Maximo Camacho

Introducing the Euro-Sting: Short 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 uncertainty.

Antimicrobial drug use and methicillin-resistant Staphylococcus aureus, Aberdeen, 1996-2000.

Relationships between antimicrobial use and MRSA prevalence are analyzed in Aberdeen, Scotland.

Short-run forecasting of the euro-dollar exchange rate with economic fundamentals

We propose a fundamentals-based econometric model for the weekly changes in the euro-dollar rate with the distinctive feature of mixing economic variables quoted at different frequencies. The model obtains good in-sample fit and, more importantly, encouraging out-of-sample forecasting results at horizons ranging from one-week to one month. Specifically, we obtain statistically significant improvements upon the hard-to-beat random-walk model using traditional statistical measures of forecasting error at all horizons. Moreover, our model obtains a great improvement when we use the direction of change metric, which has more economic relevance than other loss measures. With this measure, our model performs much better at all forecasting horizons than a naive model that predicts the exchange rate as an equal chance to go up or down, with statistically significant improvements.

Commodity prices and the business cycle in Latin America: Living and dying by commodities

We analyze the dynamic interactions between commodity prices and output growth of the seven biggest Latin American exporters: Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Venezuela. Using a novel definition of Markov-switching impulse response functions, we find that the response of each countrys output growth to commodity price shocks is time dependent, size dependent, and sign dependent. The major evidence of asymmetries in output growth responses occurs when commodity price shocks lead to regime shifts. Thus, we conclude that the design of optimal countercyclical stabilization policies should consider that the reactions of economic activity vary considerably across business cycle regimes.

Finite Sample Performance of Small Versus Large Scale Dynamic Factor Models

We 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 indicators.

EXTRACTING NONLINEAR SIGNALS FROM SEVERAL ECONOMIC INDICATORS

We 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 specification (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 data.

SPAIN-STING: SPAIN 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 operates 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 anticipating the recent and sudden downturn.

Real-time forecasting US GDP from small-scale factor models

We show that the single-index dynamic factor model developed by Aruoba and Diebold (Am Econ Rev, 100:20-24, 2010) to construct an index of US business cycle conditions is also very useful for forecasting US GDP growth in real time. In addition, we adapt the model to include survey data and financial indicators. We find that our extension is unequivocally the preferred alternative for computing backcasts. In nowcasting and forecasting, our model is able to forecast growth as well as AD and better than several baseline alternatives. Finally, we show that our extension could also be used to infer US business cycles with great accuracy.

Jump-and-Rest Effect of U.S. Business Cycles

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

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 downturn.