Efficient Estimation of Additive Partially Linear Models

I consider the problem of estimating an additive partially linear model using general series estimation methods with polynomial and splines as two leading cases. I show that the finite-dimensional parameter is identified under weak conditions. I establish the root-n-normality result for the finite-dimensional parameter in the linear part of the model and show that it is asymptotically more efficient than a semiparametric estimator that ignores the additive structure. When the error is conditional homoskedastic, my finite-dimensional parameter estimator reaches the semiparametric efficiency bound. Efficient estimation when the error is conditional heteroskedastic is also discussed.     

面向产品创新要求的协同创新客户选择

现有协同创新客户选择方法主要从客户自身特点出发,未充分考虑产品创新要求的影响。鉴于此,提出了综合考虑产品创新要求和客户自身特点的协同创新客户选择方法,建立了产品创新要求和选择协同创新客户的指标之间的质量屋模型;基于模糊加权平均法和-截集确定评价指标权重,实现了指标权重随不同产品创新要求的动态调整;基于粗数方法提出了客户数量较多情况下确定客户在各指标上评价值的方法,计算客户评价值。案例分析结果表明,该方法具有合理性和可行性。     

The evolution of conventions in minimum effort games

The literature on minimum effort game has been concerned with a symmetric game with linear payoff functions. The main aim of the present paper is to study the coordination problem arising in a not necessarily symmetric minimum effort game with two players. The sources of asymmetry can be twofold: the productivity of effort and the distribution of the join output. To select among the Pareto ranked equilibria we use the stochastic stability criterion. We show that, for any configuration parameters, the set of stochastically stable equilibria coincides with the set of potential maximizers. We also show that when the disutility of effort is linear, the Pareto dominant equilibrium is stochastically stable provided that the distributive parameter belongs to a well defined range. When the disutility of effort is nonlinear no distributive arguments can be used to successfully affect the selection process. Lastly we prove that the connection between stochastic stability and maximum potential can fail when more than two agents are considered.     

DSGE models,detrending, and the method of moments

One important question in the DSGE literature is whether we should detrend data when estimating the parameters of a DSGE model using the moment method. It has been common in the literature to detrend data in the same way the model is detrended. Doing so works relatively well with linear models, in part because in such cases the information that disappears from the data is usually related to the parameters that also disappear from the detrended model. Unfortunately, in heavy non‐linear DSGE models, parameters rarely disappear from detrended models, but information does disappear from the detrended data. Using a simple real business cycle model, we show that both the moment method estimators of parameters and the estimated responses of endogenous variables to a technological shock can be seriously inaccurate when detrended data are used in the estimation process. Using a dynamic stochastic general equilibrium model and U.S. data, we show that detrending the data before estimating the parameters may result in a seriously misleading response of endogenous variables to monetary shocks. We suggest building the moment conditions using raw data, irrespective of the trend observed in the data.     

A COMOMENT CRITERION FOR THE CHOICE OF RISKY INVESTMENT BY FIRMS*

This article uses Taylor series expansions and the assumption of small risks to derive a comoment criterion that firms should maximize so that the resulting equilibrium is Pareto optimal. This is done in two models of production under uncertainty: the state‐of‐nature model in which the firms’ outputs depend on states of nature and financial markets are complete with respect to these states of nature and the probability model in which the firms’ risky outputs are modeled by their joint probabilities and financial markets span the outcome space of the firms. The comoment criterion provides a unifying framework for the two equilibrium models of production under uncertainty, has the merit of being based on information which is readily available to firms, and provides greater insight than the theoretical criterion into the risk characteristics of its profit stream that a firm should focus on when choosing its investment plan.     

Optimal Forestry Contracts under Asymmetry of Information

In this paper, I analyze optimal royalty contracts in forestry when the harvesting firm has private information on the cost of harvesting. This infinite horizon forest rotation model with asymmetry of information on the cost parameter results in a dynamic incentive problem. Depending on whether the costs are correlated over time or not, the firm either receives rent or receives no rent, associated with the continuation part of the rotation choice. I characterize the optimal contract explicitly in both cases. I also examine the loss in expected welfare surplus resulting from the use of a linear contract instead of the more general non‐linear contract.     

A model selection approach for multiple indicators multiple causes model

This study proposes a model selection approach for determining the inclusion or exclusion of a latent variable when two exogenous and two endogenous variables are provided. The models compared are the multivariate regression model without latent variables (MR model) and the multiple indicators multiple causes model (MIMIC model). The inclusion of a latent variable in the MR model yields the MIMIC model. In the proposed approach, an information criterion is used to select the best model of the two. The efficacy of the proposed approach is examined through two types of simulation studies and empirical analyses of the shadow economy and the fiscal illusion.     

Generalized Spectral Tests for Conditional Mean Models in Time Series with Conditional Heteroscedasticity of Unknown Form

Economic theories in time series contexts usually have implications on and only on the conditional mean dynamics of underlying economic variables. We propose a new class of specification tests for time series conditional mean models, where the dimension of the conditioning information set may be infinite. Both linear and nonlinear conditional mean specifications are covered. The tests can detect a wide range of model misspecifications in mean while being robust to conditional heteroscedasticity and higher order time-varying moments of unknown form. They check a large number of lags, but naturally discount higher order lags, which is consistent with the stylized fact that economic behaviours are more affected by the recent past events than by the remote past events. No specific estimation method is required, and the tests have the appealing „nuisance parameter free” property that parameter estimation uncertainty has no impact on the limit distribution of the tests. A simulation study shows that it is important to take into account the impact of conditional heteroscedasticity; failure to do so will cause overrejection of a correct conditional mean model. In a horse race competition on testing linearity in mean, our tests have omnibus and robust power against a variety of alternatives relative to some existing tests. In an application, we find that after removing significant but possibly spurious autocorrelations due to nonsynchronous trading, there still exists significant predictable nonlinearity in mean for S&P 500 and NASDAQ daily returns.     

我国主要证券市场VaR模型变动性研究

对证券市场风险度量模型的探索,一直是国内外金融风险管理者关注和研究的热点之一。VaR(Value-at-Risk)风险度量模型,目前已成为金融机构、非金融企业、金融监管部门测量和监控市场风险的主流工具。然而VaR模型能否有效正确地度量证券市场风险,不但取决于估计的精度,还取决于选用VaR模型本身的变动性。因此,探索我国主要证券市场VaR模型的变动性,有一定的现实意义。针对我国主要证券市场指数,本文首先通过图形展示了三类(参数、半参数和非参数)VaR估计方法在不同的窗口设定下控制风险的表现;其次在平均相对偏差(MRB)和平方根相对偏差(RMSRB)的双重标准下,对三类VaR估计模型的变动性进行了比较研究,结果表明:在我国主要证券市场上,参数类VaR估计模型本身的变动性和偏离程度较小,半参数类VaR估计模型次之,而非参数类VaR估计模型本身的变动性和偏离程度较大,这在一定程度上符合新兴国家证券市场存在较大投机收益的特点。     

Model selection via genetic algorithms illustrated with cross-country growth data

We provide a new simple procedure for selecting econometric models, which is used to select the regressors of the cross-country growth model regression. This procedure is based on a heuristic approach called genetic algorithms which are used to explore the universe of models made available by a General Unrestricted Model. This search process of the correct model is only guided by the Schwarz information criterion, which acts as the loss function of the genetic algorithm in order to rank the models. Our procedure shows good performance relative to other alternative methodologies when they are compared in a simulation environment. This research was supported by the Spanish Ministry of Science and Education through the project SEJ2006-07701.     

Real wages-employment relationship in Finnish manufacturing: a VAR approach

Granger's concept of causality and the vector autoregressive(VAR) technique is used to investigate the real wages-employment relationship in Finnish manufacturing. The stationarity of the time series is examined and a number of co-integration tests for the adequacy of a pure VAR specification performed. The results using a bivariate VAR model based on a lag structure determined by Akaike's information criterion suggests that real wages Granger-cause employment. The slight non-constancy of the model suggests, however, that the conclusion concerning the nature of the real wages-emploment relationship should be treated with causion.     

两种准则下会计业绩的契约有用性评价--基于B股公司经理人员变更的实证分析

文章通过对决策有用观和契约有用观研究的回顾,指出价值相关性并不是评价会计信息有用性的惟一标准,因此提出评价准则效果的一个新的衡量标准--会计信息的契约有用性.实证部分选取B股上市公司为样本,用契约有用性标准考察在经理人员变更中,国际和国内两种准则哪一种提供的会计业绩更加有用.结果表明:国际准则的业绩对B股公司经理人员的变更具有更强的解释力,这为评价我国两套会计准则的效果提供了新的经验证据.     

Further Results on Bayesian Method of Moments Analysis of the Multiple Regression Model

In this article we extend previous BMOM results by showing how information about a variance parameter and its relation to regression coefficients produces a rich class of postdata densities for regression parameters. Prediction and model selection techniques are also described. We also discuss the well-documented link between cross-entropy and the average log odds and then use this criterion in an experiment to compare results obtained from BMOM and Bayes approaches using data generated from known models.     

Strategic trade policy under asymmetric information with screening

The design of strategic rent-extracting trade policies requires information that may be private, such as the cost structure of an industry or parameters of the demand function. As a consequence, under asymmetric information, the design of these policies is problematic. We propose screening menus consisting of different instruments (tariff vs. quota) designed to solve this informational issue. We first use a simple model that examines a Cournot duopoly between a domestic firm and a foreign firm with linear demand and cost functions, with both firms supplying a homogeneous good on the domestic market. In this scenario, if the government does not have information regarding the demand parameter, which is known by both firms, a menu consisting of a rent-extracting tariff for a low demand parameter and a rent-extracting quota for a high demand parameter maximizes the government's objective function. This menu leads the domestic firm to reveal private information. We then generalize this framework to a scenario with imperfect information regarding the firms' marginal cost. Finally, we discuss the issue of quotas generating public revenues and study the case of a menu consisting of a tariff and a free quota.     

A linear demand system within a seemingly unrelated time series equations framework

A seemingly unrelated time series equations framework for the linear almost ideal (AID) demand system is considered. The framework is applied to a consumer demand system covering nine non-durable commodities. Within a specification where the static linear AID system is augmented by latent variables representing stochastic trends and seasonality, demand homogeneity is tested; both in each equation and in the system as a whole. Income and own-price elasticities are calculated under homogeneity restrictions. Although the homogeneous model is formally rejected by statistical tests, it performs well with respect to interpretability, parameter stability and forecasting.     

Segmented trend modelling of the US GNP series

Structural changes in economic time series are modelled as piecewise linear trends. A statistical method is considered to estimate and select the number and location of trend-breakks simultaneously. The method is applied to the GNP series of the US.     

基于公平与效率均衡的城乡和谐

已有的对城乡和谐发展问题的研究大多将注意力集中在城乡和谐发展的途径方面,却忽视了对城乡和谐衡量标准的研究.本文从公平与效率统一(均衡)的视角来解析城乡和谐的标准以及城乡和谐演进的机制,并构建了一个基于公平与效率均衡的城乡和谐发展模型.该模型揭示,城乡和谐是公平与效率均衡的函数,市场机制与政府制度的相互配合共同促进城乡和谐发展.     

注意力配置对投资决策行为影响的间接检验

现代金融市场中,海量的信息与投资者注意力的有限性使人们不得不有选择性地配置注意力,选择注意是投资者获取投资信息的前提,也是投资决策过程中的一个重要环节,利用我国证券市场相关的实际数据,间接实证注意力配置对投资决策行为的影响。实证结果表明,股评信息确实吸引了投资者注意并作出了反应,股评信息发布的形式、位置、表述方式等与股评内容一样影响着注意力配置的效果。     

A small-sample correction for the likelihood ratio test

Anderson's (1958, p. 208) asymptotic expansion of the likelihood ratio criterion for testing linear hypotheses about regression coefficients in the multivariate linear model is used to derive a small-sample correction factor that may be applied in a more general context.     

Estimation and Forecasting in Models with Multiple Breaks

This paper develops a new approach to change-point modelling that allows the number of change-points in the observed sample to be unknown. The model we develop assumes that regime durations have a Poisson distribution. It approximately nests the two most common approaches: the time-varying parameter (TVP) model with a change-point every period and the change-point model with a small number of regimes. We focus considerable attention on the construction of reasonable hierarchical priors both for regime durations and for the parameters that characterize each regime. A Markov chain Monte Carlo posterior sampler is constructed to estimate a version of our model, which allows for change in conditional means and variances. We show how real-time forecasting can be done in an efficient manner using sequential importance sampling. Our techniques are found to work well in an empirical exercise involving U.S. GDP growth and inflation. Empirical results suggest that the number of change-points is larger than previously estimated in these series and the implied model is similar to a TVP (with stochastic volatility) model.