Detecting and Predicting Economic Accelerations,Recessions, and Normal Growth Periods in Real‐Time

The dichotomous characterization of the business cycle in recessions and expansions has been central in the literature over the last 50 years. However, there are various reasons to question the adequacy of this dichotomous, recession/expansion approach for our understanding of the business cycle dynamics, as well as for the prediction of future business cycle developments. In this context, the contribution of this paper to the literature is twofold. First, since a positive rate of growth at the level of economic activity can be considered as the normal scenario in modern economies due to both population and technological growth, it proposes a new non‐parametric algorithm for the detection and dating of economic acceleration periods, trend or normal growth periods, and economic recessions. Second, it uses an ordered probit framework for the estimation and forecasting of these three business cycle phases, applying an automatized model selection approach using monthly macroeconomic and financial data on the German economy. The empirical results show that this approach has superior out‐of‐sample properties under real‐time conditions compared to alternative probit models specified individually for the prediction of recessions and/or economic accelerations. Copyright © 2016 John Wiley & Sons, Ltd.     

Do credit booms predict US recessions?

This paper investigates the role of bank credit in predicting US recessions since the 1960s in the context of a bivariate probit model. A set of results emerge. First, credit booms are shown to have strong positive effects in predicting declines in the business cycle at horizons ranging from 6 to 9 months. Second, I propose to isolate the effect of credit booms by identifying the contribution of excess bank liquidity alongside a housing factor in the downturn of each cycle. Third, the out-of-sample performance of the model is tested on the most recent credit-driven recession: the Great Recession of 2008. The model performs better than a more parsimonious version where we restrict the effect of credit booms on the business cycle in the system to be zero.     

A leading indicator approach to predicting short‐term shifts in demand for business travel by air to and from the UK

This paper uses the probit model to examine whether leading indicator information could be used for the purpose of predicting short‐term shifts in demand for business travel by air to and from the UK. Leading indicators considered include measures of business expectations, availability of funds for corporate travel and some well‐known macroeconomic indicators. The model performance is evaluated on in‐ and out‐of‐sample basis, as well as against a linear leading indicator model, which is used to mimic the current forecasting practice in the air transport industry. The estimated probit model is shown to provide timely predictions of the early 1980s and 1990s industry recessions and is shown to be more accurate than the benchmark linear model. Copyright © 2005 John Wiley & Sons, Ltd.     

Identifying US business cycle regimes using dynamic factors and neural network models

We use dynamic factors and neural network models to identify current and past states (instead of future) of the US business cycle. In the first step, we reduce noise in data by using a moving average filter. Dynamic factors are then extracted from a large-scale data set consisted of more than 100 variables. In the last step, these dynamic factors are fed into the neural network model for predicting business cycle regimes. We show that our proposed method follows US business cycle regimes quite accurately in-sample and out-of-sample without taking account of the historical data availability. Our results also indicate that noise reduction is an important step for business cycle prediction. Furthermore, using pseudo real time and vintage data, we show that our neural network model identifies turning points quite accurately and very quickly in real time.     

The Euro‐Sting Revisited: The Usefulness of Financial Indicators to Obtain Euro Area GDP Forecasts

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 by accounting for 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. Our extension is also used to evaluate the relative forecasting ability of the two most reliable business cycle surveys for the euro area: 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. Copyright © 2014 John Wiley & Sons, Ltd.     

Predicting cyclical turning points with leading index in a markov switching model

We have evaluated the Commerce Department's Composite Index of Leading Indicators as a predictor of business cycle turning points using the two-state Markov switching model as the filter. Contrary to some recent studies, we found that the predictive performance of CLI is quite good and, with an exception of the 1973:11 peak, it made very little difference to the prediction of turning points whether real-time data are used instead of the revised series. We found, however, that imposing any degree of autoregression in the errors on the simple regime-shift model caused the filter to signal turning points inappropriately. Also, we found no evidence of duration dependence in post-war U.S. business cycles.     

The Role of Financial Variables in predicting economic activity

Previous research found that the US business cycle leads the European one by a few quarters, and can therefore be useful in predicting euro area gross domestic product (GDP). In this paper we investigate whether additional predictive power can be gained by adding selected financial variables belonging to either the USA or the euro area. We use vector autoregressions (VARs) that include the US and euro area GDPs as well as growth in the Rest of the World and selected combinations of financial variables. Out‐of‐sample root mean square forecast errors (RMSEs) evidence that adding financial variables produces a slightly smaller error in forecasting US economic activity. This weak macro‐financial linkage is even weaker in the euro area, where financial indicators do not improve short‐ and medium‐term GDP forecasts even when their timely availability relative to GDP is exploited. It can be conjectured that neither US nor European financial variables help predict euro area GDP as the US GDP has already embodied this information. However, we show that the finding that financial variables have no predictive power for future activity in the euro area relates to the unconditional nature of the RMSE metric. When forecasting ability is assessed as if in real time (i.e. conditionally on the information available at the time when forecasts are made), we find that models using financial variables would have been preferred in several episodes and in particular between 1999 and 2002. Copyright 2011 John Wiley & Sons, Ltd.     

Factor Models of Stock Returns: GARCH Errors versus Time‐Varying Betas

This paper investigates the implications of time‐varying betas in factor models for stock returns. It is shown that a single‐factor model (SFMT) with autoregressive betas and homoscedastic errors (SFMT‐AR) is capable of reproducing the most important stylized facts of stock returns. An empirical study on the major US stock market sectors shows that SFMT‐AR outperforms, in terms of in‐sample and out‐of‐sample performance, SFMT with constant betas and conditionally heteroscedastic (GARCH) errors, as well as two multivariate GARCH‐type models. Copyright © 2016 John Wiley & Sons, Ltd.     

Analysis of the US business cycle with a Vector‐Markov‐switching model

This paper identifies turning points for the US ‘business cycle’ using information from different time series. The model, a multivariate Markov‐switching model, assumes that each series is characterized by a mixture of two normal distributions (a high and low mean) with the switching from one to the other determined by a common Markov process. The procedure is applied to the series composing the composite coincident indicator in the USA to obtain business cycle turning points. The business cycle chronology is closer to the NBER reference cycle than the turning points obtained from the individual series using a univariate model. The model is also used to forecast the series with some encouraging results. Copyright © 2001 John Wiley & Sons, Ltd.     

Forecasting US Recessions with a Large Set of Predictors

In this paper, I use a large set of macroeconomic and financial predictors to forecast US recession periods. I adopt Bayesian methodology with shrinkage in the parameters of the probit model for the binary time series tracking the state of the economy. The in‐sample and out‐of‐sample results show that utilizing a large cross‐section of indicators yields superior US recession forecasts in comparison to a number of parsimonious benchmark models. Moreover, the data‐rich probit model gives similar accuracy to the factor‐based model for the 1‐month‐ahead forecasts, while it provides superior performance for 1‐year‐ahead predictions. Finally, in a pseudo‐real‐time application for the Great Recession, I find that the large probit model with shrinkage is able to pick up the recession signals in a timely fashion and does well in comparison to the more parsimonious specification and to nonparametric alternatives. Copyright © 2016 John Wiley & Sons, Ltd.     

On the directional predictability of equity premium using machine learning techniques

This paper applies a plethora of machine learning techniques to forecast the direction of the US equity premium. Our techniques include benchmark binary probit models, classification and regression trees, along with penalized binary probit models. Our empirical analysis reveals that the sophisticated machine learning techniques significantly outperformed the benchmark binary probit forecasting models, both statistically and economically. Overall, the discriminant analysis classifiers are ranked first among all the models tested. Specifically, the high-dimensional discriminant analysis classifier ranks first in terms of statistical performance, while the quadratic discriminant analysis classifier ranks first in economic performance. The penalized likelihood binary probit models (least absolute shrinkage and selection operator, ridge, elastic net) also outperformed the benchmark binary probit models, providing significant alternatives to portfolio managers.     

Enhancing survey‐based investment forecasts

We investigate the accuracy of capital investment predictors from a national business survey of South African manufacturing. Based on data available to correspondents at the time of survey completion, we propose variables that might inform the confidence that can be attached to their predictions. Having calibrated the survey predictors' directional accuracy, we model the probability of a correct directional prediction using logistic regression with the proposed variables. For point forecasting, we compare the accuracy of rescaled survey forecasts with time series benchmarks and some survey/time series hybrid models. In addition, using the same set of variables, we model the magnitude of survey prediction errors. Directional forecast tests showed that three out of four survey predictors have value but are biased and inefficient. For shorter horizons we found that survey forecasts, enhanced by time series data, significantly improved point forecasting accuracy. For longer horizons the survey predictors were at least as accurate as alternatives. The usefulness of the more accurate of the predictors examined is enhanced by auxiliary information, namely the probability of directional accuracy and the estimated error magnitude.     

Unemployment variation over the business cycles: a comparison of forecasting models

Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non‐linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non‐linearity in the unemployment series. Only recently have there been some developments in applying non‐linear models to estimate and forecast unemployment rates. A major concern of non‐linear modelling is the model specification problem; it is very hard to test all possible non‐linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non‐linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back‐propagation model and a generalized regression neural network model to estimate and forecast post‐war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out‐of‐sample forecast results obtained by the ANN models with those obtained by several linear and non‐linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd.     

On the Predictive Content of Autoregression Residuals: A Semiparametric,Copula‐Based Approach to Time Series Prediction

This paper proposes an adjustment of linear autoregressive conditional mean forecasts that exploits the predictive content of uncorrelated model residuals. The adjustment is motivated by non‐Gaussian characteristics of model residuals, and implemented in a semiparametric fashion by means of conditional moments of simulated bivariate distributions. A pseudo ex ante forecasting comparison is conducted for a set of 494 macroeconomic time series recently collected by Dees et al. (Journal of Applied Econometrics 2007; 22: 1–38). In total, 10,374 time series realizations are contrasted against competing short‐, medium‐ and longer‐term purely autoregressive and adjusted predictors. With regard to all forecast horizons, the adjusted predictions consistently outperform conditionally Gaussian forecasts according to cross‐sectional mean group evaluation of absolute forecast errors and directional accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model

This paper introduces a regime switching vector autoregressive model with time‐varying regime probabilities, where the regime switching dynamics is described by an observable binary response variable predicted simultaneously with the variables subject to regime changes. Dependence on the observed binary variable distinguishes the model from various previously proposed multivariate regime switching models, facilitating a handy simulation‐based multistep forecasting method. An empirical application shows a strong bidirectional predictive linkage between US interest rates and NBER business cycle recession and expansion periods. Due to the predictability of the business cycle regimes, the proposed model yields superior out‐of‐sample forecasts of the US short‐term interest rate and the term spread compared with the linear and nonlinear vector autoregressive (VAR) models, including the Markov switching VAR model.     

Assessing the historical accuracy of regional economic forecasts

This study addresses for the first time systematic evaluation of a widely used class of forecasts, regional economic forecasts. Ex ante regional structural equation model forecasts are analysed for 19 metropolitan areas. One- to ten-quarter-ahead forecasts are considered and the seven-year sample spans a complete business cycle. Counter to previous speculation in the literature, (1) dependency on macroeconomic forecasting model inputs does not substantially erode accuracy relative to univariate extrapolative methodologies and (2) stochastic time series models do not on average, yield more accurate regional economic predictions than structural models. Similar to findings in other studies, clear preferences among extrapolative methodologies do not emerge. Most general conclusions, however, are subject to caveats based on step-length effects and region-specific effects.     

Volatility spillover from the US to international stock markets: A heterogeneous volatility spillover GARCH model

A recent study by Rapach, Strauss, and Zhou (Journal of Finance, 2013, 68(4), 1633–1662) shows that US stock returns can provide predictive content for international stock returns. We extend their work from a volatility perspective. We propose a model, namely a heterogeneous volatility spillover–generalized autoregressive conditional heteroskedasticity model, to investigate volatility spillover. The model specification is parsimonious and can be used to analyze the time variation property of the spillover effect. Our in‐sample evidence shows the existence of strong volatility spillover from the US to five major stock markets and indicates that the spillover was stronger during business cycle recessions in the USA. Out‐of‐sample results show that accounting for spillover information from the USA can significantly improve the forecasting accuracy of international stock price volatility.     

Conditionally disordered proteins: bringing the environment back into the fold

For many proteins, biological function requires the folding of the polypeptide chain into a unique and persistent tertiary structure. This review concerns proteins that adopt a specific tertiary structure to function, but are otherwise partially or completely disordered. The biological cue for protein folding is environmental perturbation or minor post-translational modification. Hence, we term these proteins conditionally disordered. Many of these proteins recognize and bind other molecules, and conditional disorder has been hypothesized to allow for more nuanced control and regulation of binding processes. However, this remains largely unproven. The sequences of conditionally disordered proteins suggest their propensity to fold; yet, under the standard laboratory conditions, they do not do so, which may appear surprising. We argue that the surprise results from the failure to consider the role of the environment in protein structure formation and that conditional disorder arises as a natural consequence of the marginal stability of the folded state.     

Vector smooth transition regression models for US GDP and the composite index of leading indicators

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 US economy in real time. Copyright © 2004 John Wiley & Sons, Ltd.     

Analysis of many short time sequences: Forecast improvements achieved by shrinkage

Credibility models in actuarial science deal with multiple short time series where each series represents claim amounts of different insurance groups. Commonly used credibility models imply shrinkage of group-specific estimates towards their average. In this paper we model the claim size yu in group i and at time t as the sum of three independent components: y_it = μ_r + δ_i + ?_it. The first component, μt = μ_t?1 + m_t, represents time-varying levels that are common to all groups. The second component, δ_i, represents random group offsets that are the same in all periods, and the third component represents independent measurement errors. In this paper we show how to obtain forecasts from this model and we discuss the nature of the forecasts, with particular emphasis on shrinkage. We also assess the forecast improvements that can be expected from such a model. Finally, we discuss an extension of the above model which also allows the group offsets to change over time. We assume that the offsets for different groups follow independent random walks.