中国的高铁项目是否促进当地经济?——来自面板数据方法的新证据

2008年，中国首条高铁线——京津城际高铁开通，随后国家启动了高铁建设热潮。截至2015年，中国已基本形成了所谓“四纵四横”的高速铁路框架和走廊。但高铁建设引发了全球激烈的争论，因为公共项目的净收益与其成本之间没有直接联系，实证结果非常复杂。

INTERVAL TIME SERIES ANALYSIS WITH AN APPLICATION TO THE STERLING-DOLLAR EXCHANGE RATE

传统的经济计量学长采用了“点”测量时间系列数据。处于真实生活状况，然而，它承受轻快信息的损失，自从许多变量被间隔在一个给定的时期围住。处理这个问题，这篇论文为间隔时间系列分析提供新方法论。这个概念“间隔随机的过程”正式在基于点的经济计量学被定义为“随机的过程”的一个对应物。作者介绍间隔 stationarity 的概念，间隔统计(包括间隔平均数，间隔变化，等等) 并且建议间隔调查在间隔过程之间的动态关系的线性模型。为评价的一条新基于间隔的优化途径被建议，并且相应评估标准被导出。证明新间隔方法提供有效结果，英磅美元汇率上的一个实验例子被举。

Out-of-sample forecasts of China's economic growth and inflation using rolling weighted least squares

Macroeconomic forecasting in China is essential for the government to take proper policy decisions on government expenditure and money supply,among other matters.The existing literature on forecasting Chinas macroeconomic variables is unclear on the crucial issue of how to choose an optimal window to estimate parameters with rolling out-of-sample forecasts.This study fills this gap in forecasting economic growth and inflation in China,by using the rolling weighted least squares(WLS)with the practically feasible cross-validation(CV)procedure of Hong et al.(2018)to choose an optimal estimation window.We undertake an empirical analysis of monthly data on up to 30 candidate indicators(mainly asset prices)for a span of 17 years(2000-2017).It is documented that the forecasting performance of rolling estimation is sensitive to the selection of rolling windows.The empirical analysis shows that the rolling WLS with the CV-based rolling window outperforms other rolling methods on univariate regressions in most cases.One possible explanation for this is that these macroeconomic variables often suffer from structural changes due to changes in institutional reforms,policies,crises,and other factors.Furthermore,we find that,in most cases,asset prices are key variables for forecasting macroeconomic variables,especially output growth rate.

A General Approach to Testing Volatility Models in Time Series

Volatility models have been playing important roles in economics and finance.Using a generalized spectral second order derivative approach,we propose a new class of generally applicable omnibus tests for the adequacy of linear and nonlinear volatility models.Our tests have a convenient asymptotic null N(0,1)distribution,and can detect a wide range of misspecifications for volatility dynamics,including both neglected linear and nonlinear volatility dynamics.Distinct from the existing diagnostic tests for volatility models,our tests are robust to time-varying higher order moments of unknown form(e.g.,time-varying skewness and kurtosis).They check a large number of lags and are therefore expected to be powerful against neglected volatility dynamics that occurs at higher order lags or display long memory properties.Despite using a large number of lags,our tests do not suffer much from the loss of a large number of degrees of freedom,because our approach naturally discounts higher order lags,which is consistent with the stylized fact that economic or financial markets are affected more by the recent past events than by the remote past events.No specific estimation method is required,and parameter estimation uncertainty has no impact on the convenient limit N(0,1)distribution of the test statistics.Moreover,there is no need to formulate an alternative volatility model,and only estimated standardized residuals are needed to implement our tests.We do not have to calculate tedious and model-specific score functions or derivatives of volatility models with respect to estimated parameters,which are required in some existing popular diagnostic tests for volatility models.We examine the finite sample performance of the proposed tests.It is documented that the new tests are rather powerful in detecting neglected nonlinear volatility dynamics which the existing tests can easily miss.They are useful diagnostic tools for practitioners when modelling volatility dynamics.

Nowcasting China's GDP Using a Bayesian Approach

Real time nowcasting is an assessment of current-quarter GDP from timely released economic and financial series before the GDP figure is disseminated.Providing a reliable current quarter nowcast in real time based on the most recently released economic and financial monthly data is crucial for central banks to make policy decisions and longer-term forecasting exercises.In this study,we use dynamic factor models to bridge monthly information with quarterly GDP and achieve reduction in the dimensionality of the monthly data.We develop a Bayesian approach to provide a way to deal with the unbalanced features of the dataset and to estimate latent common factors.We demonstrate the validity of our approach through simulation studies,and explore the applicability of our approach through an empirical study in nowcasting the China's GDP using 117 monthly data series of several categories in the Chinese market.The simulation studies and empirical study indicate that our Bayesian approach may be a viable option for nowcasting the China's GDP.

Econometric Modeling and Economic Forecasting

Editorial Introduction This special issue is dedicated to forecasting and modeling which are well regarded as two of the most challenging tasks in economics and finance because of the complexities of economic and financial data,such as nonlinearity,non-stationarity,and irregularities.How to forecast economic and financial data accurately is still an open question in the profession and practice.