The Effect of Nonlinearity between Credit Conditions and Economic Activity on Density Forecasts

Abstract

This paper examines the effect of nonlinearities on density forecasting. It focuses on the relationship between credit markets and the rest of the economy. The possible nonlinearity of this relationship is captured by a threshold vector autoregressive model estimated on US data using Bayesian methods. Density forecasts thus account for the uncertainty in all model parameters and possible future regime changes. It is shown that considering nonlinearity can improve the probabilistic assessment of the economic outlook. Moreover, three illustrative examples are discussed to shed some light on the possible practical applicability of density forecasts derived from non‐linear models. Copyright © 2015 John Wiley & Sons, Ltd.

Time‐varying impacts of expectations on housing markets across hot and cold phases

“…Does nonlinearity also matter to characterise the housing sector during the housing boom (2002)(2003)(2004)(2005) and bust (2006)(2007)(2008)(2009) periods? Does it matter more during turbulent periods (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) than during normal times, as suggested in the literature such as Franta (2016)? This study aims to shed light on the puzzle.…”

“…In particular, this study explores the evidence for advantages of the nonlinear TAR over the conventional linear value at risk (VAR) across the recent housing boom-bust cycle. Franta (2016) demonstrates that nonlinearities play an important role; the TAR model is appropriate for modelling stress events and is useful in density forecasts (i.e., probabilistic outlook) of economic activities, as the evaluation sample extends from 2004 to the crisis period in 2008. Does nonlinearity also matter to characterise the housing sector during the housing boom (2002)(2003)(2004)(2005) and bust (2006)(2007)(2008)(2009) periods?…”

A Profit-to-Provisioning Approach to Setting the Countercyclical Capital Buffer: The Czech Example

“…Second, we estimate a nonlinear Markov-switching model to see whether the proposed indicators are able to identify levels of risk which may undermine the resilience of a financial sector. Franta (2016) analyzes the link between credit/financial markets and the real economy and shows that nonlinearities play an important role in predicting future economic developments. Similarly, Abdymomunov (2013), Dumprey and Klaus (2017), and Brave and Lopez (2017) use a Markovswitching modeling framework to assess the ability of various financial cycle measures to identify low or high financial stress periods.…”

“…First, it provides a way to investigate the presence of nonlinearities. Recently, Franta (2016) showed that in periods of tight credit conditions, nonlinearities play a significant role in the assessment of the economic outlook. Also, Plašil et al (2015) find some evidence of a potential nonlinear relationship between the Financial Cycle Index and the business cycle.…”