Band 22, Heft 5 - Special issue: Perspectives on the work of Timo Terasvirta / Special issue editor: Fredj Jawadi

Over the last decades a transition from a state-own monopoly to a private business took place in the Spanish fuel sector. To figure out whether downstream prices react differently to upstream price increases than to price decreases, alternative dynamic nonlinear and asymmetric error correction models are applied to weekly price data. This paper analyse the existence of price asymmetries in the fuel market in Spain during the 2011–2016 period. In comparison with traditional asymmetric price theory literature, this paper introduces a new double threshold error correction (ECM) model (DT-ECM) and new double logistic ECM models and compares them with more common linear ECM, time varying parameter models (TV-ECM), threshold autoregressive models (T-ECM), smooth transition autoregressive (STAR) models and nonlinear error correction (Logistic-ECM) and double threshold Logistic (DT-Logistic ECM). The asymmetric results found in Spain in the oil sector, show that sophisticated bivariate short-run nonlinearities are present in the gasoline market prices and that those price reactions depend on two main aspects; whether the oil price increases or decreases and on the stage (above of below) of the prices of gasoline relative to their long-run expected crude oil prices (error correction term). Those empirical results are consistent with the economic explanations based on market power-collusion and/or with consumer having search costs.

In this article, a misspecification test in conditional volatility and GARCH-type models is presented. We propose a Lagrange Multiplier type test based on a Taylor expansion to distinguish between (G)ARCH models and unknown GARCH-type models. This new test can be seen as a general misspecification test of a large set of GARCH-type univariate models. It focuses on the short-term component of the volatility. We investigate the size and the power of this test through Monte Carlo experiments and we compare it to two other standard Lagrange Multiplier tests, which are more restrictive. We show the usefulness of our test with an illustrative empirical example based on daily exchange rate returns.

Strong consistency and (weak) distributional convergence to highly non-Gaussian limits are established for closed-form, two stage least squares (TSLS) estimators of linear and threshold ARCH ( p ) models, with special attention paid to the ARCH (1) and threshold ARCH (1) cases. Conditions supporting these results include (relatively) mild moment existence criteria that enjoy empirical support. These conditions are not shared by competing estimators like OLS. Identification of the TSLS estimators depends on asymmetry, either in the model’s rescaled errors or in the conditional variance function. Monte Carlo studies reveal TSLS estimation can sizably outperform quasi maximum likelihood (QML) in small samples and even best recently proposed two-step estimators specifically designed to enhance the efficiency of QML.

We demonstrate that analysis of long series of daily returns should take into account potential long-term variation not only in volatility, but also in parameters that describe asymmetry or tail behaviour. However, it is necessary to use a conditional distribution that is flexible enough, allowing for separate modelling of tail asymmetry and skewness, which requires going beyond the skew- t form. Empirical analysis of 60 years of S&P500 daily returns suggests evidence for tail asymmetry (but not for skewness). Moreover, tail thickness and tail asymmetry is not time-invariant. Tail asymmetry became much stronger at the beginning of the Great Moderation period and weakened after 2005, indicating important differences between the 1987 and the 2008 crashes. This is confirmed by our analysis of out-of-sample density forecasting performance (using LPS and CRPS measures) within two recursive expanding-window experiments covering the events. We also demonstrate consequences of accounting for long-term changes in shape features for risk assessment.

The monetary economics literature has highlighted four issues that are important in evaluating US monetary policy since the late 1960s: (i) time variation in policy parameters, (ii) asymmetric preferences, (iii) real-time nature of data, and (iv) heteroskedasticity. In this paper, we exploit advances in sequential monte carlo methods to estimate a time-varying nonlinear Taylor rule that addresses these four issues simultaneously. Our findings suggest that US monetary policy has experienced substantial changes in terms of both the response to inflation and to real economic activity, as well as changes in preferences. These changes cannot be captured adequately by a single structural break at the late 1970s, as has been commonly assumed in the literature, and play a non-trivial role in economic performance.

This paper investigates the effect of financial fragmentation on the monetary transmission mechanism in different Euro area economies, categorized into two groups: countries considered as “core” economies and countries characterized as “peripheral” economies. We analyze the effects of financial fragmentation on the monetary transmission mechanism through the traditional interest rate channel. To gauge the impact of changes in policy rates on the behavior of real variables such as aggregate output and employment we use a Smooth Transition VAR (VSTAR) model. Employing a nonlinear multivariate time series approach helps us capture the regime-dependent dynamics of the variables under study. The results obtained show that money market rates targeted by the central bank do not completely pass through to banks’ lending rates to firms, particularly in a financially fragmented environment. This finding supports the hypothesis of an impairment of the monetary transmission mechanism as a result of financial fragmentation. Given this impairment in some sectors and regions an accompanying credit volume policy might have been appropriate.

Inflation in India has been a major cause for concern in the recent past (2008–2012). This study examines the Indian wholesale price index inflation from 1951 to 2012 using P-star (or P * ) models after accounting for the nonlinearities in the data by establishing the presence of a nonlinear long-run equilibrium. The paper establishes the presence of a threshold vector error correction model (TVECM) between prices and their long-run equilibrium with three optimal regimes to explain the short-run and long-run dynamics based on an error correcting transition term. Based on these results, the study classifies the various regimes that Indian inflation goes through based on historical economic events. The P * models (price gap, output gap and velocity gap models) were implemented regime-wise. The price gap models (output gap and income velocity gap determine inflation) were found to be optimal in the first and second regimes and consistent with theory. The velocity gap model (which has monetarist foundations) was found to be optimal in the third regime.

This paper investigates whether a variant of a Taylor rule applied to historical monetary data of the interwar period is useful to gain a better understanding of the Fed’s conduct of monetary policy over the period 1920–1940. To this end, we considered a standard Taylor rule (using two drivers: output gap and inflation gap) and proxied them differently for robustness. Further, we extended this Taylor rule to a nonlinear framework while enabling its coefficient to be time-varying and to change with regard to the phases in business cycle, in order to better capture any further asymmetry in the data and the structural break induced by the Great Depression. Accordingly, we showed two important findings. First, the linearity hypothesis was rejected, and we found that an On/Off Taylor Rule is appropriate to reproduce the conduct of monetary policy during the interwar period more effectively (the activation of drivers only occurs per regime). Second, unlike Field [Field, A. 2015. “The Taylor Rule in the 1920s.” Working Paper], we validated the use of a Taylor rule to explain the conduct of monetary policy in history more effectively. Consequently, this nonlinear Taylor rule specification provides interesting results for a better understanding of monetary regimes during the interwar period, and offers useful complements to narrative monetary history.

In this paper, I explore the changes in international business cycles with quarterly data for the eight largest advanced economies (US, UK, Germany, France, Italy, Spain, Japan, and Canada) since the 1960s. Using a time-varying parameter model with stochastic volatility for real GDP growth and inflation allows their dynamics to change over time, approximating nonlinearities in the data that otherwise would not be adequately accounted for with linear models [Granger, Clive W.J., Timo Teräsvirta, and Heather M. Anderson. 1991. “Modeling Nonlinearity over the Business Cycle.” In NBER book Business Cycles, Indicators and Forecasting (1993) , edited by James H. Stock and Mark W. Watson, University of Chicago Press.; Granger, Clive W.J. 2008. “Non-Linear Models: Where Do We Go Next – Time Varying Parameter Models?” Studies in Nonlinear Dynamics and Econometrics 12 (3): 1–11.]. With that empirical model, I document a period of declining macro volatility since the 1980s, followed by increasing (and diverging) inflation volatility since the mid-1990s. I also find significant shifts in inflation persistence and cyclicality, as well as in macro synchronization and even forecastability. The 2008 global recession appears to have had an impact on some of this. I ground my empirical strategy on the reduced-form solution of the workhorse New Keynesian model and, motivated by theory, explore the relationship between greater trade openness (globalization) and the reported shifts in international business cycle. I show that globalization has sizeable (yet nonlinear) effects in the data consistent with the implications of the model – yet globalization’s contribution is not a foregone conclusion, depending crucially on more than the degree of openness of the international economy.