Browsing by Subject "Time Series"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Publication
    Open Access
    Forecasting travelers in Spain with Google queries
    (Sage Journals Home, 2017-10-31) Camacho, Maximo; Pacce, Matias; Métodos Cuantitativos para la Economía y la Empresa
    We examine whether Google query trends helps economic agents with predictions about the checking in and overnight stays of travelers in Spain in real time. Using a dynamic factor approach and a real-time database of vintages that reproduces the exact information that was available to a forecaster at each particular point in time, we show that the models including query trends outperform models that exclude these leading indicators. In this way, we aim to contribute to the literature on the link between the Internet and the tourismmarkets.
  • Thumbnail Image
    Publication
    Open Access
    Inference on filtered and smoothed probabilities in Markov-switching autoregressive models
    (Taylor & Francis Group, 2018-05-22) Alvarez, Rocio; Camacho, Maximo; Ruiz, Manuel; Métodos Cuantitativos para la Economía y la Empresa
    We derive a statistical theory that provides useful asymptotic approximations to the distributions of the single inferences of filtered and smoothed probabilities, derived from time series characterized by Markov-switching dynamics. We show that the uncertainty in these probabilities diminishes when the states are separated, the variance of the shocks is low, and the time series or the regimes are persistent. As empirical illustrations of our approach, we analyze the U.S. GDP growth rates and the U.S. real interest rates. For both models, we illustrate the usefulness of the confidence intervals when identifying the business cycle phases and the interest rate regimes.
  • Thumbnail Image
    Publication
    Open Access
    Markov-switching dynamic factor models in real time
    (2018) Camacho, Maximo; Perez-Quiros, Gabriel; Poncela, Pilar; Métodos Cuantitativos para la Economía y la Empresa
    We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as mixed sampling frequencies and ragged-edge data. First, we evaluate the theoretical gains of using data that are available promptly for computing probabilities of recession in real time. Second, we show how to estimate the model that deals with unbalanced panels of data and mixed frequencies, and examine the benefits of this extension through several Monte Carlo simulations. Finally, we assess its empirical reliability for the computation of real-time inferences of the US business cycle, and compare it with the alternative method of forecasting the probabilities of recession from balanced panels.
  • Thumbnail Image
    Publication
    Open Access
    Regional business cycle phases in Spain
    (2019-06-04) Camacho, Maximo; Pacce, Matias; Ulloa, Camilo; Métodos Cuantitativos para la Economía y la Empresa
    We characterize regional business cycles for Spain using monthly Social Security affiliations. Based on a set of Markov-switching models, we find substantial synchronization of regional business cycles, which has increased since the Great Recession. We do however evidence a regional leading and lagging performance that repeats itself across the different recessions. Typically, earlier signals of national recessions appear in the Islands and Valencia, and are propagated from the periphery to the center. Moreover, north-western regions tend to start the regional recoveries with a significant lag.
  • Thumbnail Image
    Publication
    Open Access
    The propagation of industrial business cycles
    (Cambridge University Press, 2017-09-21) Camacho, Maximo; Leiva-Leon, Danilo; Métodos Cuantitativos para la Economía y la Empresa
    This paper examines the business cycle linkages that propagate industry-specific business cycle shocks throughout the economy in a way that (sometimes) generate aggregated cycles. The transmission of sectorial business cycles is modelled through a multivariate Markov-switching model, which is estimated by Gibbs sampling. Based on nonparametric density estimation approaches, we find that the number and location of modes in the distribution of industrial dissimilarities change over the business cycle. There is a relatively stable trimodal pattern during expansionary and recessionary phases characterized by highly, moderately and lowly synchronized industries. However, during phase changes, the density mass spreads out from the central part to the higher end of lowly synchronized industries.