The current paper experiments with the conception and analytical model of principle components. The analysis utilizes data of Sudan to test the model fitness and data suitability. All the country's economic sectors are used in the analysis which also integrates high, medium and low income strata...

Donald Trump won the election in 2016 largely because enough voters in three states, all in the Rustbelt, who had voted for Barack Obama in both 2008 and 2012, switched their vote from Democratic to Republican. Economic dislocations played a crucial role in these swing states or democratic...

Social science has come under a firestorm of criticism following the discovery that many findings cannot be replicated. This paper argues that increasing the use of out-of-sample tests would improve the reliability of findings in all fields, including “complex” fields such as macroeconomics....

The paper studies Non-Stationary Dynamic Factor Models such that: (1) the factors Ft are I(1) and singular, i.e. Ft has dimension r and is driven by a q-dimensional white noise, the common shocks, with q r, and (2) the idiosyncratic components are I(1). We show that Ft is driven by r-c...

The data mining technique of time series clustering is well established in many fields. However, as an unsupervised learning method, it requires making choices that are nontrivially influenced by the nature of the data involved. The aim of this paper is to verify usefulness of the time series...

There is growing literature in macroeconomics, especially on business cycle synchronization, employing different methods of time series clustering. However, even as an unsupervised learning method, this technique requires making choices that are nontrivially influenced by the nature of the data...

The paper studies Non-Stationary Dynamic Factor Models such that: (1) the factors Ft are I(1) and singular, i.e. Ft has dimension r and is driven by a q-dimensional white noise, the common shocks, with q r, and (2) the idiosyncratic components are I(1). We show that Ft is driven by r-c...

Stationarity tests are used to detect mean reversion in a certain dataset. Mean Reversion processes suggest a non-random behavior in a time series (Lo and MacKinley, 1988). Previous research has focused on studying mean reversion at stock price level (Debondt and Thaler, 1985; Lindemann et al.,...

ARFIMA models, as advocated by Jiang and Tian for use in long-term volatility forecasting, are found in a follow-up empirical study to be dominated by a certain simple historical predictor of stock price volatility at a five-year horizon. (This particular historical predictor is not recommended...