Data and Code for: "Going Negative at the Zero Lower Bound: The Effects of Negative Nominal Interest Rates"

Principal Investigator(s):  View help for Principal Investigator(s) Mauricio Ulate, Federal Reserve Bank of San Francisco

Version:  View help for Version V1

Name File Type Size Last Modified
  Data 08/03/2020 07:49:PM
  Model 09/23/2020 12:20:PM
README.pdf application/pdf 289 KB 09/23/2020 08:12:AM

Project Citation: 

Ulate, Mauricio. Data and Code for: “Going Negative at the Zero Lower Bound: The Effects of Negative Nominal Interest Rates.” Nashville, TN: American Economic Association [publisher], 2020. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-12-17. https://doi.org/10.3886/E120506V1

Project Description

Summary:  View help for Summary
After the Great Recession several central banks started setting negative nominal interest rates in an expansionary attempt, but the effectiveness of this measure remains unclear. Negative rates can stimulate the economy by lowering the rates that commercial banks charge on loans, but they can also erode bank pro fitability by squeezing deposit spreads. This paper studies the effects of negative rates in a new DSGE model where banks intermediate the transmission of monetary policy. I use bank-level data to calibrate the model and find that monetary policy in negative territory is between 60% and 90% as effective as in positive territory.

Scope of Project

Subject Terms:  View help for Subject Terms Banks; monetary policy; interest rates
JEL Classification:  View help for JEL Classification
      E32 Business Fluctuations; Cycles
      E44 Financial Markets and the Macroeconomy
      E52 Monetary Policy
      E58 Central Banks and Their Policies
      G21 Banks; Depository Institutions; Micro Finance Institutions; Mortgages
Geographic Coverage:  View help for Geographic Coverage 19 Advanced Countries
Time Period(s):  View help for Time Period(s) 1990 – 2018
Collection Date(s):  View help for Collection Date(s) 2018 – 2019
Universe:  View help for Universe The paper uses data of three types of banks: 1) Bank Holding Companies, 2) Retail & Consumer Banks, 3) Universal Commercial Banks, as collected by Fitch Solutions, in 19 countries: 1) Australia, 2) Canada, 3) Norway, 4) United States, 5) United Kingdom, 6) Switzerland, 7) Denmark, 8) Japan, 9) Sweden, 10) Austria, 11) Belgium, 12) Finland, 13) France. 14) Germany, 15) Italy, 16) Luxembourg, 17) Netherlands, 18) Portugal, 19) Spain, annually between 1990 and 2018.
Data Type(s):  View help for Data Type(s) administrative records data

Methodology

Data Source:  View help for Data Source Fitch Solution, 2018-2019.
Unit(s) of Observation:  View help for Unit(s) of Observation Banks

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V1 [2020-12-17]

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