Errors-in-variables estimation with wavelets

Gençay, Ramazan; Gradojević, Nikola

doi:10.1080/00949655.2010.495073

Cited by 24 publications

(7 citation statements)

References 42 publications

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“…In addition, wavelets have also been used in economic and financial forecasting (see the studies by Michis 2014bMichis , 2015bMichis and Guy 2017;Rua 2017;Caraiani 2017;Risse 2019), econometric estimation (Michis and Sapatinas 2007;Gençay and Gradojevi 2011;Gilles et al 2009;Jensen 2000) and testing (Hong and Kao 2004;Fan and Gençay 2010;Gençay and Signori 2015), as well as in filtering-denoising applications (Fleming et al 2000;Capobianco 2003;Bruzda 2014;Michis 2011). Rua and Nunes (2009) analyzed international comovements between economic sector returns over different time scales.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiscale Partial Correlation Clustering of Stock Market Returns

Michis

2022

JRFM

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This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.

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Section: Literature Reviewmentioning

confidence: 99%

Multiscale Partial Correlation Clustering of Stock Market Returns

Michis

2022

JRFM

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“…The missing data are concentrated either at the beginning of the sample or in coincidence with war periods (for several European countries). For countries with missing data within the sample, such as Belgium, Germany, Spain, France, Netherlands, and Norway, co-integration analysis was performed using a wavelet-based approximation of the aggregate series, which yielded unbiased and consistent estimates for the intercept and slope parameters, as in Ramsey et al (2010), Gencay andGradejovic (2011), andRamsey (2012). In particular, wavelet multi-resolution decomposition analysis, by returning at each step a set of averages (along with a set of differences between adjacent averages) based on different window lengths, allowed us to obtain a collection of approximations of the original signal, from finer (S 1 ) to coarser (S 4 ) resolution levels.…”

Section: Dataset and Literaturementioning

confidence: 99%

Long swings in the growth of government expenditure: an international historical perspective

Gallegati

Tamberi

2022

Public Choice

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Adopting an international historical perspective, this study aims to identify the main empirical regularities in the long-run growth pattern of government expenditure. The application of parametric and non-parametric analyses to a sample of developed countries observed over the period 1880–2018 allows us to detect two main findings. The first is that, beyond the long-term growth of government expenditures in absolute terms, there is evidence for three expansionary long waves corresponding to the booms before and during the twentieth century’s two world wars, along with the ‘golden age of public sector intervention’. The latter refers to the decline in cross-country heterogeneity in the trends and composition of absolute growth of government expenditure since the 1960s. The ‘ratchet phenomenon’ in the pre-WWII period and the shift in ideological focus from market to government failures in the last decades of the twentieth century provide explanations that complement Wagner’s law and are consistent with the observed long-term evolution of the growth of government expenditure.

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“…Then, we extend our result to the model with Fourier-oscillating noises by developing Delaigle-Meister's technique (Delaigle and Meister 2011) from one-dimensional to multidimensional cases. Wavelet estimators are widely used in regression estimation (Li et al 2008;Chesneau 2010;Gencay and Gradojevic 2011;Chaubey et al 2013). We finally study the strong consistency of wavelet estimators.…”

Section: Introductionmentioning

confidence: 99%