Maximo Camacho

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Departamento de metodos cuantitativos,
Facultad de Economia y Empresa,
Universidad de Murcia,
 30100, Murcia, Spain

+00 34 868 887982 (Phone)
+00 34 868 887905 (Fax)
mcamacho@um.es

In this page...

1. Working papers

2. Former (and current) students

3. Published papers

4. Teaching (Spanish)

5. Curriculum vitae

Working Papers

   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 ragged edges and mixed frequencies. We examine the theoretical benefits of this extension and corroborate the results through several Monte Carlo simulations. Finally, we assess its empirical reliability to compute real-time inferences of the US business cycle.

   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.

   This paper extends the Markov-switching vector autoregressive models to accommodate both the typical lack of synchronicity that characterizes the real-time daily flow of macroeconomic information and economic indicators sampled at different frequencies. The results of the empirical application suggest that the model is able to capture the features of the NBER business cycle chronology very accurately.

   We examine the finite-sample performance of small versus large scale dynamic factor models. Our Monte Carlo analysis reveals that small scale factor models outperform large scale models in factor estimation and forecasting for high level of cross-correlation across the idiosyncratic errors of series that belong 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 yield satisfactory or even better forecasting results than a large scale dynamic factor model that uses all the economic indicators.

   We examine the short-term performance of two alternative approaches of forecasting from dynamic factor models. The first approach extracts the seasonal component of the individual indicators before estimating the dynamic factor model, while the alternative uses the non seasonally adjusted data in a model that endogenously accounts for seasonal adjustment. Our Monte Carlo analysis reveals that the performance of the former is always comparable to or even better than that of the latter in all the simulated scenarios. Our results have important implications for the factor models literature because they show the that the common practice of using seasonally adjusted data in this type of models is very accurate in terms of forecasting ability. Using five coincident indicators, we illustrate this result for US data.

 

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Former and current students

 

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Published Papers

   We show that the single-index dynamic factor model developed by Aruoba and Diebold (AD, 2010) to construct an index of the US business cycle conditions is also very useful to forecast 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 to compute 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 the US business cycles very precisely.

   We show that an extension of the Markov-switching dynamic factor models that accounts for the specificities of the day to day monitoring of economic developments such as ragged edges, mixed frequencies and data revisions is a good tool to forecast the Euro area recessions in real time. We provide examples that show the nonlinear nature of the relations between data revisions, point forecasts and forecast uncertainty. According to our empirical results, we think that the real time probabilities of recession are an appropriate statistic to capture what the press call green shoots.

   We analyze the dynamic interactions between commodity prices and output growth of the seven greatest exporters Latin American countries: Argentina, Brazil, Colombia, Chile, Mexico, Peru and Venezuela. Using Markov-switching impulse response functions, we find that the responses of their respective output growths to commodity price shocks are time dependent, size dependent and sign dependent. Overall, the major evidence of asymmetries in output growth responses occurs when commodity price shocks lead to regime shifts. Accordingly, the design of optimal counter-cyclical stabilization policies in this region should take into account that the reactions of the economic activity vary considerably across business cycle regimes

   This paper uses an extension of the Euro-Sting single-index dynamic factor model to construct short-term forecasts of quarterly GDP growth for the euro area, as also including financial variables as leading indicators. From a simulated real-time exercise, the model is used to investigate the forecasting accuracy across the different phases of the business cycle. In addition, the model is used to evaluate the relative forecasting ability of the two most watched business cycle surveys for the eurozone, the PMI and the ESI. We show that the latter produces more accurate GDP forecasts than the former. Finally, the proposed model is also characterized by its great ability to capture the European business cycle, as well as the probabilities of expansion and/or contraction periods.

   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 with the direction of change metric, which has more economic relevance. 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, these being statistically significant improvements.

   In this paper we extend the Stock and Watson’s (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 behaviour of the Spanish business cycle well and helps to estimate with high precision the regime-switching probabilities in line with business cycle phases. 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.

   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.

   In this paper, I find that real U.S. GDP is better characterized as a trend stationary Markov-switching process than as having a (regime-dependent) unit root. I examine the effects of both assumptions on the analysis of business cycle features and their implications for the persistence of the dynamic response of output to a random disturbance.

   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

   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.

   We analyze the redistributive impact of the 1990s expansion in US, UK, France, Germany, Italy, and Spain by evaluating the income distribution changes over the trough-peak years that determine the expansionary period in these countries. Our empirical strategy separates between market-driven changes affecting income distribution and the role of government interventions through taxes and transfer payments. Overall, the Euro-area tax and transfer system plays a crucial role in offsetting the market-driven poverty and inequality evolutions. However, government interventions reduce the equalitarian effect of the market in UK, and aggravate the market-driven inequality in US.

   This paper provides a comprehensive framework to analyze business cycle features other than synchronization. We use stationary bootstrap and model-based clustering methods to analyze similarities and differences among the European cycles. We find evidence that the length, deep and shape of cycles differ across European countries and that these differences are not decreasing over time. Finally, even though we find some correlation between business cycle synchronization and characteristics, there is important information in the characteristics that is not captured by the synchronization measures.

   We propose a new panel data methodology to test real convergence in a non-linear framework. It extends the existing methods by combining three approaches: the threshold model, the panel data unit root tests and the computation of critical values by bootstrap simulation. We apply our methodology on the per-capita outputs of a total of fifteen European countries, including some of the East-European countries that have recently joined the EU.

  • 2008. Determinants of Japanese Yen Interest Rate Swap Spreads: Evidence from a Smooth Transition Vector Autoregressive Model (with Carl R. Chen and Ying Huang). In Journal of Futures Markets, Vol. 28, No. 1, 2008, pp. 82-107. Download the paper.

   This paper investigates the determinants of variations in the yield spreads between Japanese yen interest rate swaps and Japan government bonds for a period from 1997 to 2005. A smooth transition vector autoregressive (STVAR) model and generalized impulse response functions are used to analyze the impact of various economic shocks on swap spreads. The volatility based on a GARCH model of the government bond rate is identified as the transition variable that controls the smooth transition from high volatility regime to low volatility regime. The break point of the regime shift occurs around the end of the Japanese banking crisis. The impact of economic shocks on swap spreads varies across the maturity of swap spreads as well as regimes. Overall, swap spreads are more responsive to the economic shocks in the high volatility regime. Moreover, volatility shock has profound effects on shorter maturity spreads, while the term structure shock plays an important role in impacting longer maturity spreads. Our results also show noticeable differences between the non-linear and linear impulse response functions.

   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.

   We propose a comprehensive methodology to characterize the business cycle comovements across European economies and some industrialized countries, without imposing any given model but trying to "leave the data speak". We develop a novel method to show that there is no evidence of a "European economy" that acts as an attractor to the other economies of the area. We show that the establishment of the Monetary Union has not significantly increased the level of comovements across Euro-area economies. Finally, we are able to explain an important proportion of the distances across their business cycles using macro-variables related to the structure of the economy, to the directions of trade, and to the size of the public sector.

   Based on a novel extension of existing multivariate Markov-switching models, we provide the reader with a useful tool to analyze current business conditions and to make predictions about the future state of the Euro-area economy in real time. Apart from the Industrial Production Index, we find that the European Commission Industrial Confidence Indicator, that is issued with no delay, is very useful to construct the real-time predictions.

   In this paper, we propose a new framework to analyze pairwise business cycle synchronization across a given set of countries. We show that our approach, that is based on multivariate Markov-switching procedures, lead to more confident results than other popular approaches developed in the literature. According to recent findings, we show that the G7 countries seem to exhibit two differentiated "Euro-zone" and "English-speaking" business cycles dynamics.

   I investigate cointegrating relationships such that, even though the long-run attractors are assumed to be linear, the dynamics of the equilibrium errors depends on the business cycle. I postulate a Markov-switching stochastic trends model to study both the short-run responses to permanent shocks and the effects of severe recessions in the long-run growth. I apply these findings to explore the short-run and long-run asymmetric relationships among output, consumption and investment.

   In this paper, I extend to a multiple-equation context the linearity, model selection, and model adequacy tests recently proposed for univariate Smooth Transition Regression models. Using this result, I examine the nonlinear forecasting power of the Conference Board Composite index of Leading Indicators to predict both output growth and the business-cycle phases of the U.S. economy in real time.

   We use the Stock-Watson diffusion index methodology to summarize the information contained ina wide set of monthly series (published in the Statistical Bulletin of the Bank of Spain) by menas of a reduced number of facors. We find that the first two factors may be used as indicators of the core inflation and the business cycle dynamics of the Spanish economy, respectively. In addition, we study the effects of incorporating large information sets for the analysis of monetary policy. Finally, we show that forecasting prices and output with our factors outperforms other standard alternative forecasting procedures.

   We propose an optimal filter to transform the Conference Board Composite Leading Index (CLI) into recession probabilities in the US economy. We also analyze the CLI's accuracy at anticipating US output growth. We compare the predictive performance of linear, VAR extensions of smooth transition regression and switching regimes, probit, nonparametric models and conclude that a combination of the switching regimes and nonparametric forecasts is the best strategy at predicting both the NBER business cycle schedule and GDP growth. This confirms the usefulness of CLI, even in a real-time analysis.

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Teaching (Spanish)

 I prepared some computer lectures in econometrics that include E-views and GAUSS files. You can click on the link for visiting my teaching web pages.

1. Econometrics

2. Forecasting models

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Curriculum Vitae

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