Search

In a globalised world, inflation in a given country may be becoming less responsive to domestic economic activity, while being increasingly determined by international conditions. Consequently, understanding the international sources of vulnerability of domestic inflation is turning fundamental for policy makers. In this paper, we propose the construction of Inflation-at-risk and Deflation-at-risk measures of vulnerability obtained using factor-augmented quantile regressions estimated with international factors extracted from a multi-level Dynamic Factor Model with overlapping blocks of inflations corresponding to economies grouped either in a given geographical region or according to their development level. The methodology is implemented to inflation observed monthly from 1999 to 2022 for over 115 countries. We conclude that, in a large number of developed countries, international factors are relevant to explain the right tail of the distribution of inflation, and, consequently, they are more relevant for the vulnerability related to high inflation than for average or low inflation. However, while inflation of developing low-income countries is hardly affected by international conditions, the results for middle-income countries are mixed. Finally, based on a rolling-window out-of-sample forecasting exercise, we show that the predictive power of international factors has increased in the most recent years of high inflation., Comment: 53 pages, 18 figures

This paper posits the decline in market capitalization following a monopoly breakup serves as a means to gauge how financial markets assess market power. Our research, which employs univariate structural time series models to estimate the firm's value without the breakup and juxtapose it with actual post-divestiture values, reveals a staggering drop in AT&T's value by 65% and AMX's by 32% from their pre-breakup levels. These findings underscore the contemporary valuation of monopoly rents as perceived by financial markets, highlighting the significant impact of monopoly breakup on market capitalization and the need for a deeper understanding of these dynamics., Comment: 25 pages, 5 figures

In this paper, we propose fitting unobserved component models to represent the dynamic evolution of bivariate systems of centre and log-range temperatures obtained monthly from minimum/maximum temperatures observed at a given location. In doing so, the centre and log-range temperature are decomposed into potentially stochastic trends, seasonal, and transitory components. Since our model encompasses deterministic trends and seasonal components as limiting cases, we contribute to the debate on whether stochastic or deterministic components better represent the trend and seasonal components. The methodology is implemented to centre and log-range temperature observed in four locations in the Iberian Peninsula, namely, Barcelona, Coru\~{n}a, Madrid, and Seville. We show that, at each location, the centre temperature can be represented by a smooth integrated random walk with time-varying slope, while a stochastic level better represents the log-range. We also show that centre and log-range temperature are unrelated. The methodology is then extended to simultaneously model centre and log-range temperature observed at several locations in the Iberian Peninsula. We fit a multi-level dynamic factor model to extract potential commonalities among centre (log-range) temperature while also allowing for heterogeneity in different areas in the Iberian Peninsula. We show that, although the commonality in trends of average temperature is considerable, the regional components are also relevant., Comment: 49 pages, 20 figures

We introduce a high-dimensional factor model with time-varying loadings. We cover both stationary and nonstationary factors to increase the possibilities of applications. We propose an estimation procedure based on two stages. First, we estimate common factors by principal components. In the second step, considering the estimated factors as observed, the time-varying loadings are estimated by an iterative generalized least squares procedure using wavelet functions. We investigate the finite sample features by some Monte Carlo simulations. Finally, we apply the model to study the Nord Pool power market's electricity prices and loads., Comment: 31 pages, 11 figures