MICA-BBVA: A factor model of economic and financial indicators for short-term GDP forecasting

In this paper we extend the Stock and Watson's (Leading economic indicators, new approaches and forecasting records, 1991) single-index dynamic factor model in an econometric framework that has the advantage of combining information from real and financial indicators published at different frequencies and delays with respect to the period to which they refer. We find that the common factor reflects the behavior of the Spanish business cycle well.We also show that financial indicators are useful for forecasting output growth, particularly when certain financial variables lead the common factor. Finally, we provide a simulated real-time exercise and prove that the model is a very useful tool for the short-term analysis of the Spanish Economy

Are the High-growth Recovery Periods Over?

We present evidence about the loss of the so-called ?plucking effect?, that is, a high-growth phase of the cycle typically observed at the end of recessions. This result matches the popular belief, presented informally by different authors, that the current recession will have permanent effects, or that the current recession will have an L shape versus the old-time recessions that have always had a V shape. Furthermore, we show that the loss of the ?plucking effect? can explain part of the Great Moderation. We postulate that these two phenomena may be due to changes in inventory management brought about by improvements in information and communications technologies.Business cycle characteristics, Great Moderation, High-growth recovery

Introducing the EURO-STING : sort term indicator of euro area growth

We propose a model to compute short-term forecasts of the Euro area GDP growth in real-time. To allow for forecast evaluation, we construct a real-time data set that changes for each vintage date and includes the exact information that was available at the time of each forecast. In this context, we provide examples that show how data revisions and data availability affect point forecasts and forecast uncertaint

Ñ-STING : España short term indicator of growth

We develop a dynamic factor model to compute short term forecasts of the Spanish GDP growth in real time. With this model, we compute a business cycle index which works well as an indicator of the business cycle conditions in Spain. To examine its real time forecasting accuracy, we use real-time data vintages from 2008.02 through 2009.01. We conclude that the model exhibits good forecasting performance in anticipating the recent and sudden downtur

The propagation of industrial business cycles

En este artículo se examina la evolución de la distribución de los vínculos de ciclos económicos a nivel de industria, los cuales son modelados con procesos markovianos multivariados y estimados por el muestreo de Gibbs. Utilizando técnicas no paramétricas, se encuentra que el número y la ubicación de las modas de la distribución de disimilitudes industriales cambian a lo largo del ciclo económico. En particular, existe un patrón trimodal relativamente estable durante las fases expansivas y recesivas, caracterizadas por industrias con sincronía alta, moderada y baja. Sin embargo, durante los cambios de fase del ciclo económico, la masa de densidad se desplaza desde las industrias moderadamente sincronizadas hasta las industrias poco sincronizadas. Esto concuerda con una transmisión secuencial de los choques que afectan a los ciclos económicos industrialesThis paper examines the evolution of the distribution of industry-specific business cycle linkages, which are modelled through a multivariate Markov-switching model and estimated by Gibbs sampling. Using non parametric 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 from moderately synchronized industries to lowly synchronized industries. This agrees with a sequential transmission of the industrial business cycle dynamic

Jump-and-rest effect of U.S. business cycles

One of the most extended empirical stylized facts about output dynamics in the United States is the positive autocorrelation of output growth. This paper shows that the positive autocorrelation can be better captured by shifts between business cycle states rather than by the standard view of autoregressive coefficients. This result is extremely robust to different nonlinear alternative models and also applies not only to output but to the most relevant macroeconomic variables.[resumen de autor

Symbolic transfer entropy test for causality in longitudinal data

In this paper, we use multiple-unit symbolic dynamics and the concept of transfer entropy to develop a non-parametric Granger causality test procedure for longitudinal data. Monte Carlo simulations show that our test displays the correct size and large power in situations where linear panel data causality tests fail such as when the linearity assumption breaks down, when the data generating process is heterogeneous across the cross-section units or presents struc-tural breaks, when there are extreme observations in some of the cross-section units, when the process displays causal dependence in the conditional variance and when the analysis involves qualitative data. We illustrate the usefulness of our proposal with the analysis of the dynamic causal relationships between public expenditure and GDP, between rm productivity and rm size in US manufacturing sectors, and among sovereign credit rating, growth and interest rates.The authors acknowledge financial support from protect PID2019-107192 GB-I00 (AEI/10.13039/501100011033) and from MINECO projects ECO2016-76178-P and ECO2015-65637-P, which are co-financed by FEDER funds. This study is part of the collaborative activities performed under the program Groups of Excellence of the Region of Murcia, the Fundacion Seneca, Science and Technology Agency of the Region of Murcia Project 19884/GERM/15

Inference on filtered and smoothed probabilities in Markov-switching autoregressive models

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.M. Camacho and M. Ruiz acknowledge the financial support from projects ECO2016-76178-P and ECO2015-65637-P, respectively. Rocio Alvarez acknowledges the financial support from project CIP16013 (Universidad Central de Chile). This study is the result of the activity carried out under the program Groups of Excellence of the region of Murcia, the Fundación Séneca, Science and Technology Agency of the region of Murcia project 19884/GERM/15

Finite sample performance of small versus large scale dynamic factor models

Incluye bibliografíaWe examine the finite-sample performance of small versus large scale dynamic factor models. Our Monte Carlo analysis reveals that small scale factor models out-perform large scale models in factor estimation and forecasting for high levels of cross-correlation across the idiosyncratic errors of series belonging to the same category, for oversampled categories and, especially, for high persistence in either the common factor series or the idiosyncratic errors. Using a panel of 147 US economic indicators, which are classified into 13 economic categories, we show that a small scale dynamic factor model that uses one representative indicator of each category yields satisfactory or even better forecasting results than a large scale dynamic factor model that uses all the economic indicatorsExaminamos las propiedades en pequeña muestra de modelos dinámicos factoriales de pequeña escala frente a modelos de dimensiones grandes. Nuestro análisis de Montecarlo revela que los modelos de pequeña escala se ajustan mejor en la estimación de los factores y predicen mejor cuando existen altos niveles de correlación entre los errores idiosincráticos de la series que pertenecen a la misma categoría económica, cuando existe una sobrerrepresentación de una determinada categoría y, especialmente, cuando existe alta persistencia del factor común o de los errores idiosincráticos. Usando un panel de 147 indicadores económicos para EEUU, que se clasifican en 13 categorías, mostramos que un modelo dinámico de pequeña escala con una sola serie por categoría da mejor resultado en predicción que usar todos los indicadore

Extracting non-linear signals from several economic indicators

Incluye bibliografíaWe develop a twofold analysis of how the information provided by several economic indicators can be used in Markov-switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully non-linear multivariate specifi cation (one-step approach) with the “shortcut” of using a linear factor model to obtain a coincident indicator which is then used to compute the Markov-switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US dataEn este trabajo analizamos cómo la información proveniente de varios indicadores económicos puede utilizarse en un modelo de factores dinámicos con estructura de cadenas de Markov para identifi car puntos de giro del ciclo económico. Primero comparamos cómo un modelo con una completa especifi cación no lineal (una sola etapa) predice los puntos de giro en comparación con un modelo donde se estima un modelo de factores dinámicos lineal y luego se computan las probabilidades de cambio de régimen usando un modelo estándar univariante al factor (dos etapas). Segundo, analizamos el hecho de incrementar nuestro conjunto de información y de dónde proviene la ganancia de incrementar el número de las variables consideradas en el modelo. Nuestros resultados sugieren que, pese a que estimar el modelo en un solo paso es mejor que estimarlo en varias etapas, la ganancia marginal disminuye cuanto mejor sean los indicadores utilizados y más variables se utilicen en la estimación del signo no lineal. Usando las cuatro series que constituyen el índice coincidente de Stock y Watson, ilustramos este resultado para la economía de EEU