Credit growth, monetary policy and economic activity in a three-regime TVAR model

Avdjiev, Stefan; Zeng, Zheng

doi:10.1080/00036846.2014.916391

Cited by 31 publications

(18 citation statements)

References 31 publications

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“…In contrast to the linear VARs, the threshold VAR (TVAR) model allows for capturing a nonlinear dependence structure between macroeconomic variables (see Avdjiev & Zeng, 2014;Wolters, Teräsvirta, & Lütkepohl, 1998) and is defined as follows:…”

Section: Threshold Var Modelmentioning

confidence: 99%

Forecasting US GNP growth: The role of uncertainty

Segnon

Gupta

Bekiros

et al. 2018

Journal of Forecasting

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A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.

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Section: Threshold Var Modelmentioning

confidence: 99%

Forecasting US GNP growth: The role of uncertainty

Segnon

Gupta

Bekiros

et al. 2018

Journal of Forecasting

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“…Moreover, the level might not be stationary for some country group. 27 Estimates for c and γ can be found in the Appendix C in Table A.2. studies by Gilchrist and Mojon (2014), Bassett et al (2014), Bijsterbosch and Falagiarda (2014), Avdjiev and Zeng (2014), Gilchrist and Zakrajšek (2012), Hristov et al (2012), Serwa (2012), Helbling et al (2011), de Bondt et al (2010, Gilchrist et al (2009), Alessandro andJoão (2006), and Balke (2000), to name a few, also analyze the link between credit dynamics and economic activity in one way or another. Connecting overleveraging of banks, bank lending to the private sector, and output dynamics in a regime change model is a novel feature of our study.…”

Section: Resultsmentioning

confidence: 99%

Overleveraging in the Banking Sector: Evidence from Europe

2014

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. Overleveraging of the banking sector has been considered as one of the main causes of the 2007-09 financial crisis and the subsequent great recession. It was also of major concern for the subsequent BIS regulatory policies resulting in Basel III and its request for higher capital requirements. It has now become highly relevant for the planned European banking union. Overleveraging of the banking sector exposes the financial sector and the macroeconomy to vulnerabilities, but also, as critics state, seems to constrain credit flows to the private sector. We present here a measure of overleveraging, defined as the difference of actual and sustainable debt, conduct an empirical study on overleveraging for 40 banks in Europe, and study the vulnerabilities and credit contractions that can arise subsequently. Before the year 2004 overleveraging has not been a serious problem as leverage was on a sustainable level. However, in the run-up to the financial crisis, actual and optimal debt ran apart and the banking sector began to suffer from overleveraging. We use a nonlinear Vector STAR model to evaluate the hypothesis that periods of increasing debt levels are accompanied by more severe credit constraints than periods of low leveraging. We demonstrate this for country groups across Europe.JEL classification: C61, E32, G01

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“…In the case of the USA, Case and Shiller (1989) find some evidence of inertia in single family home 6 There is also a huge literature on nonlinearity and asymmetry of business cycles. For example, see Avdjiev and Zeng (2014), Balke (2000), Kim, Morley, and Piger (2005), and Morley and Piger (2012), among others for literature on nonlinearity in business cycles. It is certainly plausible to think that the forecasting performance of different models may depend on the state of the business cycle.…”

Section: Methodsmentioning

confidence: 99%

“…It is perfectly conceivable to imagine that the relationship between the housing market and different predictors is nonlinear or, alternatively, that there may be asymmetry in the forecasting performance that may depend upon the state of the business cycle. The literature on nonlinearity in the business cycle is huge; for example, see Avdjiev and Zeng (2014), Balke (2000), Kim et al (2005), and Morley and Piger (2012), among others. The nonlinearity in the forecasting models presented here can be a promising avenue for future research.…”

Section: Combination Forecastmentioning

confidence: 99%

Forecasting house prices in OECD economies

Kishor

Marfatia

2017

Journal of Forecasting

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In this paper, we forecast real house price growth of 16 OECD countries using information from domestic macroeconomic indicators and global measures of the housing market. Consistent with the findings for the US housing market, we find that the forecasts from an autoregressive model dominate the forecasts from the random walk model for most of the countries in our sample. More importantly, we find that the forecasts from a bivariate model that includes economically important domestic macroeconomic variables and two global indicators of the housing market significantly improve upon the univariate autoregressive model forecasts. Among all the variables, the mean square forecast error from the model with the country's domestic interest rates has the best performance for most of the countries. The country's income, industrial production, and stock markets are also found to have valuable information about the future movements in real house price growth. There is also some evidence supporting the influence of the global housing price growth in out‐of‐sample forecasting of real house price growth in these OECD countries.

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Credit growth, monetary policy and economic activity in a three-regime TVAR model

Cited by 31 publications

References 31 publications

Forecasting US GNP growth: The role of uncertainty

Forecasting US GNP growth: The role of uncertainty

Overleveraging in the Banking Sector: Evidence from Europe

Forecasting house prices in OECD economies

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