Macroeconomic Effects of Loan Supply Shocks: Empirical Evidence for Peru

This paper quantifies and assesses the impact of an adverse loan supply (LS) shock on Peru's main macroeconomic aggregates using a Bayesian vector autoregressive (BVAR) model in combination with an identification scheme with sign restrictions. The main results indicate that an adverse LS shock: (i) reduces credit and real GDP growth by 372 and 75 basis points in the impact period, respectively; (ii) explains 11.2% of real GDP growth variability on average over the following 20 quarters; and (iii) explained a 180-basis point fall in real GDP growth on average during 2009Q1-2010Q1 in the wake of the Global Financial Crisis (GFC). Additionally, the sensitivity analysis shows that the results are robust to alternative identification schemes with sign restrictions; and that an adverse LS shock has a greater impact on non-primary real GDP growth.

Identification and inference with ranking restrictions

We propose to add ranking restrictions on impulse‐responses to sign restrictions to narrow the identified set in vector autoregressions (VARs). Ranking restrictions come from micro data on heterogeneous industries in VARs, bounds on elasticities, or restrictions on dynamics. Using both a fully Bayesian conditional uniform prior and prior‐robust inference, we show that these restrictions help to identify productivity news shocks in the data. In the prior‐robust paradigm, ranking restrictions, but not sign restrictions alone, imply that news shocks raise output temporarily, but significantly. This holds both in an application with rankings in the form of heterogeneity restrictions and in another applications with slope restrictions as rankings. Ranking restrictions also narrow bounds on variance decompositions. For example, the bound of the contribution of news shocks to the forecast error variance of output narrows by about 30 pp at the one‐year horizon. While misspecification can be a concern with added restrictions, they are consistent with the data in our applications.

Demand, supply, monetary policy, and oil price shocks in the Russian economy (Analysis based on the BVAR model with sign restrictions)

This paper considers a simple Bayesian vector autoregressive model for the Russian economy based on data for real GDP, GDP deflator and oil price as an exogenous variable that acts as a proxy variable for the terms of trade. Along with the impact of oil price shocks, the model estimates the impact of supply and demand shocks, the identification of which is based on the approach of sign restrictions. According to the results obtained, at the end of 2014 and in 2015, demand shocks had a positive impact on GDP growth, which can be interpreted as a positive effect of the ruble devaluation at the end of 2014. In the next years, demand shocks led mainly to a slowdown in economic growth. The paper also attempts to identify monetary policy shocks and assesses their impact on GDP, household consumption and investment. According to the results, the effect of monetary shocks in 2015—2019 on all endogenous variables was negative. However, an increase in the interest rate at the end of 2014 is identified mostly as an endogenous reaction to other shocks, and the effect of the monetary shock on GDP in 2015 is nearly zero. In 2017, monetary shocks slowed down GDP by 0.92 percentage points.