Testing For Threshold Nonlinearity in Short-Term Interest Rates

Gospodinov, Nikolay

doi:10.1093/jjfinec/nbi016

Cited by 27 publications

(36 citation statements)

References 58 publications

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“…For instance, Chiang and Doong (2001) found that for daily stock returns the GARCH parameters of a TAR-GARCH(1,1) model were highly significant. Gospodinov (2005) used a TAR framework with GARCH errors to test for threshold nonlinearity in short-term interest rates and concluded that allowing for threshold nonlinearities in conditional mean and variance led to significant improvements of short-term forecasts.…”

Section: Interval Forecastsmentioning

confidence: 99%

Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models

Misiorek¹,

Trueck²,

Weron³

2006

Studies in Nonlinear Dynamics &Amp; Econometrics

153

151

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In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR/ARX ("X" stands for exogenous/fundamental variable -system load in our study), AR/ARX-GARCH, TAR/TARX and Markov regime-switching models to California Power Exchange (CalPX) system spot prices. We then use them for out-ofsample point and interval forecasting in normal and extremely volatile periods preceding the market crash in winter 2000/2001. We find evidence that (i) non-linear, threshold regime-switching (TAR/TARX) models outperform their linear counterparts, both in point and interval forecasting, and that (ii) an additional GARCH component generally decreases point forecasting efficiency. Interestingly, the former result challenges a number of previously published studies on the failure of non-linear regime-switching models in forecasting. * The authors are grateful to Dick van Dijk and two anonymous referees for insightful comments and suggestions. This work was supported in part by KBN grant 4-T10B-030-25 (to Misiorek).

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Section: Interval Forecastsmentioning

confidence: 99%

Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models

Misiorek¹,

Trueck²,

Weron³

2006

Studies in Nonlinear Dynamics &Amp; Econometrics

153

151

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“…For example, different authors suggest thresholdtype nonlinearities in business cycle dynamics (Beaudry and Koop 1993;Potter 1995;Pesaran and Potter 1997), unemployment (Koop and Potter 2004), interest rates (Tsay 1998;Hansen and Seo 2002;Gospodinov 2005), financial conditions (Galbraith 1996;Balke 2000;Atanasova 2003), the effects of monetary policy on output (Sander and Kleimeier 2004;Donayre 2014Donayre , 2015, the effects of fiscal policy on output Gorodnichenko 2012, 2013;Fazzari, Morley, and Panovska 2015), and exchange rates (Taylor 2001;Bec, BenSalem, and Carrasco 2004;Sarno, Taylor, and Chowdhury 2004;Juvenal and Taylor 2008).…”

Section: Outliers In Economic Time Seriesmentioning

confidence: 99%

Outliers and persistence in threshold autoregressive processes

Ahmad

Donayre

2016

Studies in Nonlinear Dynamics &Amp; Econometrics

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This paper uses Monte Carlo simulations to investigate the effects of outlier observations on the properties of linearity tests against threshold autoregressive (TAR) processes. By considering different specifications and levels of persistence for the data-generating processes, we find that additive outliers distort the size of the test and that the distortion increases with the level of persistence. In addition, we also find that larger additive outliers can help to improve the power of the test in the case of persistent TAR processes.

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“…Gospodinov [36] finds threshold effects in both the autoregressive and GARCH errors for interest rates.…”

Section: Interest Ratesmentioning

confidence: 99%