Common cycles and the importance of transitory shocks to macroeconomic aggregates

Issler, João Victor; Vahid, Farshid

doi:10.1016/s0304-3932(01)00052-6

Cited by 76 publications

(76 citation statements)

References 25 publications

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“…5 Other applications of common cycles and common trends methodology in business cycles (national, local and sectoral), macroeconomic variables, health expenditures, metals and commodities can be found in the papers, such as: Vahid and Engle (1993), Engle and Issler (1995), Issler and Vahid (2001), Narayan (2008), Narayan and Narayan (2008a, b), and Issler et al, (2014). 6 Common trends and cycles can also be introduced into the multivariate structural time series model of Harvey (1989) and Koopman et al (2000).…”

Section: Introductionmentioning

confidence: 99%

“…While, cointegration is a common feature, another common feature of interest is the presence of common serial correlation patterns, i.e., common cycles. Issler and Vahid (2001) pointed out that the joint modeling of common-trend and commoncycle restrictions to identify permanent and transitory shocks has a clear advantage over the use of common-trend restrictions only. Understandably, if common-cycle restrictions are correctly imposed, estimates of the dynamic model (traditionally, a vector autoregression (VAR) model) are more precise, and leads to more accurate measurement of the relative importance of permanent and transitory shocks.…”

Section: Introductionmentioning

confidence: 99%

“…Common features estimation approach introduced previously in the literature (e.g., Engle and Kozicki, 1993;Vahid and Engle, 1993;Issler and Vahid, 2001) require that some linear combinations formed from the first differences of the cointegrated I(1) time series be white noise and, hence, the co-movements be contemporaneous. There is indeed a quite strong assumption that does not consider other possible co-cycle structures.…”

Section: Introductionmentioning

confidence: 99%

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Common cycles and common trends in the stock and oil markets: Evidence from more than 150 years of data

2017

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This paper investigates the role of permanent and transitory shocks, within the framework of common cycles and common trends, in explaining stock and oil prices. We perform a multivariate variance decomposition analysis of monthly data on the West Texas Intermediate (WTI) oil price and the S&P500. The dataset used in the study spans a long period of 150 years and therefore contains a rich history to examine both the short-and longrun comovement properties of oil and stock prices. Given that the oil and stock markets might comove both in the short-and long-run, it is of interest to see the relative impacts of transitory and permanent shocks on both variables. We find that (log) oil price and (log) S&P 500 share a common stochastic trend for our full sample of September 1859 to July 2015, but a common cycle only exists during the post-WW II period. Full and post-WW II samples have quite different common feature estimates in terms of the impact of permanent and transitory shocks as measured by the impulse responses and forecast error variance decompositions. We also find that in the short-run oil is driven mostly by cycles (transitory shocks) and stock market is mostly driven by permanent shocks. But, permanent shocks dominate in the long-run.1

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Common cycles and common trends in the stock and oil markets: Evidence from more than 150 years of data

2017

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“…Indeed, common cycles appear in many theoretical economic models. For examples, in models of aggregate consumption behavior as discussed in Vahid and Engle (1993) and Issler and Vahid (2001), the proportion of total income that accrues to "myopic" individuals (Campbell and Mankiw, 1990), that is, individuals that consume their income entirely in every period, or excess sensitivity of consumption to current income (Flavin, 1993), provide that first differences of I(1) consumption and income share a common cycle. Note that in the consumption theory of Hall (1978) consumption and income share only a common stochastic trend.…”

Section: Common Cyclesmentioning

confidence: 99%

“…The common stochastic trend is generated by an integrated productivity shock, while the deviation of capital stock from its steady state value determines the transitional dynamics of output, consumption and investment. Other example is Campbell (1987) where the saving path implies that disposable income and consumption cointegrate (Issler and Vahid, 2001). However, it could happen that observed comovements are in contrast with the theoretical model, and some efforts are toward reconciling the model with the data: as an example, in the DSGE model of Justiniano et al (2010), an extension of the well known model of Christiano et al (2005), investment shocks are the driven forces of fluctuations over the business cycle.…”

Section: Introductionmentioning

confidence: 99%

Modelling Comovements of Economic Time Series: A Selective Survey

Centoni

Cubadda

2011

SSRN Journal

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Unit‐Root Tests

Phillips¹

2014

Wiley StatsRef: Statistics Reference Online

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In the conduct of empirical macroeconomic research, unit root, cointegration, common cycle, and related tests statistics are often constructed using logged data, even though there is often no clear reason, at least from an empirical perspective, why logs should be used rather than levels. Unfortunately, it is also the case that standard data transformation tests, such as those based on the Box-Cox transformation, cannot be shown to be consistent unless an assumption is made concerning whether the series being examined is I(0) or I( 1), so that a sort of circular testing problem exists. In this paper, we address two quite different but related issues that arise in the context of data transformation. First, we address the circular testing problem that arises when choosing data transformation and the order of integratedness. In particular, we propose a simple randomized procedure, coupled with sample conditioning, for choosing between levels and log-levels specifications in the presence of deterministic and/or stochastic trends. Second, we note that even if pretesting is not undertaken to determine data transformation, it is important to be aware of the impact that incorrect data transformation has on tests frequently used in empirical works. For this reason, we carry out a series of Monte Carlo experiments illustrating the rather substantive effect that incorrect transformation can have on the finite sample performance of common feature and cointegration tests. These Monte Carlo findings underscore the importance of either using economic theory as a guide to data transformation and/or using econometric tests such as the one discussed in this paper as aids when choosing data transformation.

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Common cycles and the importance of transitory shocks to macroeconomic aggregates

Cited by 76 publications

References 25 publications

Common cycles and common trends in the stock and oil markets: Evidence from more than 150 years of data

Common cycles and common trends in the stock and oil markets: Evidence from more than 150 years of data

Modelling Comovements of Economic Time Series: A Selective Survey

Unit‐Root Tests

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