The Bank Lending Channel Revisited

Disyatat, Piti

doi:10.1111/j.1538-4616.2011.00394.x

Cited by 220 publications

(117 citation statements)

References 42 publications

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“…Meanwhile, the negative coefficients of [Δ(M1/GDP)] 2 kt are statistically significant at 10% level for columns [1] through [5], except for column [3]. Hence, the empirical results show a nonlinear relationship between ΔLoans and Δ(M1/GDP) for the models described in columns [1], [4] and [5]. The estimated coefficients in columns [4] and [5] are statistically significant at 1% level.…”

Section: Resultsmentioning

confidence: 59%

“…Hence, the empirical results show a nonlinear relationship between ΔLoans and Δ(M1/GDP) for the models described in columns [1], [4] and [5]. The estimated coefficients in columns [4] and [5] are statistically significant at 1% level. Along the same line, Table 3 also shows a nonlinear relationship between ΔLoans and Δ(M2/GDP) for the models described in columns [4] and [5] with an estimated coefficient at 1% level.…”

Section: Resultsmentioning

confidence: 74%

“…The estimated coefficients in columns [4] and [5] are statistically significant at 1% level. Along the same line, Table 3 also shows a nonlinear relationship between ΔLoans and Δ(M2/GDP) for the models described in columns [4] and [5] with an estimated coefficient at 1% level. Table 3 presents the fixed-effects regression of loans (net of provisions) growth on several explanatory variables, among which is the growth of the ratio between M2 and the GDP.…”

Section: Resultsmentioning

confidence: 81%

“…In order to correct for this bias, we present specifications estimated using the method by [19]Arellano and Bond (1991), [20]Arellano and Bover (1995) and extended by [21]Blundell and Bond (1998) in columns [4] and [5]. [21]Blundell and Bond (1998) propose a system GMM estimator that combines the standard set of equations in first-differences with lagged levels as instruments, and an additional set of equation in levels with lagged first-differences as instruments to consistently estimate the model.…”

Section: Resultsmentioning

confidence: 99%

“…This requires the banks to increase interest rates on new loans due to a decline in the supply of credit [3] There are two traditional channels can explain the association between monetary policy and the evolution of the balances of bank deposits [4]. Central banks can change the level of deposits through the control of bank reserves and via manipulation of the money multiplier.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Monetary Expansion and the Banking Lending Channel

et al. 2016

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This paper examines the bank lending channel, which considers how monetary authority actions affect the variation of loans. We focus on the BRICS (Brazil, Russia, India, China and South Africa) totalizing 1254 banks from five countries in the period 2000-2012 (totalizing 13 years). The empirical results show that the effect of money supply growth on the growth of loans is non-linear and inverted U-shaped. In this context, our results show empirical evidence expansionary monetary policies do not increase the propensity of economic agents to systematically take greater risks on the market. After a certain level of money stock, increases in the money supply do not lead to increased negotiated credit.

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Section: Resultsmentioning

confidence: 59%

Section: Resultsmentioning

confidence: 74%

Section: Resultsmentioning

confidence: 81%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Monetary Expansion and the Banking Lending Channel

et al. 2016

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Monetary policy shock and impact asymmetry in bank lending channel: Evidence from the UK housing sector

et al. 2022

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Banks play a defining role in translating monetary policy shocks to pull or push‐effects in the housing market. The literature is ambiguous on the exact role of bank lending channel (BLC) in translating such effects into either moderation or acceleration of dynamics in the housing market. This paper argues that monetary policy shocks, of the same magnitude, can have asymmetric implications for a housing market via a state dependent BLC, particularly during expansion and recessionary phases of the business cycle. We test this hypothesis for the UK housing sector using a long quarterly data (1973Q1‐2015Q4) and employing Markov Switching Vector Auto Regression (MSVAR) models. Our results show that the magnitude of the bank lending channel is contingent upon the state of the economy, with a one standard deviation expansionary monetary policy shock producing a significant effect only in normal economic times. Further study on whether large cuts in policy rates could stimulate mortgage lending and whether there is impact asymmetry to dissimilar expansionary monetary policy shocks during financial crisis, we show that a sharp cut in policy rate indeed stimulates the BLC greater compared to smaller expansionary money policy shocks during recessions.

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Linear versus non‐linear modelling: A systemic analysis with empirical data on credit and investment

Vithessonthi,

Ushakova,

Schwaninger

2024

Syst Res Behav Sci

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The purpose of this article is to shed light on the capabilities of linear versus non‐linear forms of modelling complex systems. A comparative analysis is realized, which shows the differences between two established methods — regression analysis (RA) and system dynamics (SD). To realize that methodological comparison, we need a benchmark in substantive terms. Therefore, we embed our analysis in a realistic scenario. A set of hypotheses related to the banking and investment sector is tested on the basis of rich empirical macro‐ and firm‐level data. We examine (1) whether the central bank's monetary policy affects the lending rate of commercial banks and (2) whether the lending rate of the commercial banks affects non‐financial firms' investment. A RA is conducted, to test our hypotheses on the assumption of linear relationships between the variables. To explore the non‐linear relationships between the variables, and to test the robustness of our regression results, we then model the system under investigation using the SD method(ology). The regression results show that the monetary policy rate has a positive and immediate effect on the lending rates and a negative but lagged effect on the bank loan supply. The long‐term lending rates are negatively associated with corporate investment, whilst the bank loan supply has no direct effect on corporate investment. Although the regression results indicate that monetary policy rate has no direct effect on corporate investment, the SD model shows that the monetary policy rate exerts a negative effect on corporate investment. Hence, the two types of modelling can come to different results, given the distinct implications of the exogenous perspective (i.e. the exogenous condition of predictor variables typically required in RA) and the endogenous perspective (i.e. the predictor variables are endogenized, as in SD). Comparing the outcomes of the two methodologies, we conclude that they complement each other. RA is easier to model. SD enhances the RA method by enabling a better understanding of non‐linear phenomena.

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The Bank Lending Channel Revisited

Cited by 220 publications

References 42 publications

Monetary Expansion and the Banking Lending Channel

Monetary Expansion and the Banking Lending Channel

Monetary policy shock and impact asymmetry in bank lending channel: Evidence from the UK housing sector

Linear versus non‐linear modelling: A systemic analysis with empirical data on credit and investment

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