Browsing by Subject "Business cycles"

Now showing 1 - 7 of 7
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    A New Approach to Dating the Reference Cycle
    (Taylor & Francis Group, 2020-07-01) Camacho, Maximo; Gadea, María Dolores; Gómez Loscos, Ana; Métodos Cuantitativos para la Economía y la Empresa
    This article proposes a new approach to the analysis of the reference cycle turning points, defined on thebasis of the specific turning points of a broad set of coincident economic indicators. Each individual pair ofspecific peaks and troughs from these indicators is viewed as a realization of a mixture of an unspecifiednumber of separate bivariate Gaussian distributions whose different means are the reference turningpoints. These dates break the sample into separate reference cycle phases, whose shifts are modeled by ahidden Markov chain. The transition probability matrix is constrained so that the specification is equivalentto a multiple change-point model. Bayesian estimation of finite Markov mixture modeling techniques issuggested to estimate the model. Several Monte Carlo experiments are used to show the accuracy of themodel to date reference cycles that suffer from short phases, uncertain turning points, small samples, andasymmetric cycles. In the empirical section, we show the high performance of our approach to identifyingthe US reference cycle, with little difference from the timing of the turning point dates established by theNBER. In a pseudo real-time analysis, we also show the good performance of this methodology in terms ofaccuracy and speed of detection of turning point dates.
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    Econometric methods for business cycle dating
    (2023-12-13) Camacho, Maximo; Gadea, M. Dolores; Métodos Cuantitativos para la Economía y la Empresa
    Business cycle dating helps in developing economic analysis and is useful for economic agents whether they be policy makers, investors or academics. This paper reviews old and recent research on dating the reference cycle turning points and is intended as a guide to the applied researcher. All these methods provide a statistical alternative to cycle dating committees, although full automatism and researcher’s art could be complements rather than substitutes in some dating scenarios. Our survey divides the dating literature into two groups with different approaches to dating the business cycle from a set of coincident economic indicators: averagethen- date or date-then average. In both cases, the dating techniques can be divided into nonparametric and parametric. The paper shows the theoretical foundations of both types of techniques and describes in detail the algorithms or estimation methods necessary for their implementation. Finally, the paper describes empirical applications of the different methods with data of different frequencies, trying to show how they work in practice and pointing out their advantages and disadvantages. This empirical illustrations include a compilation of the codes in different languages (R, Matlab or Gauss). In our opinion, future research should focus on developing methods that are robust to changes in volatility or large outliers and on exploring the usefulness of big data sources and the classification ability offered by machine learning methods.
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    Evaluating OECD’s main economic indicators at anticipating recessions
    (Wiley, 2020-05-31) Camacho, Maximo; Palmieri, Gonzalo; Métodos Cuantitativos para la Economía y la Empresa
    Using receiver operating characteristic (ROC) techniques, we evaluate the predictive content of the monthly main economic indicators (MEI) of the Organization for Economic Co-operation and Development (OECD) for predicting both growth cycle and business cycle recessions at different horizons. From a sample that covers 123 indicators for 32 OECD countries as well as for Brazil, China, India, Indonesia, the Russian Federation, and South Africa, our results suggest that the OECD's MEI show a high overall performance in providing early signals of economic downturns worldwide, albeit they perform a bit better at anticipating business cycles than growth cycles. Although the performance for OECD and non-OECD members is similar in terms of timeliness, the indicators are more accurate at anticipating recessions for OECD members. Finally, we find that some single indicators, such as interest rates, spreads, and credit indicators, perform even better than the composite leading indicators.
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    Factor models for large and incomplete data sets with unknown group structure
    (Elsevier B.V. on behalf of International Institute of Forecasters., 2023-07) Camacho, Maximo; Lopez-Buenache, German; Métodos Cuantitativos para la Economía y la Empresa
    Most economic applications rely on a large number of time series, which typically have a remarkable clustering structure and they are available over different spans. To handle these databases, we combined the expectation–maximization (EM) algorithm outlined by Stock and Watson (JBES, 2002) and the estimation algorithm for large factor models with an unknown number of group structures and unknown membership described by Ando and Bai (JAE, 2016; JASA, 2017) . Several Monte Carlo experiments demonstrated the good performance of the proposed method at determining the correct number of clusters, providing the appropriate number of group-specific factors, identifying error-free group membership, and obtaining accurate estimates of unobserved missing data. In addition, we found that our proposed method performed substantially better than the standard EM algorithm when the data had a grouped factor structure. Using the Federal Reserve Economic Data FRED-QD, our method detected two distinct groups of macroeconomic indicators comprising the real activity indicators and nominal indicators. Thus, we demonstrated the usefulness of our group-specific factor model for studies of business cycle chronology and for forecasting purposes.
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    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.
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    The Great Recession. Worse than ever?
    (2018) Camacho, Maximo; Gadea, M. Dolores; Perez-Quiros, Gabriel; Métodos Cuantitativos para la Economía y la Empresa
    We develop an international comparative assessment of the Great Recession, in terms of the features that characterize the form of the recession phases, namely length, depth and shape. The potential unobserved heterogeneity in the international recession characteristics is modeled by a finite mixture model. Using Bayesian inference via Gibbs sampling, the model classiffies the Great Recession suffered by a large number of countries into dfferent clusters, determining its severity in cross section and time series and dimensions. Our results suggest that the business cycle features of the Great Recession are not dfferent from others in an international perspective. By contrast, we show that the only distinctive feature of the Great Recession wasits unprecedented degree of synchronicity.
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    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.