Single Beta Models and Currency Futures Prices

The conditional capital asset pricing model is applied to foreign currency futures prices, covariance risk being measured relative to excess returns from a broadly diversified international portfolio of equities. Positive time-varying risk premia are found in all five currencies tested when the difference between the US and the average foreign interest rates is used as an instrumental variable for the expected excess return from the common stock portfolio.     

Testing For Efficient Contracts In Unionized Labour Markets

This paper addresses the design of empirical tests to distinguish between two competing explanations of wage and employment determination in unionized labour markets, the labour-demand and efficient-contract models. We argue that most of the tests employed are restrictive, propose an alternative non-nested approach, a central feature of which is the variation in the set of instrumental variables across the models, and provide an illustration of how it might be implemented, using data from the Workplace Industrial Relations Survey (WIRS) 1984 Panel File. The results demonstrate how the traditional approach can lead to inappropriate conclusions, and thereby emphasize the empirical importance of the specification of the instrumental variables.     

Alternative Techniques for Estimation of Cross-Section Gravity Models

This paper compares four different estimators with respect to their suitability for cross‐section gravity model estimation. In many circumstances, a Hausman–Taylor approach can be recommended. This framework may provide consistent parameter estimates, when OLS or the traditional random‐effects model are biased. In contrast to the fixed‐effects approach, it allows to estimate parameters of variables such as GDP or GDP per capita, which vary only in a single dimension. The Hausman–Taylor model deserves attention in the estimation of cross‐sectional gravity models.     

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.     

Deriving and Testing Efficient Estimates of WTP Distributions in Destination Choice Models

Estimation of welfare measures is often a dominant driver in the empirical literature on nonmarket valuation. To this end, qualitative choice models based on random utility theory have been widely employed in outdoor recreation studies. A frequent goal of applied studies has been the estimation of welfare changes associated with site attribute changes at recreation sites in order to inform regulatory policy and resource management. We review the evolution of the methodology of random utility theory in this field with a focus on taste heterogeneity models and then focus on the recent proposal of specifying utility in the WTP-space (Train K, Weeks M (2005) Discrete choice models in preference space and willing-to-pay space. In: Scarpa R, Alberini A (eds) Applications of simulation methods in environmental and resource economics, chapter 1. Springer, Dordrecht, pp 1–16). Our empirical application is on outdoor alpine recreation data. We emphasize the efficiency and direct testing that using the maximum simulated likelihood estimator affords to practitioners using the WTP-space approach, and illustrate these with examples.     

非洲货币合作模式及其影响

文章回顾了非洲货币合作的主要发展历程,对非洲货币合作组织管理机构的产生和作用等方面进行比较,认为非洲货币合作的模式是世界上惟一一个融合不同发展水平国家的地区性货币体系,它以一种国家责任的方式实施对区域货币的监管,是不发达地区金融合作体系的典范。     

Testing the Efficient Markets Hypothesis with Futures Markets Data: Forecast Errors, Model Predictions and Live Cattle

At the forefront of empirical research into the examination of the efficiency of futures commodity markets, two fundamentally different testing techniques have been popularised – the 'forecast error' and 'model prediction' approaches. This paper assesses the relative strengths of these techniques by contrasting results obtained when both approaches are used to examine the efficiency of the Sydney live cattle futures market. While neither model provides evidence to suggest that this market is inefficient, it is clearly shown that the model prediction approach enjoys a number of distinct advantages over its rival. Indeed, the model prediction approach provides additional information that is important not only for those interested in testing the efficiency of futures markets, but is important for anyone interested in developing a greater understanding of the determination of prices and the behaviour of agents in futures markets.     

On Efficient Estimation and Correct Inference in Models with Generated Regressors: a General Approach

Two approaches have been developed for deriving the properties of efficiency and consistency of standard errors of two step estimators of linear models containing current or lagged unobserved expectations of a single variable. One method is based on the derivatives of the likelihood function and information matrix, while the other uses the true covariance matrix of the disturbance vector when unknown parameters or variables are replaced by corresponding estimates. In this paper, the second approach is extended to cases where the structural equation is nonlinear and the model contains expectations of more than one variable or expectations of future variables. The properties of a frequently used estimator to deal with missing observations problems, a model involving a variance as an explanatory variable, and a recently developed estimator for autoregressive moving average models can be easily derived using the results of the paper. Methods for improving the efficiency of two step estimators are outlined.
JEL Classification Number: C13     

Simulated Non-Parametric Estimation of Dynamic Models

This paper introduces a new class of parameter estimators for dynamic models, called simulated non-parametric estimators (SNEs). The SNE minimizes appropriate distances between non-parametric conditional (or joint) densities estimated from sample data and non-parametric conditional (or joint) densities estimated from data simulated out of the model of interest. Sample data and model-simulated data are smoothed with the same kernel, which considerably simplifies bandwidth selection for the purpose of implementing the estimator. Furthermore, the SNE displays the same asymptotic efficiency properties as the maximum-likelihood estimator as soon as the model is Markov in the observable variables. The methods introduced in this paper are fairly simple to implement, and possess finite sample properties that are well approximated by the asymptotic theory. We illustrate these features within typical estimation problems that arise in financial economics.     

Alternative Models of Recreational Off-Highway Vehicle Site Demand

A controversial recreation activity is off-highway vehicle use. Off-highway vehicle use is controversial because it is incompatible with most other activities and is extremely hard on natural eco-systems. This study estimates utility theoretic incomplete demand systems for four off-highway vehicle sites. Since two sets of restrictions are equally consistent with utility theory both are imposed and the best fitting restrictions are identified using Voung’s non-nested testing scheme. The demand system is modeled using both Poisson and negative binomial II distributions. Data are provided by a survey conducted at four recreational off-highway vehicle (OHV) sites in western North Carolina.     

Semiparametric Estimation of Rational Expectation Models

In this paper we consider the problem of estimating a semiparametric rational expectation model. We derive the root-n asymptotic normality result of the finite dimensional parameters of the model.

     

Nonparametric Estimation of Sample Selection Models

Sample selection models provide an important way of accounting for economic decisions that combine discrete and continuous choices and of correcting for nonrandom sampling. Nonparametric estimators for these models are developed in this paper. These can be used for estimating shapes and important economic quantities, as in standard nonparametric regression. Endogeneity of regressors of interest is allowed for. Series estimators for these models are developed, which are useful for imposing additivity restrictions that arise from selection corrections. Convergence rates and asymptotic normality results are derived. An application to returns to schooling among Australian young females is given.     

Non-Parametric Identification and Estimation of Truncated Regression Models

In this paper, we consider non-parametric identification and estimation of truncated regression models in both cross-sectional and panel data settings. For the cross-sectional case, Lewbel and Linton (2002) considered non-parametric identification and estimation through continuous variation under a log-concavity condition on the error distribution. We obtain non-parametric identification under weaker conditions. In particular, we obtain non-parametric identification through discrete variation under a non-periodicity condition on the hazard function of the error distribution. Furthermore, we show that the presence of continuous regressors may lead to stronger identification results. Our non-parametric estimator is shown to be consistent and asymptotically normal, and outperforms that of Lewbel and Linton (2002) in a simulation study. For the panel data setting, we provide the first systematic treatment of non-parametric identification and estimation of the truncated panel data model with fixed effects by extending our treatment of the cross-sectional case. We also consider various other extensions.     

Testing Equilibrium Models of Regional Disparities

The large and persistent regional disparities of most European economies have been explained as a disequilibrium phenomenon; convergence between backward and successful regions is slow because the equilibrating forces are weak. Recently, two models have emerged where regional disparities are seen as an equilibrium phenomenon; the amenity model, which assumes that high unemployment and low wages reflect favourable living conditions, and the matching model, which views high unemployment as the result of labour market congestion in declining regions. The paper derives some key empirical implications of the models and examines whether the regional pattern of migration, unemployment and wages in Norway conforms with these implications. The results are supportive of the matching model but not of the amenity model.     

Average Derivative Estimation of Hedonic Price Models

Conventional parametric techniques for estimatinghedonic price models require a correct functionalform. In this paper, we side-step this parametricshortcoming by estimating a hedonic price model usingaverage derivative estimation (ADE). Thissemiparametric approach produces robust estimates ofthe marginal effects without assuming a specificfunctional form a priori. In our application ofthe model to a unique data set on Korean home prices,ADE produced estimates consistent with priorexpectations, providing initial evidence that themodel may represent a viable alternative when usingthe hedonic approach.     

A Comparison of Alternative Exchange Rate Forecasting Models*

This paper compares a number of structural and times-series models on the basis of their accuracy in forecasting the A ustralian-US dollar exchange rate out of sample. Purchasing power parity, forward exchange theory, static and dynamic specifications of both the flexible price and sticky price monetary models, and univariate ARIMA models are considered in the paper. Exchange rate forecasts are generated at horizons of one to four quarters. In contrast to overseas results which support the view that the exchange rate follows a random walk, several models in this study are found to generate forecasts superior to the random walk model.     

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.     

Estimation of Discrete Choice Dynamic Programming Models

This study reviews estimation methods for the infinite horizon discrete choice dynamic programming models and conducts Monte Carlo experiments. We consider: the maximum likelihood estimator (MLE), the two‐step conditional choice probabilities estimator, sequential estimators based on policy iterations mapping under finite dependence, and sequential estimators based on value iteration mappings. Our simulation result shows that the estimation performance of the sequential estimators based on policy iterations and value iteration mappings is largely comparable to the MLE, while they achieve substantial computation gains over the MLE by a factor of 100 for a model with a moderately large state space.