On the Low-frequency Relationship Between Public Deficits and Inflation
Journal of Applied Econometrics,
We estimate the low-frequency relationship between fiscal deficits and inflation and pay special attention to its potential time variation by estimating a time-varying vector autoregression model for US data from 1900 to 2011. We find the strongest relationship neither in times of crisis nor in times of high public deficits, but from the mid 1960s up to 1980. Employing a structural decomposition of the low-frequency relationship and further narrative evidence, we interpret our results such that the low-frequency relationship between fiscal deficits and inflation is strongly related to the conduct of monetary policy and its interaction with fiscal policy after World War II.
Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.
In Search for Yield? Survey-based Evidence on Bank Risk Taking
Journal of Economic Dynamics and Control,
Monetary policy can have an impact on economic and financial stability through the risk taking of banks. Falling interest rates might induce investment into risky activities. This paper provides evidence on the link between monetary policy and bank risk taking. We use a factor-augmented vector autoregressive model (FAVAR) for the US for the period 1997–2008. Besides standard macroeconomic indicators, we include factors summarizing information provided in the Federal Reserve’s Survey of Terms of Business Lending (STBL). These data provide information on banks׳ new loans as well as interest rates for different loan risk categories and different banking groups. We identify a risk-taking channel of monetary policy by distinguishing responses to monetary policy shocks across different types of banks and different loan risk categories. Following an expansionary monetary policy shock, small domestic banks increase their exposure to risk. Large domestic banks do not change their risk exposure. Foreign banks take on more risk only in the mid-2000s, when interest rates were ‘too low for too long’.
Macroeconomic Factors and Micro-Level Bank Risk
Bundesbank Discussion Paper 20/2010,
The interplay between banks and the macroeconomy is of key importance for financial and economic stability. We analyze this link using a factor-augmented vector autoregressive model (FAVAR) which extends a standard VAR for the U.S. macroeconomy. The model includes GDP growth, inflation, the Federal Funds rate, house price inflation, and a set of factors summarizing conditions in the banking sector. We use data of more than 1,500 commercial banks from the U.S. call reports to address the following questions. How are macroeconomic shocks transmitted to bank risk and other banking variables? What are the sources of bank heterogeneity, and what explains differences in individual banks’ responses to macroeconomic shocks? Our paper has two main findings: (i) Average bank risk declines, and average bank lending increases following expansionary shocks. (ii) The heterogeneity of banks is characterized by idiosyncratic shocks and the asymmetric transmission of common shocks. Risk of about 1/3 of all banks rises in response to a monetary loosening. The lending response of small, illiquid, and domestic banks is relatively large, and risk of banks with a low degree of capitalization and a high exposure to real estate loans decreases relatively strongly after expansionary monetary policy shocks. Also, lending of larger banks increases less while risk of riskier and domestic banks reacts more in response to house price shocks.