Spurious principal components

The principal component regression (PCR) is often used to forecast macroeconomic variables when there are many predictors. In this letter, we argue that it makes sense to pre-whiten the predictors before including these in a PCR. With simulation experiments, we show that without such pre-whitening, spurious principal components can appear and that these can become spuriously significant in a PCR. With an illustration to annual inflation rates for five African countries, we show that non-spurious principal components can be genuinely relevant in empirical forecasting models.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

A quantile regression approach for estimating panel data models using instrumental variables

We introduce a quantile regression approach to panel data models with endogenous variables and individual effects correlated with the independent variables. We find newly developed quantile regression methods can be easily adapted to estimate this class of models efficiently.     

中国省级区域国际旅游竞争力决定因素分析

基于理论分析提出了影响我国省级区域旅游目的地国际旅游竞争力的8个决定因素,即旅游资源、旅游资本、旅游业劳动力、政府作用、企业能力、旅游产业结构、基础设施和区位条件。以我国31个省(区)为样本,利用基于面板数据的多元线性回归分析方法,从实证角度定量研究了上述8个决定因素对省级区域国际旅游竞争力影响的显著性及其相对重要性。回归结果显示:我国各省(区)国际旅游竞争力来源的98.5%能够由8个决定因素解释;除旅游产业结构外,其他决定因素对各省级区域国际旅游竞争力的影响均在0.01的水平上显著。     

Does the Reserve Bank of Australia follow a forward-looking nonlinear monetary policy rule?

ABSTRACT

The conventional monetary policy rule describes a simple linear relationship between the domestic interest rate, inflation rate and output gap. An important extension to this rule is to incorporate the forward-looking behaviour of central banks, where it is assumed that they target an expected level of inflation instead of its current realised value. Using quarterly observations for the period 1993:1-2018:2, this paper investigates whether the conduct of monetary policy in Australia can be described by a forward-looking linear monetary policy rule, or by a nonlinear forward-looking monetary policy rule. In particular, the nonlinear forward-looking monetary policy rule is analysed in a regime-switching framework using a smooth logistic transition regression model. While the results show that the conventional forward-looking linear monetary policy rule describes the application of monetary policy in Australia reasonably well, the interest rate setting behaviour of the RBA is best described by a nonlinear forward-looking monetary policy rule.     

Economic forecasting with evolved confidence indicators

We present a machine-learning method for sentiment indicators construction that allows an automated variable selection procedure. By means of genetic programming, we generate country-specific business and consumer confidence indicators for thirteen European economies. The algorithm finds non-linear combinations of qualitative survey expectations that yield estimates of the expected rate of economic growth. Firms’ production expectations and consumers’ expectations to spend on home improvements are the most frequently selected variables – both lagged and contemporaneous. To assess the performance of the proposed approach, we have designed an out-of-sample iterative predictive experiment. We found that forecasts generated with the evolved indicators outperform those obtained with time series models. These results show the potential of the methodology as a predictive tool. Furthermore, the proposed indicators are easy to implement and help to monitor the evolution of the economy, both from demand and supply sides.     

开放式基金"赎回之谜"的实证检验

我国开放式基金是否存在"赎回之谜"以及存在的条件和范围尚无明确的结论.运用多元线性回归模型、面板数据模型和分位数回归模型三种方法对我国开放式基金"赎回之谜"进行检验,结果显示:分位数模型能够捕捉基金业绩对基金净申购率的非对称性影响,这是多元线性回归模型和面板数据模型无法实现的.通过将分位数模型和对应分析方法结合使用,发...     

我国上市公司年度报告信息披露结构的实证分析

以上市公司强制性信息披露水平、自愿性信息披露以及总体信息披露水平的显著性检验为基础,建立上市公司自愿信息披露指数与其影响因素之间的多元线性回归模型,来试图找出我国上市公司年度报告信息披露的结构特征,从而为改进我国上市公司信息披露水平提供最直接的证据。     

基于地理加权回归的上海市房价空间分异及其影响因子研究

利用上海市外环以内2010年12月1014个小区的平均房价数据,通过构建地理加权回归模型,并与基于全局最小二乘法(OLS)进行比较,揭示上海小区房价的空间分异和不同影响因子的影响。研究发现,每增加或减少一个单位各影响因子对房价的影响大小依次为:建成时间,到CBD距离,绿化率,到公园距离,距地铁站距离,距超市距离和距学校距离。同时,地理加权回归分解成局部参数估计优于OLS提供的全局参数估计,它可以深刻的揭示出房价和空间影响因子之间复杂的关系,而且可视化的工具可以用地图的形式更详细的呈现出城市房价的整体景观,这些都是传统OLS无法比拟的。     

要素市场配置与我国城乡居民收入差距研究

在我国城乡要素市场人为分割情况下,劳动力、土地、资本等要素在城乡间的流动受到限制,要素的这种错误配置导致了较低的配置效率,从而使得城乡间经济收敛的内在机制无法发挥作用。通过选取一定的统计指标,对1990-2011年我国城乡要素收入进行分解,用定性与定量相结合的方法分析要素市场配置对城乡居民收入差距的影响。研究表明,土地要素和劳动力要素对城乡居民收入差距影响最大。应采取完善要素市场配置体制,促进劳动力、土地、资本等要素合理配置,完善政府宏观调控职能等政策缩小城乡居民收入差距。