Forecasting the UK economy with a medium-scale Bayesian VAR

We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.     

Impact of the Fast Track Land Reform Programme (FTLRP) on agricultural production: A tobacco success story in Zimbabwe?

The study ascertained the impact of the Fast Track Land Reform Programme (FTLRP) in Zimbabwe on tobacco production. The Chow Test, Vector Error Correction Model (VECM) and the Vector Error Granger-Causality Test were applied. The results reveal that there was a structural break in tobacco sales in the year 2000. Furthermore, in the long-run, area under tobacco production had a positive impact whilst number of tobacco producers had a negative impact on tobacco sold pre-FTLRP. Post-FTLRP, area of tobacco and number of tobacco producers had negative impact. In addition, the FTLRP induced an 8.94 % increase in the speed of adjustment in correcting the long-run equilibrium in tobacco sales. In the short-run, the FTLRP caused a percentage increase in the area of tobacco production and number of tobacco producers to induce a 0.65 % and 0.76 % increase in the tobacco sales, respectively. Area of tobacco production and number of tobacco producers Granger-caused tobacco sales in the pre-FTLRP period. Post-FTLRP, the number of tobacco growers Granger-caused tobacco sales. It is concluded that the FTLRP had an impact on tobacco sales, mainly through the number of tobacco growers. The study recommends the specialisation and training of the new farmers to improve productivity.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

基于 LSSVM 的供应链金融业务中企业信用风险评估研究

针对目前供应链金融业务中企业信用风险评估不科学与不完善的问题,在对企业信用风险评估的指标体系进行梳理和完善的基础上,利用最小二乘支持向量机(LSSVM)的基本原理尝试构建了企业信用风险 LSSVM 评估模型,并对模型的可行性和优越性做了分析和验证。研究结果表明：LSSVM 评估模型收敛速度快、准确度较高、泛化能力强,为供应链金融业务中的企业信用风险评估提供了一种新的办法和思路。     

中国外贸进出口对税收的影响：理论与实证

对外贸易对中国的税收有怎样的影响?回答这一问题，对制定贸易政策有重要的现实意义。本文对1-2-3模型进行修正，将总产出区分为4种具有不同税收特征的产品，构建相应的税收模型，从理论上分析了对外贸易对税收有正面的影响。实际测算显示，一般贸易出口每增加1元人民币，会相应带动当年税收增加0．4～0．5元人民币。通过对季度数据的Granger因果检验和向量误差修正模型分析，本文发现出口增加对税收有持续的正面影响。     

FDI、国际贸易及我国经济增长的协整分析与VECM模型

本文利用我国1983-2004年的经济数据进行实证检验,根据协整理论建立向量误差修正模型(VECM)。实证结果说明外国直接投资、国际贸易与经济增长间具有长期均衡关系,且我国国内生产总值的增长与外国直接投资有双向因果关系,但相互影响的程度不同;我国为出口导向型经济增长国家且外国直接投资对国际贸易具有促进作用.     

Unconventional monetary policies and the corporate bond market

The paper uses a reduced-form vector autoregressive framework to study the effects of quantitative easing and operation “twist”, as well as a conventional monetary expansion, on corporate bond yields and spreads. We construct rating- and maturity-based weekly bond portfolios using TRACE and simulate monetary policies as shocks to the Treasury yield curve. We find that none of the policies can persistently lower corporate spreads, and that operation twist is the only policy capable of lowering corporate yields. This latter finding can be accounted for by the operation twist’s ability to keep the monetary base constant and, therefore, to flatten the riskless yield curve without generating inflationary expectations.     

HOW TO MEASURE THE ECONOMIC IMPACT OF VECTOR‐BORNE DISEASES AT COUNTRY LEVEL

Vector‐borne diseases (VBDs) are widespread in less developed countries and reemerging in developed ones. Available economic studies agree that VBDs have significant effects on countries' economic outcomes, and affirm that a systematic evaluation of such effects is crucial for the efficient allocation of resources to health‐related priorities. This paper provides a comparative assessment of available methodologies for measuring the economic impact of VBDs at national level. We review both macroeconometric and micro‐based approaches, and examine advantages and disadvantages of current methods. We conclude by suggesting possible areas for future research.     

A critical overview of privacy-preserving approaches for collaborative forecasting

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.     

Using Machine Learning for measuring democracy: A practitioners guide and a new updated dataset for 186 countries from 1919 to 2019

We provide a comprehensive overview of the literature on the measurement of democracy and present an extensive update of the Machine Learning indicator of Gründler and Krieger (2016). Four improvements are particularly notable: First, we produce a continuous and a dichotomous version of the Machine Learning democracy indicator. Second, we calculate intervals that reflect the degree of measurement uncertainty. Third, we refine the conceptualization of the Machine Learning Index. Finally, we significantly expand the data coverage by providing democracy indices for 186 countries in the period from 1919 to 2019.