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Unconventional Monetary Policies in the UK, estimating their impact using shrinkage and persistent volatility

英国的非常规货币政策，利用收缩和持续波动来估计其影响

基本信息

批准号：
1916649
负责人：
金额：
--
依托单位：
King's College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1916649
关键词：
Unconventional Monetary Policies UK estimating

项目摘要

Two main strands in econometric analysis literature for macro-econometrics and finance are, i) structural change and volatility modelling and ii) high-dimensional regressions. For these two strands we propose a framework that has the potential to create a number of contributions. Below, we briefly describe the setup and goals of the research idea and illustrates some potential empirical applications where our methodology can be applied. First, empirical work has concluded that variation in volatility is very persistent, a feature illustrated in estimated parameters that lie close to the boundary for stationarity. This implies that volatility can be characterised by persistent and possibly non-stationary processes. In this content we propose the use of a kernel volatility estimator (KVE) that has the potential to adequately fit the observed behaviour of volatility. Our estimator requires a small set of assumptions and can disentangle persistent types of volatilities and lower type such as ARCH and SV. To illustrate this ability, Monte Carlo (MC) simulations are required. The potential gains of this framework are first, that we do not impose linear types of volatility and second, that parameter estimates can be more precise if our estimator is able to correctly estimate the persistent volatility that can be the major driver in the data. Second, the growing availability of large datasets in the last ten years can potentially assist econometricians under the assumption that they carry rich and relevant information. Their size creates an "ill-posed" problem where even basic methods as ordinary regression cannot work. The literature has produced many ways to tackle this problem that can be separated in the sparse regression and dimensionality reduction framework respectively. Its main aim is first, reduce the dimensions of the regressors by producing factors that carry the maximal informative content, in terms of correlations and further to exclude regressors that have minimal information for inference. The main avenue of doing this is to expand the classical least squares estimator minimisation problem with penalties that induce shrinkage and dimensionality reduction, commonly on the first and second norms. The most well known estimators are the Lasso, Ridge regression, Sparse Partial Least Squares and the elastic net. In this content we proposed a different estimator that generalises the above. Specifically, we do not impose a specific number of norm-penalties but instead we envision an estimator that includes a vast number of norm-penalties, whose performance will be assessed by Cross-Validation and out of sample forecasting. The benefit of this procedures is that while agnostic to the number of norm-penalties to include a-priori, we produce a penalisation scheme that can potentially work in a different way for each dataset and can yield better results. Assessing the performance of this estimator requires MC with synthetic datasets. In terms of the empirical applications our estimators are natural candidates to examine first the volatility that exists in stock indexes and whether parameter estimates obtained from the KVE procedure are better in forecasting. Further both the KVE as well as the shrinkage estimator can help us to potentially examine the effects unconventional monetary policies, as employed by the Bank of England, had in the real economy and whether the effect deteriorated after their initial employment.

宏观计量经济学和金融计量经济学分析文献中的两个主要方面是，i）结构变化和波动建模，ii）高维回归。对于这两个股，我们提出了一个框架，有可能创造一些贡献。下面，我们简要描述了研究思路的设置和目标，并说明了我们的方法可以应用的一些潜在的实证应用。首先，实证研究得出的结论是，波动性的变化是非常持久的，一个特征说明了估计的参数，接近平稳性的边界。这意味着波动性可以被描述为持续的和可能非平稳的过程。在本内容中，我们提出了使用内核波动估计（KVE），有可能充分适应观察到的波动行为。我们的估计需要一个小的假设集，可以解开持续类型的波动率和较低的类型，如波动率和SV。为了说明这种能力，需要蒙特卡罗（MC）模拟。这个框架的潜在好处是，首先，我们不强加线性类型的波动率，其次，如果我们的估计器能够正确估计持续波动率，参数估计可以更精确，这可能是数据中的主要驱动因素。其次，在过去十年中，大型数据集的可用性不断增加，这可能有助于计量经济学家假设它们携带丰富和相关的信息。他们的规模创造了一个“不适定”的问题，即使是普通回归的基本方法也无法工作。文献已经产生了许多方法来解决这个问题，可以分别在稀疏回归和降维框架中分离。它的主要目的是首先，通过产生携带最大信息内容的因素来减少回归量的维度，在相关性方面，并进一步排除具有最小信息的回归量。这样做的主要途径是扩展经典的最小二乘估计最小化问题，惩罚引起收缩和降维，通常在第一和第二范数上。最著名的估计是Lasso，岭回归，稀疏偏最小二乘和弹性网络。在本内容中，我们提出了一个不同的估计，概括了上述。具体来说，我们不施加特定数量的范数惩罚，而是设想一个包括大量范数惩罚的估计量，其性能将通过交叉验证和样本预测进行评估。这个过程的好处是，虽然不知道包含先验的范数惩罚的数量，但我们产生了一个惩罚方案，该方案可能以不同的方式为每个数据集工作，并可以产生更好的结果。评估此估计器的性能需要MC与合成数据集。在实证应用方面，我们的估计是自然的候选人，首先检查存在于股票指数的波动性，以及是否从KVE过程中获得的参数估计更好的预测。此外，KVE和收缩估计量都可以帮助我们潜在地研究英格兰银行所采用的非常规货币政策对真实的经济的影响，以及这种影响在最初实施后是否恶化。