On the typical spectral shape of an economic variable

By Daniel Levy, Hashem Dezhbakhsh. 2003

347 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e31b89e0f922cc6a9b44e89233fa7b74c2cd805a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

Citations in the corpus

Long Cycles in Growth: Explorations Using New Frequency Domain Techniques with US Data (2010, neutral): “(page 155) Granger (1966)”
Recursive Macroeconomic Theory (2000, background): “See chapter 11 for an extensive analysis of the one-sector growth model when the government levies time-varying flat rate taxes on consumption, capital, and labor as well as offering an investment tax credit.”
On History of Mathematical Economics: Application of Fractional Calculus (2019, background): “Granger (see preprint [22], paper [23], the collection of the works [24,25]) was the first to point out long-term dependencies in economic data.”
Measuring the frequency dynamics of financial connectedness and systemic risk (2015, neutral): “The role of covariance among the processes can be studied through the exclusion of the covariance terms, and the role of similarity can be examined through individual spectral densities; however, as mentioned, most of the economic series have similar spectral densities (Granger, 1966).”
International Evidence on Output Fluctuation and Shock Persistence (2003, positive): “(6)Levy and Dezhbakhsh (2002) estimate the spectra of output levels for the 58 countries in our sample and find that the level series have strikingly similar spectra that exhibit the shape that Granger (1966) calls ‘‘the typical spectral shape’’ for economic variables measured in level—the spectral ”
Quantile coherency: A general measure for dependence between cyclical economic variables (2015, positive): “After Granger (1966) studied the behaviour of single time series, important literature using cross-spectral analysis to identify the dependence between variables quickly emerged (from Granger (1969) to more recent Croux et al. (2001)).”
Comparing Alternative Predictors Based on Large-Panel Factor Models (2012, neutral): “Using a large panel of US macroeconomic variables, Stock and Watson (2004a) find that both methods perform similarly (although they focus on the weighting of the idiosyncratic and not on the dynamic restrictions), while Boivin and Ng (2005) find that SW’s method largely outperforms the FHLR’s and, i”
Using wavelets to decompose the time–frequency effects of monetary policy (2008, background): “For example, spectral techniques can be used to identify some stylized business cycle facts [21,28], and seasonal components [37,50], or to highlight different relations among economic variables at distinct frequencies, such as the paradoxical relations between production and inventories [51].”
Estimating the Parameters of a Fractional Brownian Motion by Discrete Variations of its Sample Paths (2001, background): “…usd to uccessfully model data exhibiting long-range dependence and arising ina variety of different scientific fields, including hydrology (McLeod etal., 1978), biology (Collins and De Luca, 2000), medicine (Kuklinski etal., 1989), economics (Granger, 1966) or traffic network (Willinger, 1995).”
Bilateral trade and shocks in political relations: Evidence from China and some of its major trading partners, 1990–2013 (2017, background): “In the time series literature, this type of spurious causality is known as the “temporal aggregation” or sampling bias (Granger, 1966, 1969; Marcellino, 1999; Wei, 1982; Breitung and Swanson, 2002; Taylor, 2001; etc.).”
Spectral Analysis for Economic Time Series (2005, background): “[20]) and on the general spectral structure of economic data [12].”
A theory of robust long-run variance estimation (2007, background): “For c51, such a spectral density is a very rough approximation of the typical spectral shape of economic time series as estimated by Granger (1966), with substantially more spectral mass at low frequencies compared to higher frequencies.”
Fractional) Beta Convergence (2000, background): “This is what Granger (1966) de…nes to be the “typical spectral shape of an economic variable” and it is the main feature of a long memory process 9 .”
Robust Spectral Analysis (2011, background): “Spectral analysis has traditionally played an important role in the analysis of economic time series; see, among many others, Granger (1966), Sargent (1987, chap.”