The cointegrated vector autoregressive model with general deterministic terms

In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model $X_t=\gamma Z_t+Y_t$, where $Z_t$ belongs to a large class of deterministic regressors and $Y_t$ is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression, and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are ${\chi }^2$-distributed.     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.     

The Minimal Entropy Martingale Measures for Exponential Additive Processes

In this paper, we will consider exponential additive processes as a financial market model. Under a mild condition, we will determine the minimal entropy martingale measures (MEMMs) for the exponential additive processes. To this end, we will prepare several results on the exponential moment of additive processes and integrals based on them. As an application of our result, we will deduce optimal strategy for exponential utility maximization problem. We will also investigate our result through several examples, such as time-dependent versions of double Poisson model, Merton model and Kou model.     

金融发展、交通设施与居民消费———基于半参数可加模型的实证分析

本文利用半参数可加模型实证分析了金融发展水平、交通设施条件与中国居民消费的关系,实证结果表明：（１）金融系统的规模扩张与居民消费存在显著的正向线性关系,而金融系统效率的改善对居民消费则主要表现为倒“Ｕ”型的非线性影响。（２）公共交通和道路设施条件与居民消费的线性关系不同,前者为正,而后者为负,但两者的非线性影响大体一致,均表现为“Ｎ”型趋势。并对如何提升居民消费水平提出了相应的政策建议。     

Improving the way land use change is handled in economic models

Sound economic modelling of land use in global economic models is critical for evaluating agricultural, biofuel, and climate policies. Current approaches do not preserve physical land area, do not account for the fact that land is of different qualities, or do not explicitly include the cost of converting land from one use to another. This study proposes a land use modelling framework building on the additive form of the constant elasticity of transformation (ACET) approach. We demonstrated that the framework could (1) directly provide traceable physical land use results, (2) flexibly handle land productivity differences based on biophysical information, (3) explicitly introduce land conversion cost, and (4) provide welfare decomposition in light of land heterogeneity and conversion cost. An experiment of mandating a 10 percent increase in grain consumption in the US food sector showed that ignoring land heterogeneity and conversion cost would underestimate the welfare loss by 28 percent.     

基于广义可加模型的跨境人民币流动影响因素实证分析

利用广义可加模型对影响跨境人民币流动的因素进行理论和实证分析,发现贸易进出口总额对跨境人民币流动的线性作用与非线性作用不一样.随着一国贸易进出口总额的增加,对跨境人民币流动的非线性影响呈现由正向转为负向的趋势;人民币的有效汇率对于会造成跨境人民币流动水平小幅波动;资本市场的发展水平对于跨境人民币流动的影响是水平线性的.最后,本文比较了广义可加模型与多元线性回归模型、协整模型的差异,体现了广义可加模型用于经济领域研究的优势.     

A general class of additively decomposable inequality measures

Summary. This paper presents and characterizes a two-parameter class of inequality measures that contains the generalized entropy measures, the variance of logarithms, the path independent measures of Foster and Shneyerov (1999) and several new classes of measures. The key axiom is a generalized form of additive decomposability which defines the within-group and between-group inequality terms using a generalized mean in place of the arithmetic mean. Our characterization result is proved without invoking any regularity assumption (such as continuity) on the functional form of the inequality measure; instead, it relies on a minimal form of the transfer principle – or consistency with the Lorenz criterion – over two-person distributions. Received: October 27, 1997; revised: March 25, 1998     

Estimation of hedonic price functions via additive nonparametric regression

We model a hedonic price function for housing as an additive nonparametric regression. Estimation is done via a backfitting procedure in combination with a local polynomial estimator. It avoids the pitfalls of an unrestricted nonparametric estimator, such as slow convergence rates and the curse of dimensionality. Bandwidths are chosen using a novel plug in method that minimizes the asymptotic mean average squared error (AMASE) of the regression. We compare our results to alternative parametric models and find evidence of the superiority of our nonparametric model. From an empirical perspective our study is interesting in that the effects on housing prices of a series of environmental characteristics are modeled in the regression. We find these characteristics to be important in the determination of housing prices.First version received: October 2002/Final version received: October 2003We thank B. Baltagi and two anonymous referees for their comments. The authors retain responsibility for any remaining errors.     

An empirical note about additive outliers and nonstationarity in Latin-American inflation series

This note shows the empirical dangers of the presence of large additive outliers when testing for unit roots using standard unit root statistics. Using recent proposed procedures applied to four Latin-American inflation series, I show that the unit root hypothesis cannot be rejected.Jel classification: C2, C3, C5I want to thank Pierre Perron for useful comments on a preliminary version of this paper. Helpful comments from an anonymous referee, and Yiagadeesen Samy are appreciated. I thank the Editor Baldev Raj for useful comments about the final structure of this paper. Finally, I also thank André Lucas for helpful suggestions concerning the use of his nice computer program Robust Inference Plus Estimation (RIPE).First revision received: August 2001/Final revision received: December 2002     

Additive Structural Decomposition Analysis and Index Number Theory: An Empirical Application of the Montgomery Decomposition

In recent years, a large number of empirical articles on structural decomposition analysis, which aims at disentangling an aggregate change in a variable into its r factors, has been published in this journal. Commonly used methods are the average of the two polar decompositions and the average of all r! elementary decompositions (Dietzenbacher and Los, 1998 Dietzenbacher, E. and Los, B. 1998. Structural decomposition techniques: sense and sensitivity. Economic Systems Research, 10: 307–323. [Taylor & Francis Online] [Google Scholar], D&L). We propose to use instead the ‘ideal’ Montgomery decomposition, which means that it satisfies the requirement of factor reversal imposed in index number theory. We prefer it to the methods previously mentioned. The average of the two polar decompositions is not ‘ideal’, so that the outcome depends on the ordering of the factors. The average of all elementary decompositions is ‘ideal’, but requires the computation of an ever increasing number of decompositions when the number of factors increases. Application to the example of D&L (four factors) shows that the three methods yield results that are close to each other.