The impact of sales promotions on store performance: a structural vector autoregressive approach

The present paper analyses the impact of sales promotions on store performance, in the short and long term, from the retailer's point of view. Relationships among promoted and regular sales in the hypermarkets of a large-scale retail chain of national importance, are investigated by means of a structural vector autoregressive model (SVAR). Statistically significant effects of sales promotions in the heavy household section on store sales are found in the short-run; these promotions produce additional sales and thus act as an attractive factor. Promotions in textile category, on the contrary, produce an immediate negative effect on net sales. In the long run, negative statistically significant effects on regular sales are detected when promotions are repeatedly implemented within perishables category.     

VAR研究现状及在我国金融市场的应用前景

伴随着金融市场的产生和发展,如何防范金融风险成为金融界人士关注的焦点。作为一种新的防范金融风险的工具,理论上,外国学者认为,VAR在信息披露、资源配置与绩效评价三个方面发挥重要的作用;实践上,在国外已被广泛应用于各个领域。以纠正非对称金融衍生工具度量市场风险方法的缺陷。在我国,对VAR理论上的研究虽起步较晚,但仍有不少成果。随着我国金融业市场化程度的不断提高,VAR在我国金融业加强管理、防范投资组合风险、增强市场透明度等方面的应用前景将是十分广阔的。     

上海股市流动性、收益率、交易量三者关系探讨

采用VAR(向量自回归)方法考察上海股票市场上流动性与收益率、交易量之间的关系。综合考虑理论计算结果和实证结果,可以作出判断:交易量和收益率导致流动性变动的因果关系显著,反之,因果关系并不显著。     

中国式财政分权的宏观经济绩效——基于限制型VAR模型的经验分析

本文从中央政府与地方政府行为角度构建了中国式分权与宏观经济绩效之间的关联框架,指出中国式分权体制下的地方政府竞争行为构成了中国经济增长以及周期性过热的主导力量。由于中国不同省份的自然历史、地理位置以及发展政策差异极大,即使是同样的分权程度也可能产生不同的经济绩效与激励效果,这也就使得面板数据模型回归结论对其设定形式非常敏感。因此,本文采用受限VAR模型来实证分析中国式财政分权对于经济增长率和通货膨胀率的作用效应,结论证实了本文的理论预期。     

新疆产业结构变迁与恩格尔系数协整关系的实证研究

通过构造一个由消费结构、城镇居民收入水平、第二产业结构和第三产业结构四个变量组成的VAR模型,对新疆城乡居民的消费结构与产业结构相互作用的关系及其动态特征进行了实证研究。根据设定的模型、格兰杰因果检验、脉冲响应函数、预测方差分解和协整检验,发现新疆城镇居民消费结构会影响第二产业结构的转换,随着居民收入水平的提高,恩格尔系数呈下降趋势。城乡居民收入水平对产业结构升级产生一定影响。     

Choosing Prior Hyperparameters: With Applications to Time-Varying Parameter Models

Time-varying parameter models with stochastic volatility are widely used to study macroeconomic and financial data. These models are almost exclusively estimated using Bayesian methods. A common practice is to focus on prior distributions that themselves depend on relatively few hyperparameters such as the scaling factor for the prior covariance matrix of the residuals governing time variation in the parameters. The choice of these hyperparameters is crucial because their influence is sizeable for standard sample sizes. In this article, we treat the hyperparameters as part of a hierarchical model and propose a fast, tractable, easy-to-implement, and fully Bayesian approach to estimate those hyperparameters jointly with all other parameters in the model. We show via Monte Carlo simulations that, in this class of models, our approach can drastically improve on using fixed hyperparameters previously proposed in the literature. Supplementary materials for this article are available online.     

Bayesian testing of restrictions on vector autoregressive models

In this study, we propose a prior on restricted Vector Autoregressive (VAR) models. The prior setting permits efficient Markov Chain Monte Carlo (MCMC) sampling from the posterior of the VAR parameters and estimation of the Bayes factor. Numerical simulations show that when the sample size is small, the Bayes factor is more effective in selecting the correct model than the commonly used Schwarz criterion. We conduct Bayesian hypothesis testing of VAR models on the macroeconomic, state-, and sector-specific effects of employment growth.     

Forecast mean squared error reductionin the VAR(1) process

When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.

In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings.     

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This article develops a new identification procedure to estimate the contemporaneous relation between monetary policy and the stock market within a vector autoregression (VAR) framework. The approach combines high-frequency data from the futures market with the VAR methodology to circumvent exclusion restrictions and achieve identification. Our analysis casts doubt on VAR models imposing a recursive structure between innovations in policy rates and stock returns. We find that a tightening in policy rates has a negative impact on stock prices and that the Federal Reserve (Fed) has responded significantly to movements in the stock market. Estimates are robust to various model specifications.