Nonlinear Models in Macroeconometrics

Teräsvirta, Timo

doi:10.1093/acrefore/9780190625979.013.177

Cited by 8 publications

(7 citation statements)

References 51 publications

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“…Alternatives to IVAR frameworks -such as, e.g., regime switching frameworks or smooth transition VARs -are available to capture the nonlinear e¤ects of macroeconomic shocks (for a recent survey, see Teräsvirta (2018)). We prefer to employ the IVAR framework (1) for three reasons.…”

Section: Nonlinear Empirical Methodologymentioning

confidence: 99%

Uncertainty and Monetary Policy During the Great Recession

2021

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Section: Nonlinear Empirical Methodologymentioning

confidence: 99%

Uncertainty and Monetary Policy During the Great Recession

2021

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“…The forecasting literature over the past two decades proposed numerous econometric approaches to account for time series peculiarities across business cycles, such as regime shifts, time‐varying dynamics, non‐Gaussianity and stochastic volatility, to name a few (for comprehensive recent discussions see, among others, Chan, 2017; Chan & Hsiao, 2014; Koop & Korobilis, 2010; Stock & Watson, 2016, 2017; Teräsvirta, 2018). While these advances yielded improvements for in‐sample estimations of forecasting models, the forecasting literature has so far predominantly assessed model performance over the full evaluation sample and thereby devoted less attention to the very same kind of business cycle asymmetries for the out‐of‐sample performance evaluations of forecasting models (cf.…”

Section: Introductionmentioning

confidence: 99%

State‐dependent evaluation of predictive ability

Siliverstovs

Wochner

2020

Journal of Forecasting

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This study systematically broadens the relevance of possible model performance asymmetries across business cycles in the spirit of the recent state-dependent forecast evaluation literature (e.g. Chauvet & Potter, 2013) to hundreds of macroeconomic indicators and deepens the forecast evaluation of the recent factor model literature on hundreds of target variables (e.g. Stock & Watson, 2012b) in a state-dependent manner. Our results are consistent with both strands of the literature and generalize the former to over 200 macroeconomic indicators and differentiate the latter across three levels of temporal granularity:We document systematic model performance differences in both absolute and relative terms across business cycles (longitudinal) as well as across variable groups (cross-sectional) and find these performance differences to be robust across several alternative specifications. The cross-sectional prevalence and robustness of state dependency shown in this article encourages economic forecasters to complement model performance assessments with a state-dependent evaluation of predictive ability.

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“…For the applied researcher, this means that the estimated moduli from linear VARs do not contain useful information about possible instabilities of the underlying process. Provided there are theoretical priors about the nature of the underlying nonlinearity, nonlinear time series models may be a useful option (see Kilian and Lütkepohl, 2017; Teräsvirta et al, 2010; Teräsvirta, 2018). For example, smooth transition models might adequately approximate the sigmoid nonlinearities found in most macroeconomic limit cycle models.…”

Section: Discussionmentioning

confidence: 99%

“…Second, it is related to the literature on nonlinear time series models, such as threshold or smooth transition (vector) autoregressive models (Teräsvirta, Tjøstheim and Granger, 2010; Kilian and Lütkepohl, 2017; Teräsvirta, 2018), which add flexible nonlinearities to workhorse linear models. Most closely related is the study by Beaudry et al .…”

Section: Introductionmentioning

confidence: 99%

Estimating Nonlinear Business Cycle Mechanisms with Linear Vector Autoregressions: A Monte Carlo Study*

Köhler

Jump

2022

Oxf Bull Econ Stat

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The paper investigates how well linear vector autoregressions (VARs) identify endogenous cycle mechanisms and cycle frequencies when the underlying process is a nonlinear limit cycle. We conduct Monte Carlo simulations with five nonlinear models in which cycles are driven by the interaction of two state variables. We find that while linear VARs quantitatively underestimate the strength of the interaction mechanism, they successfully identify the qualitative presence of a cycle mechanism in most cases (55%-100%). Our results further suggest that linear VARs are surprisingly successful at estimating cycle frequencies of nonlinear processes.'some systems of low-order linear stochastic difference equations with a non-oscillatory deterministic part, and therefore no cycle, display key business cycle features [...] I thus do not refer to business cycles, but rather to business cycle phenomena, which are nothing more nor less than a certain set of statistical properties of a certain set of important aggregate time series.' Recently, however, the idea of endogenous cycles has re-emerged in macroeconomic theory. Azariadis (2018) and Gal í (2018), for example, call for new models with complex eigenvalues and endogenous propagation mechanisms to account for periodic fluctuations JEL Classification numbers: C15, C32, E32. *We thank the editor Jonathan Temple, an anonymous referee,

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Nonlinear Models in Macroeconometrics

Cited by 8 publications

References 51 publications

Uncertainty and Monetary Policy During the Great Recession

Uncertainty and Monetary Policy During the Great Recession

State‐dependent evaluation of predictive ability

Estimating Nonlinear Business Cycle Mechanisms with Linear Vector Autoregressions: A Monte Carlo Study*

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