Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis

We consider simple methods to improve the growth nowcasts and forecasts obtained by mixed-frequency MIDAS and UMIDAS models with a variety of indicators during the Covid-19 crisis and recovery period, such as combining forecasts across various specifications for the same model and/or across different models, extending the model specification by adding MA terms, enhancing the estimation method by taking a similarity approach, and adjusting the forecasts to put them back on track using a specific form of intercept correction. Among these methods, adjusting the original nowcasts and forecasts by an amount similar to the nowcast and forecast errors made during the financial crisis and subsequent recovery seems to produce the best results for the US, notwithstanding the different source and characteristics of the financial crisis. In particular, the adjusted growth nowcasts for 2020Q1 get closer to the actual value, and the adjusted forecasts based on alternative indicators become much more similar, all unfortunately indicating a much slower recovery than without adjustment, and very persistent negative effects on trend growth. Similar findings also emerge for forecasts by institutions, for survey forecasts, and for the other G7 countries.     

NOMINAL GDP TARGETING AND CENTRAL BANK CONSERVATIVENESS

In the literature on monetary economics, there is the ‘inflationary bias’ result which predicts that the rate of inflation will be biased towards a higher level under discretionary monetary policy than under a rule‐based policy regime. It is established that a credible nominal target can eliminate this ‘inflationary bias’. In this paper, we examine the case of nominal GDP targeting, which is a rule‐based monetary regime. Depending on the degree of conservativeness by the central bank, we show in a stylized model the choice of different combination of inflation and real GDP targets can still result in an ‘inflationary bias’, and there also exists the possibility of a ‘dis‐inflationary bias’.     

Characteristics and implications of Chinese macroeconomic data revisions

The research examining macroeconomic data for developed economies suggests that an understanding of the nature of data revisions is important both for the production of accurate macroeconomic forecasts and for forecast evaluation. This paper focuses on Chinese data, for which there has been substantial debate about data quality for some time. The key finding in this paper is that, while it is true that the Chinese macroeconomic data revisions are not well-behaved, they are not very different from similarly-timed U.S. macroeconomic data revisions. The positive bias in Chinese real GDP revisions is a result of the fast-growing service sector, which is notably hard to measure in real time. A better understanding of the revisions process is particularly helpful for studies of the forecast errors from surveys of forecasters, where the choice of the vintage for outcomes may have an impact on the estimated forecast errors.     

High-frequency credit spread information and macroeconomic forecast revision

We examine whether professional forecasters incorporate high-frequency information about credit conditions when revising their economic forecasts. Using a mixed data sampling regression approach, we find that daily credit spreads have significant predictive ability for monthly forecast revisions of output growth, at both the aggregate and individual forecast levels. The relationships are shown to be notably strong during ‘bad’ economic conditions, which suggests that forecasters anticipate more pronounced effects of credit tightening during economic downturns, indicating an amplification effect of financial developments on macroeconomic aggregates. The forecasts do not incorporate all financial information received in equal measures, implying the presence of information rigidities in the incorporation of credit spread information.     

我国M2现状及未来趋势浅析

对我国当前M2快速增长的原因,从经济发展、居民储蓄和外汇储备等三个方面进行深入分析,并在此基础上对未来发展趋势进行预测分析。     

Role of banks in financial inclusion in India

Financial inclusion is emerging as a new paradigm of economic growth that plays major role in driving away the poverty from the country. It refers to delivery of banking services to masses including privileged and disadvantaged people at an affordable terms and conditions. Financial inclusion is important priority of the country in terms of economic growth and advanceness of society. It enables to reduce the gap between rich and poor population. In the current scenario financial institutions are the robust pillars of progress, economic growth and development of the economy. The present study aims to examine the impact of financial inclusion on growth of the economy over a period of seven years. Secondary data is used which has been analyzed by multiple regression model as a main statistical tool. Results of the study found positive and significant impact of number of bank branch and Credit deposit ratio on GDP of the country, whereas an insignificant impact has been observed in case of ATMs growth on Indian GDP.     

浙江省区域物流增量与GDP增量关系的计量分析

本文首先使用Granger因果关系检验法检验浙江省1958—2004年间的物流增量和GDP增量的因果性,结果发现本省GDP增量的变化是引起物流增量变化的原因。通过协整和误差修正模型对两者的长期和短期关系进行了研究。研究发现,浙江省区域物流增量与GDP增量间存在着可靠的协整关系。这说明随着浙江经济的快速持续发展,保持两者的均衡发展具有重要的意义。最后提出基本结论以及相关政策建议。     

Forecasting GDP growth with NIPA aggregates: In search of core GDP

In addition to GDP, which is measured using expenditure data, the U.S. national income and product accounts (NIPAs) provide a variety of measures of economic activity, including gross domestic income and other aggregates that exclude one or more of the components that make up GDP. Similarly to the way in which economists have attempted to use core inflation—which excludes volatile energy and food prices—to predict headline inflation, the omission of GDP components may be useful in extracting a signal as to where GDP is going. We investigate the extent to which these NIPA aggregates constitute “core GDP.” In an out-of-sample forecasting exercise using a novel real-time dataset of NIPA aggregates, we find that consumption growth and the growth of GDP excluding inventories and trade have historically outperformed a canonical univariate benchmark for forecasting GDP growth, suggesting that these are promising measures of core GDP growth.     

Forecasts in times of crises

Financial crises pose unique challenges for forecast accuracy. Using the IMF’s Monitoring of Fund Arrangements (MONA) database, we conduct the most comprehensive evaluation of IMF forecasts to date for countries in times of crises. We examine 29 macroeconomic variables in terms of bias, efficiency, and information content to find that IMF forecasts add substantial informational value, as they consistently outperform naive forecast approaches. However, we also document that there is room for improvement: two-thirds of the key macroeconomic variables that we examine are forecast inefficiently, and six variables (growth of nominal GDP, public investment, private investment, the current account, net transfers, and government expenditures) exhibit significant forecast biases. The forecasts for low-income countries are the main drivers of forecast biases and inefficiency, perhaps reflecting larger shocks and lower data quality. When we decompose the forecast errors into their sources, we find that forecast errors for private consumption growth are the key contributor to GDP growth forecast errors. Similarly, forecast errors for non-interest expenditure growth and tax revenue growth are crucial determinants of the forecast errors in the growth of fiscal budgets. Forecast errors for balance of payments growth are influenced significantly by forecast errors in goods import growth. The results highlight which macroeconomic aggregates require further attention in future forecast models for countries in crises.     

Does high‐speed rail connection really promote local economy? Evidence from China’s Yangtze River Delta

High‐speed rail (HSR) has led to a transportation revolution in China. This paper uses the county‐level panel data of China’s Yangtze River Delta to investigate the effect of HSR connection on local economy. To address the issue of endogenous HSR route placement, we use a straight‐line strategy to construct potential HSR connection variables as instrumental variables of the actual HSR connection. Both the difference‐in‐differences and instrumental variable methods show that HSR connection impedes local economy, especially in peripheral regions. The impediment effect is channeled through population reallocation from peripheral to core areas and the restructuring of industries.