An objective Bayesian analysis of common stochastic trends in international stock prices and exchange rates

This paper uses Bayesian methods to analyze unit root and cointegration properties of two different finance data sets. Avoiding the use of subjective prior information, the paper surveys and utilizes several different objective Bayesian methodologies in an investigation of common stochastic trends in international stock markets and in spot and forward exchange rates for several different countries.     

纽约股票市场对中国A股市场的影响

首先，本文考察了纽约股市波动对中国A股市场的影响。一般认为，中国的A股市场由于严格的资本控制而免受外国影响。但是，通过月度、每周、每日的样本数据（1992年到2004年），经过季节性调整和汇率变动调整后，我们发现上海和深圳A股市场的变动与美国股票价格指数变动相一致。其次，我们考察国家贝塔值（country beta）在马尔可夫转换误差修正模型中的动态关系。对中国市场来说，国家贝塔值和错误纠正项的重要性紧密相连。在东亚金融危机之前，美国市场对中国A股市场的影响普遍存在，而东亚金融危机产生后，则是通过国家贝塔值来影响中国股市的收益。     

Evidence on the existence of speculative bubbles in farmland prices

We conduct tests for the contribution of speculative bubbles to farmland prices. These tests are carried out under the hypothesis that farmland investors rationally form expectations. The outcome of tests reported here allows us to infer whether farmland prices are determined by market fundamentals-discounted returns from the highest economic land use-or whether rumors about farmland price movements are self-fulfilling. The tests are stationarity and cointegration tests relating farmland prices to rents. The tests are carried out using data from three farm production regions-the Corn Belt, the Northern Plains, and the Lake States. In each region, we find little evidence to reject the hypothesis that market fundamentals determine farmland prices.     

A model of fractional cointegration, and tests for cointegration using the bootstrap

The paper proposes a framework for modelling cointegration in fractionally integrated processes, and considers methods for testing the existence of cointegrating relationships using the parametric bootstrap. In these procedures, ARFIMA models are fitted to the data, and the estimates used to simulate the null hypothesis of non-cointegration in a vector autoregressive modelling framework. The simulations are used to estimate p-values for alternative regression-based test statistics, including the F goodness-of-fit statistic, the Durbin–Watson statistic and estimates of the residual d. The bootstrap distributions are economical to compute, being conditioned on the actual sample values of all but the dependent variable in the regression. The procedures are easily adapted to test stronger null hypotheses, such as statistical independence. The tests are not in general asymptotically pivotal, but implemented by the bootstrap, are shown to be consistent against alternatives with both stationary and nonstationary cointegrating residuals. As an example, the tests are applied to the series for UK consumption and disposable income. The power properties of the tests are studied by simulations of artificial cointegrating relationships based on the sample data. The F test performs better in these experiments than the residual-based tests, although the Durbin–Watson in turn dominates the test based on the residual d.     

Demand for broad money and expenditure components in Japan: an empirical study

The main objective of the present study is to examine empirically the long-run relation of broad money demand and its determinants in Japan. In contrast with previous study, the present study considers various components of final expenditure demand as determinants that are final consumption goods, expenditure on investment goods and exports. Using quarterly data over the period 1973Q1–2000Q2, the results of the bounds test [J. Appl. Econ. 16 (2001) 289] indicate a stable long-run relationship between demand of real M2 and its determinants. The empirical results also highlight that different domestic demand components yields different effects on Japanese broad money demand behavior. The estimated unrestricted error-correction model appears to track the data well and the results have important policy implications.     

Analysis of cointegrated VARMA processes

VARMA (vector autoregressive moving average) processes are proposed for modelling cointegrated variables. For this purpose the echelon form is combined with the error correction form. Procedures for estimating the Kronecker indices which characterize the echelon form and for specifying the cointegration rank are discussed. The asymptotic distribution of the coefficient estimators is given. An example based o n US macroeconomic data illustrates the procedure and demonstrates its feasibility in practice.     

Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models

Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings.     

资本渐进开放下我国与国际利率的联动分析——基于中国和美国的比较

一般来说,利率在国家间的传导主要有两条渠道:国际贸易间的传导和资本自由流动下的利率平价机制。本文采取分阶段考察,通过建立计量经济学模型,利用单位根检验和协整分析加以验证,发现在资本项目部分开放后,中美利率开始具有联动效应,作用方式又具有自身的特点。目前资本项目开放度不够高是阻碍我国同世界利率联动的主要原因。     

R＆D、R＆D溢出、内生增长和内生收敛

根据强调知识与技术创新、知识与技术溢出重要性的当代内生增长理论，本文建立了一个将R＆D和R＆D溢出与资本积累之间、R＆D和R＆D溢出与总产出增长之间直接关联起来的动态模型。面板数据协整检验实证分析结果表明R＆D和R＆D溢出与资本积累之间、R＆D和R＆D溢出与总产出增长之间分别存在显著的正面长期协整关联。进一步的分析表明，R＆D与资本积累之间、R＆D与总产出增长之间分别存在显著的长期双向格兰杰因果关系。由此观知，R＆D乃长期经济增长源泉之所在。另一方面，尽管资本积累或总产出增长并不格兰杰导致R＆D溢出，证据表明R＆D溢出格兰杰导致资本积累和总产出增长。这种由R＆D溢出到资本积累和总产出增长的单向格兰杰因果关系意味着尽管知识与技术的跨国传播并非必然发生。其实为世界经济增长的重要动力。     

协整向量误设对相关检验的影响

常用于检验既定协整关系的统计量有tDF和tECM两种，但由于真实数据生成过程未知，估计模型中可能存在一定程度的协整向量误设，从而使统计量的分布特征受到影响。本文首先探讨tDF检验的隐含系数约束α=γ，即短期弹性等于先验长期弹性；其次分析零假设下两种统计量的分布特征，以及先验设定γ对信号噪声比q进而对tECM分布特征的影响；最后在局部备择假设下，给出两种统计量的渐近分布，并表明向量误设会降低协整检验的势，其程度与设定误差d正相关。