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.     

Can stable environmental protection officials’ tenure reduce illegal emissions?

Based on the day-night PM_2.5 difference of the nearest atmospheric monitoring points to measure whether there is illegal emission, this paper explores the impact of environmental protection officials' tenure (EPLT) on illegal emissions. The results show that there is a positive correlation between EPLT and illegal emissions. Stable EPLT can reduce illegal emissions by increasing environmental information disclosure and environmental subsidies. It can increase illegal emissions by weakening public pollution reporting and media supervision, environmental law enforcement and promoting corruption. In addition, EPLT can increase illegal emissions as EPLT is less than 3.55 years, while EPLT can increase illegal emissions as EPLT exceeds 6.21 years. Further, when mayor's tenure and EPLT are both stable, the promotion effect of EPLT on illegal emission is stronger. Stable company director's tenure can also aggravate the promotion effect of EPLT on illegal emissions.     

A Bayesian analysis of a variance decomposition for stock returns

We apply Bayesian methods to study a common vector autoregression (VAR)-based approach for decomposing the variance of excess stock returns into components reflecting news about future excess stock returns, future real interest rates, and future dividends. We develop a new prior elicitation strategy, which involves expressing beliefs about the components of the variance decomposition. Previous Bayesian work elicited priors from the difficult-to-interpret parameters of the VAR. With a commonly used data set, we find that the posterior standard deviations for the variance decomposition based on these previously used priors, including “non-informative” limiting cases, are much larger than classical standard errors based on asymptotic approximations. Therefore, the non-informative researcher remains relatively uninformed about the variance decomposition after observing the data. We show the large posterior standard deviations arise because the “non-informative” prior is implicitly very informative in a highly undesirable way. However, reasonably informative priors using our elicitation method allow for much more precise inference about components of the variance decomposition.     

The Structural Relation Between Mortgage and Market Interest Rates

This paper analyzes the dynamic relationship between primary and secondary mortgage markets and the short-term and long-term market interest rates. Using a series of monthly data on fixed rate mortgage rates and GNMA rates, we explore the dependence and speed of adjustment in these primary and secondary mortgage rates to each other as well as to the long and short-term government rates. The results indicate that residential mortgage rates in general, appear to follow the long-term rate and are not very sensitive to movements in the short-term interest rate.     

货币政策和银行风险承担：一种非线性关系

宽松的货币政策会通过估值、收入和现金流机制,追逐收益机制以及中央银行沟通和反应机制等渠道增加银行风险承担,通过风险转移机制降低银行风险承担,从而导致货币政策与银行风险承担之间可能存在复杂的非线性关系。本文使用面板阈值模型,基于我国银行业数据研究了货币政策对银行风险承担的影响,发现我国货币政策对银行风险承担的影响存在门限效应,即货币政策对银行风险承担的影响取决于货币政策基准利率偏离泰勒规则利率的程度。当这种偏离小于门限值时,宽松货币政策会增加银行风险承担;当这种偏离大于门限值时,宽松货币政策会降低银行风险承担。本文研究对更好地理解我国货币政策对银行风险承担及金融稳定的影响有一定参考意义。     

Minnesota-type adaptive hierarchical priors for large Bayesian VARs

Large Bayesian VARs with stochastic volatility are increasingly used in empirical macroeconomics. The key to making these highly parameterized VARs useful is the use of shrinkage priors. We develop a family of priors that captures the best features of two prominent classes of shrinkage priors: adaptive hierarchical priors and Minnesota priors. Like adaptive hierarchical priors, these new priors ensure that only ‘small’ coefficients are strongly shrunk to zero, while ‘large’ coefficients remain intact. At the same time, these new priors can also incorporate many useful features of the Minnesota priors such as cross-variable shrinkage and shrinking coefficients on higher lags more aggressively. We introduce a fast posterior sampler to estimate BVARs with this family of priors—for a BVAR with 25 variables and 4 lags, obtaining 10,000 posterior draws takes about 3 min on a standard desktop computer. In a forecasting exercise, we show that these new priors outperform both adaptive hierarchical priors and Minnesota priors.     

The Austrian current account deficit: Driven by twin deficits or by intertemporal expenditure allocation?

The evidence presented in the paper rejects the twin deficit hypothesis for the Austrian current account balance during the last two decades. The results are based on an estimate of a vector error correction model including quarterly data for the current account balance and potentially relevant variables driving its dynamics. We compute the variance decomposition of the current account's forecast error and its generalized impulse responses to shocks in the innovations of the system. The results in favor of intertemporal expenditure reallocation cannot be reproduced within a second analysis including the current account and a measure of net output, however. The estimated implicit current account balance, interpreted as the discounted expected change in future net output, does not follow the actual behaviour of the current account. First version received: June 1999/Final version received: March 2001     

Threshold cointegration and nonlinear adjustment between goods and services inflation in the United States

In this paper, we model the long-run relationship between goods and services inflation for the United States over the period 1968:1–2003:3. Our empirical methodology makes use of recent developments on threshold cointegration that consider the possibility of a nonlinear relationship between the two inflation series. According to our results, the null hypothesis of linear cointegration would be rejected in favor of a two-regime threshold cointegration model. Consequently, we could expect a cointegrating relationship only when the divergence between services inflation and goods inflation is above the threshold point estimate.     

中国居民资产配置效率的门限效应研究:金融约束视角

本文基于金融约束视角,结合微观调查数据,并运用门限回归法和两阶段最小二乘法,对我国居民资产配置效率的门限效应问题进行研究。结果表明,从流动性约束、房产持有及商业资产持有三个角度分析居民面临的金融约束,居民资产配置效率的非对称门限效应在不同样本中都显著存在。居民持有资产的风险水平越高,带来的投资收益增量部分越低。同时,持有房产在缓解居民金融约束的同时,资产风险增加对于居民资产配置收益的促进作用仍然下降,受流动性约束影响低的居民以及持有商业资产的居民均能够获得更高的资产配置收益。在不改变金融约束的框架下,以商业性资产替代房产作为主要资产种类能够从微观层面增强经济稳定性,降低系统性金融风险,优化收入结构,提升资产配置效率。     

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.