On the (Un)foreseeable path of the UK economy through uncertain times
研究英国GDP增长、通胀、失业率和政策利率的样本内变动与样本外预测，比较英格兰银行及多种模型的表现，发现贝叶斯时变模型和增广自回归模型各有优势。
Against the backdrop of uncertain economic and financial times we assess the in-sample movements and out-of-sample forecasting performance of UK GDP growth, CPI inflation, unemployment rate and the Bank of England's policy interest rate. Bank of England’s forecasts are ranked first for GDP growth and inflation. Larger Bayesian time-varying models, which rely on domestic variables (such as economic policy uncertainty, the sterling exchange rate, Divisia M4 growth, and financial stress) and international variables (such as global supply chain pressures, oil prices, and geopolitical risk) provide superior unemployment rate forecasts and predict, together with market expectations of interest rates, the policy interest rate better than other competing models. An Augmented AutoRegressive (AR) model of global supply chain pressures, pandemic effects, and trade uncertainty (the latter on the rise in the run-up to the US presidential elections and following the implementation of President Trump’s tariff-related policies) offer significant forecasting power for UK inflation and very short-term GDP growth. The Augmented AR model makes no assumptions about the future path of the policy interest rate. This might be an attractive and alternative option to the Bank of England's forecasting analysis which currently relies on market expectations of interest rates.