Financial information and macroeconomic forecasts

Abstract

We study the forecasting power of financial variables for macroeconomic variables for 62 countries between 1980 and 2013. We find that financial variables such as credit growth, stock prices and house prices have considerable predictive power for macroeconomic variables at one to four quarters horizons. A forecasting model with financial variables outperforms the World Economic Outlook (WEO) forecasts in up to 85 percent of our sample countries at the four quarters horizon. We also find that cross-country panel models produce more accurate out-of-sample forecasts than individual country models.

Comparison on Subannual Seasonality of Building Construction in European Countries

“…The economy, whether regional, national or international, is subject to variability resulting from many macro-and microeconomic factors (Caruso, 2019;Chen and Hung, 2010;Makin, 2019). Modeling of time-frequency variability by examining the direction and magnitude of the impact of these factors is feasible as a result of the application of a variety of techniques of econometric analysis (Borjigin et al, 2018;Chen and Ranciere, 2019;Ghoddusi et al, 2019;Liu et al, 2019). However, regardless of the determinants used to analyze the condition of the economy, precise information about the characteristics of its cyclical variation is very important (Miyazaki, 2009;Sheldon, 2017;Tan and Mathews, 2010).…”

Macrofinancial Causes of Optimism in Growth Forecasts

“…Indeed, the Basel III framework for banking supervision includes specific guidance for national regulators to use the credit-to-GDP gap to measure the financial cycle and calibrate the stance of macroprudential policy tools (BCBS 2010 ). Second, its widespread availability over long periods allows us to study a large sample of countries—twice as large as previous papers such as Kirti ( 2018 ) and Chen and Rancière ( 2019 )—including low-income economies with less developed financial systems. 11 Our full estimation sample consists of 218 programs (89 GRA and 129 PRGT) for which credit aggregate series are available for at least seven years prior to program approval, allowing us to compute our measure of the real-time credit-to-GDP gap.…”

“…Schularick and Taylor ( 2012 ) show that financial credit expansions forecast declines in real activity in a sample of 14 countries over the past 130 years. Chen and Rancière ( 2019 ) present simple models incorporating financial variables including credit growth that provide good out-of-sample accuracy when forecasting output in a sample of 62 countries. Closely related to our work, they show that the models outperform the IMF’s World Economic Outlook (WEO) in an out-of-sample horserace of forecast accuracy for 85 percent of the countries they study.…”

Forecasting unemployment in the euro area with machine learning

“…The frequency is monthly, and the data span the period from 1998:4 to 2019:9 for a total of 243 observations for each variable. The 36 explanatory variables were selected based on economic theory and previous relevant empirical studies (Chen & Ranciere, 2019;Groen & Kapetanios, 2016;Kim & Swanson, 2014;Shen, 1996). The data for the unemployment rate were obtained from the ECB Statistical Data Warehouse The target variable (label) is defined by the first difference of the unemployment rate: 0 when the difference is negative implying that the unemployment rate decreases and 1 when the difference is positive and the unemployment rate rises.…”