Interindustry Linkages of Prices - Analysis of Japan's Deflation

Abstract

The interactions among macroprices with leads and lags play a significant role in explaining the behavior of an aggregate price index. Thus, to understand inflation and deflation, it is essential to explore the mechanism according to which these macroprices interact with each other. On the basis of a new method, we show that, irrespective of the sources of shocks, a robust flow of changes occurs in domestic prices from upstream to downstream. Moreover, we demonstrate that macroprices change in clusters, and we identify these clusters. Firms are not symmetric. Overall, our analysis suggests that the inertia arising from input/output linkages in production explains the behavior of aggregate prices.

Untangling the complexity of market competition in consumer goods -A complex Hilbert PCA analysis

“…The former was originally introduced in [16,17,18,19,20] using the Hilbert transformation developed in [21,22,23,24,25] among others. The approach has been successfully applied in several areas of natural science and economics [26,27,12,13]. We further introduced improvements on CHPCA by [11].…”

“…Alternatively, we propose a novel method called complex Hilbert principal component analysis (CHPCA) [10,11] to unravel the complexity of the consumer choice process across multiple competitive products. CHPCA was developed originally in econophysics as an extension of Principal Component analysis (PCA) to uncover temporal comovements among variables observed in the macro economy [12] or foreign exchange markets [13]. CHPCA can handle massive high-dimensional time-series data without any strong assumptions about the phenomenon of interest.…”

E Pluribus, Quaedam: Gross Domestic Product out of a Dashboard of Indicators

“…But for the RMT to hold, it is required that no autocorrelation exists in the series and that the series are infinitely dimensional -in the sense that both N, T → ∞, with Q = T N finite. To be free from these two tight restrictions, one can alternatively rely upon the less demanding rotational random shuffling (RRS) simulations which, in the limit, converge to the same theoretical distribution of RMT (Iyetomi et al, 2011;Aoyama et al, 2017;Kichikawa et al, 2020), represented by the Marchenko-Pastur distribution:…”

Projecting XRP price burst by correlation tensor spectra of transaction networks

“…The aim of such method is to analyze high dimensional data to find key factors for the collective dynamics of many quantities. For example, it is used to study the daily returns of different stocks [13][14][15] and foreign exchange rates 16,17 , monthly macroeconomic data 18 , or different medical data such as electroencephalogram, magnetoencephalography data recording 19 . Also, D. Kondor et al applied principal component analysis on the matrices obtained from the daily network snapshots to show the relationship between the price of bitcoin with structural changes in the transaction network 20 .…”