This paper studies the dynamics of Mexican inflation by using a wavelet multiresolution analysis on 16 indexes of the Mexican Consumer Price Index. This enables us to estimate the long-term trend, seasonality, and local shocks of the inflation series, even when the series are non-stationary. The...

In this study we combine clustering techniques with a moving window algorithm in order to filter financial market data outliers. We apply the algorithm to a set of financial market data which consists of 25 series selected from a larger dataset using a cluster analysis technique taking into...

This paper uses the wavelet methodology to analyze the dynamics of inflation in Mexico at different frequencies over time. First, we analyze the monthly behavior of the headline, core, and noncore inflation from January 2007 to December 2018. Subsequently, the decomposition shows that the shocks...

In machine learning and data science literature, clustering is the task of dividing the observations (data points) into several categories in such a way that data points falling into one group are being dissimilar than the data points falling to the other groups such that the variation within a...

The paper offers an alternative approach to analyzing stock market time series data. The purpose is to develop descriptive, more intuitive, and closer to reality analogs of the behavior of US stock market prices, as indexed by the S&P500 stock price index covering the period October 2003 to...

The classical theory about foreign exchange rate explains its fluctuations as the resulting of a random walk motion. In this paper, such a theory is put into question by performing Brock, Dechert and Scheinkman's (1987) test on the Austrian Schilling - US Dollars exchange rate for the period...

In this paper, we present an evolutionary model of industry dynamics yielding endogenous business cycles with 'Keynesian' features. The model describes an economy composed of firms and consumers/workers. Firms belong to two industries. The first one performs R&D and produces heterogeneous...

Vector autoregressions (VARs) are economically interpretable only when identified by being transformed into a structural form (the SVAR) in which the contemporaneous variables stand in a well-defined causal order. These identifying transformations are not unique. It is widely believed that...

The Scaled Model of Error has gained considerable popularity during the past ten years as a device for computing probabilistic population forecasts of the cohort-component type. In this report we investigate how sensitive probabilistic population forecasts produced by means of the Scaled Model...