从银行贷款标准到银行信贷条件：一种SVAR方法

Federal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print)ISSN 2767-3898 (Online) From Bank Lending Standards to Bank Credit Conditions: AnSVAR Approach Vihar Dalal; Daniel A. Dias; Pinar Uysal 2025-055 Please cite this paper as:Dalal,Vihar,Daniel A. Dias,and Pinar Uysal (2025).“From Bank Lending Stan-dards to Bank Credit Conditions: An SVAR Approach,” Finance and Economics Discus-sion Series 2025-055.Washington:Board of Governors of the Federal Reserve System,https://doi.org/10.17016/FEDS.2025.055. NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment.The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or theBoard of Governors. References in publications to the Finance and Economics Discussion Series (other thanacknowledgement) should be cleared with the author(s) to protect the tentative character of these papers. From Bank Lending Standards to Bank CreditConditions: An SVAR Approach∗ Vihar Dalal†Daniel A. Dias‡Pinar Uysal§ July 8, 2025 Abstract This paper uses a structural vector autoregressive (SVAR) model—identified with anexternal monetary policy instrument and sign restrictions—to derive a measure of bankcredit conditions from changes in bank lending standards. The model incorporates dataon interest rates, bank credit, and survey-based measures of bank lending standardsto identify monetary policy, credit demand, and credit supply shocks.Using theseidentified shocks, we construct a novel measure of bank credit conditions that corre-sponds to the component of credit growth that would occur if credit demand remainedunchanged, reflecting solely the impacts of monetary policy and credit supply shocks.Using this measure, we find that credit supply–driven changes in bank credit condi-tions have a stronger impact on real outcomes in the euro area, whereas monetarypolicy–driven changes play a larger role in the U.S. economy. JEL classification codes: C32, C36, G21.Keywords: Bank Credit; Bank Lending Surveys; Monetary Policy; External Instruments; Sign Restrictions; SVAR 1. Introduction Central banks must assess whether credit conditions are expansionary or restrictive to ef-fectively carry out their mandates. Excessively loose credit can fuel inflation and financialvulnerabilities through excessive lending, while overly tight conditions may dampen invest-ment and consumption, constraining growth. One way in which central banks gauge creditconditions is by surveying banks on changes in lending standards. However, these surveysare mostly focused on capturing shifts in standards, not their absolute levels or overall creditconditions.This paper proposes a method to quantify the level of bank credit conditionsusing publicly available data on changes in lending standards and a state-of-the-art struc-tural vector autoregressive (SVAR) identification methodology.Building on this measure,we examine how shifts in bank credit conditions impact key macroeconomic variables in theUnited States and the euro area. The Federal Reserve Board (Fed) has conducted the Senior Loan Officer Opinion Surveyon Bank Lending Practices (SLOOS) since 1967, and, for most of the time this survey hasbeen conducted, it has asked banks whether—during the past three months— their standardsfor approving loan applications of a certain type (e.g., commercial and industrial loans) hadtightened, remained unchanged, or eased.1 Similarly, many other central banks, such asthe Bank of Japan, the Bank of England, and the European Central Bank (ECB), also runsurveys that include similar questions about banks’ lending standards in their respectivejurisdictions.2In this paper we only use results from the Fed’s SLOOS and the ECB’s Euro- area Bank Lending Survey (BLS), but the approach we propose can be easily implementedin other countries. Because of the unique and rich information provided by the Fed’s SLOOS and the ECB’sBLS, their results have been widely utilized in academic research.In the rest of the in-troduction, we review related papers relevant to our research question to contextualize ourcontribution within the existing literature. One of the first papers to use bank lending stan-dards in the context of a vector autoregressive (VAR) model was Lown and Morgan (2006).3These authors used a six-variable VAR model and identified the shocks of interest recur-sively. Although this approach to identifying shocks was standard at the time, it has sincebeen replaced by more-advanced and less-restrictive methods. In our paper, we follow themethodology of Cesa-Bianchi and Sokol (2022) and identify shocks using an external instru-ment combined with sign restrictions. Bassett et al. (2014) use individual banks’ responsesand bank-specific and economy-wide variables to remove demand-related information fromlending standards.4 This is an important contribution