distributions. The functionals of the error–contaminated income distribution that approximate the measurement error induced distortions can be estimated. So it is possible to investigate the sensitivity of welfare measures to alternative amounts of measurement error and, when an estimate of the measurement error variance is available, to calculate corrected welfare measures. The methods are illustrated in an application using Indonesian household expenditure data.     

有偏型EWMA的股指期货保证金设定的实证研究

本文是对建立在正态分布假设的EWMA期货保证金模型的改进.文中引入基于非对称Laplace分布发展起来的有偏型EWMA方法,建立了新的期货交易保证金模型,较好地反映了期货合约价格序列有偏、厚尾的现象.同时采用comish-Fisher(CF)方法确定出期货价格波动系数,降低了预测误差.将此模型应用到股指期货合约保证金水平的测定中,结果表明本研究所建立模型能够节省保证金的收取总量,预测结果较好.     

Dynamic modeling under linear-exponential loss

We develop a methodology of parametric modeling of time series dynamics when the underlying loss function is linear-exponential (Linex). We propose to directly model the dynamics of the conditional expectation that determines the optimal predictor. The procedure hinges on the exponential quasi-maximum likelihood interpretation of the Linex loss and nicely fits the multiplicative error modeling framework. Many conclusions relating to estimation, inference and forecasting follow from results already available in the econometric literature. The methodology is illustrated using data on United States GNP growth and Treasury bill returns.     

MEASUREMENT ERROR IN THE BANK OF ITALY'S SURVEY OF HOUSEHOLD INCOME AND WEALTH

This paper is aimed at evaluating the incidence of measurement error in the Bank of Italy's Survey of Household Income and Wealth (SHIW). In the case of time-invariant variables, we assess the degree of inconsistency of answers given by panel households in subsequent survey waves. For quantities that vary with time, we estimate the incidence of measurement error by decomposing observed variability into true dynamics and error-induced noise. We apply the Heise model or the latent Markov model, depending on whether the data are continuous or categorical. We also present regression models that explain the error-generating process. Our results are relevant to researchers who use SHIW data for economic analysis, but also to data producers involved in similar income and wealth surveys. The methods we describe and test can be employed in a number of contexts to gain better understanding of data-related problems and plans for survey improvement.