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.     

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.     

In search of leading indicators of economic activity in Germany

In this paper we present two new composite leading indicators of economic activity in Germany estimated using a dynamic factor model with and without regime switching. The obtained optimal inferences of business cycle turning points indicate that the two‐state regime switching procedure leads to a successful representation of the sample data and provides an appropriate tool for forecasting business conditions. Copyright © 2003 John Wiley & Sons, Ltd.     

Evaluation of Regime Switching Models for Real‐Time Business Cycle Analysis of the Euro Area

In this paper, we aim at assessing Markov switching and threshold models in their ability to identify turning points of economic cycles. By using vintage data updated on a monthly basis, we compare their ability to date ex post the occurrence of turning points, evaluate the stability over time of the signal emitted by the models and assess their ability to detect in real‐time recession signals. We show that the competitive use of these models provides a more robust analysis and detection of turning points. To perform the complete analysis, we have built a historical vintage database for the euro area going back to 1970 for two monthly macroeconomic variables of major importance for short‐term economic outlook, namely the industrial production index and the unemployment rate. Copyright © 2013 John Wiley & Sons, Ltd.     

International interdependence of business cycles in the manufacturing industry: The use of leading indicators for forecasting and analysis

This article introduces new leading indicators for fifteen industrialized countries which enable the business cycle in manufacturing to be forecast fairly reliably between 4 and 6 months ahead. These indicators are based on an improved variant of the NBER method, yielding a composite leading indicator characterized by less erratic movements and clear turning points. The indicators are used to explore the international interdependence of business cycles and to examine the degree to which this interdependence is affected by growing economic integration, as in the EC. For each of the countries studied, the various foreign economies affecting the local business climate are identified. Since the business cycles of some countries clearly lead those of others, this international interdependence can be used to further improve the predictive power of the leading indicators in the lagging countries.     

Introducing the Euro Area‐wide Leading Indicator (ALI): Real‐Time Signals of Turning Points in the Growth Cycle from 2007 to 2011

This paper introduces a new monthly euro Area‐wide Leading Indicator (ALI) for the euro area growth cycle which is composed of nine leading series and derived from a one‐sided bandpass filter. The main findings are that (i) the GDP growth cycle in the euro area can be well tracked, in a timely manner and at monthly frequency, by a reference growth cycle indicator (GCI) derived from industrial production excluding construction, (ii) the ALI reliably leads turning points in the GCI by 5 months and (iii) longer leading components of the ALI are good predictors of the GCI up to 9 months ahead. A real‐time case study on the ALI's capabilities for signalling turning points in the euro area growth cycle from 2007 to 2011 confirms these findings. Copyright © 2013 John Wiley & Sons, Ltd.     

Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle

Four methods of model selection—equally weighted forecasts, Bayesian model‐averaged forecasts, and two models produced by the machine‐learning algorithm boosting—are applied to the problem of predicting business cycle turning points with a set of common macroeconomic variables. The methods address a fundamental problem faced by forecasters: the most useful model is simple but makes use of all relevant indicators. The results indicate that successful models of recession condition on different economic indicators at different forecast horizons. Predictors that describe real economic activity provide the clearest signal of recession at very short horizons. In contrast, signals from housing and financial markets produce the best forecasts at longer forecast horizons. A real‐time forecast experiment explores the predictability of the 2001 and 2007 recessions. Copyright © 2015 John Wiley & Sons, Ltd.     

Improving the Timeliness of Turning Signals for Business Cycles with Monthly Data

The conventional growth rate measures (such as month‐on‐month, year‐on‐year growth rates and 6‐month smoothed annualized rate adopted by the US Bureau of Labor Statistics and Economic Cycle Research Institute) are popular and can be easily obtained by computing the growth rate for monthly data based on a fixed comparison benchmark, although they do not make good use of the information underlying the economic series. By focusing on the monthly data, this paper proposes the k‐month kernel‐weighted annualized rate (k‐MKAR), which includes most existing growth rate measures as special cases. The proposed k‐MKAR measure involves the selection of smoothing parameters that are associated with the accuracy and timeliness for detecting the change in business turning points. That is, the comparison base is flexible and is likely to vary for different series under consideration. A data‐driven procedure depending upon the stepwise multiple reality check test for choosing the smoothing parameters is also suggested in this paper. The simple numerical evaluation and Monte Carlo experiment are conducted to confirm that our measures (in particular the two‐parameter k‐MKAR) improve the timeliness subject to a certain degree of accuracy. The business cycle signals issued by the Council for Economic Planning and Development over the period from 1998 to 2009 in Taiwan are taken as an example to illustrate the empirical application of our method. The empirical results show that the k‐MKAR‐based score lights are more capable of reflecting turning points earlier than the conventional year‐on‐year measure without sacrificing accuracy. Copyright © 2016 John Wiley & Sons, Ltd.     

A classifying procedure for signalling turning points

A Hidden Markov Model (HMM) is used to classify an out‐of‐sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead of estimating past turning points using maximum likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method, here called a ‘Markov Bayesian Classifier (MBC)’, is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourably with earlier HMM studies. Some theoretical arguments for this are given. Copyright © 2004 John Wiley & Sons, Ltd.     

Forecasting for business planning: A case study of IBM product sales

This is a case study of a closely managed product. Its purpose is to determine whether time-series methods can be appropriate for business planning. By appropriate, we mean two things: whether these methods can model and estimate the special events or features that are often present in sales data; and whether they can forecast accurately enough one, two and four quarters ahead to be useful for business planning. We use two time-series methods, Box-Jenkins modeling and Holt-Winters adaptive forecasting, to obtain forecasts of shipments of a closely managed product. We show how Box-Jenkins transfer-function models can account for the special events in the data. We develop criteria for choosing a final model which differ from the usual methods and are specifically directed towards maximizing the accuracy of next-quarter, next-half-year and next-full-year forecasts. We find that the best Box-Jenkins models give forecasts which are clearly better than those obtained from Holt-Winters forecast functions, and are also better than the judgmental forecasts of IBM's own planners. In conclusion, we judge that Box-Jenkins models can be appropriate for business planning, in particular for determining at the end of the year baseline business-as-usual annual and monthly forecasts for the next year, and in mid-year for resetting the remaining monthly forecasts.     

Bayesian Analysis of a Threshold Stochastic Volatility Model

This paper proposes a parsimonious threshold stochastic volatility (SV) model for financial asset returns. Instead of imposing a threshold value on the dynamics of the latent volatility process of the SV model, we assume that the innovation of the mean equation follows a threshold distribution in which the mean innovation switches between two regimes. In our model, the threshold is treated as an unknown parameter. We show that the proposed threshold SV model can not only capture the time‐varying volatility of returns, but can also accommodate the asymmetric shape of conditional distribution of the returns. Parameter estimation is carried out by using Markov chain Monte Carlo methods. For model selection and volatility forecast, an auxiliary particle filter technique is employed to approximate the filter and prediction distributions of the returns. Several experiments are conducted to assess the robustness of the proposed model and estimation methods. In the empirical study, we apply our threshold SV model to three return time series. The empirical analysis results show that the threshold parameter has a non‐zero value and the mean innovations belong to two separately distinct regimes. We also find that the model with an unknown threshold parameter value consistently outperforms the model with a known threshold parameter value. Copyright © 2016 John Wiley & Sons, Ltd.     

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.     

Signal extraction and estimation of a trend: a Monte Carlo study

Several authors (King and Rebelo, 1993; Cogley and Nason, 1995) have questioned the use of exponentially weighted moving average filters such as the Hodrick–Prescott filter in decomposing a series into a trend and cycle, claiming that they lead to the observation of spurious or induced cycles and to misinterpretation of stylized facts. However, little has been done to propose different methods of estimation or other ways of defining trend extraction. This paper has two main contributions. First, we suggest that the decomposition between the trend and cycle has not been done in an appropriate way. Second, we argue for a general to specific approach based on a more general filter, the stochastic trend model, that allows us to estimate all the parameters of the model rather than fixing them arbitrarily, as is done with mainly of the commonly used filters. We illustrate the properties of the proposed technique relative to the conventional ones by employing a Monte Carlo study. Copyright © 1999 John Wiley & Sons, Ltd.     

Performance evaluation of the New Connecticut Leading Employment Index using lead profiles and BVAR models

The leading and coincident employment indexes for the state of Connecticut developed following the recession of the early 1990s fell short of expectations. This paper performs two tasks. First, it describes the process of revising the Connecticut Coincident and Leading Employment Indexes. Second, it analyzes the statistical properties and performance of the new indexes by comparing the lead profiles of the new and old indexes as well as their out‐of‐sample forecasting performance, using the Bayesian Vector Autoregressive (BVAR) method. The new coincident index shows improved performance in dating employment cycle chronologies. The lead profile test demonstrates that superiority in a rigorous, non‐parametric statistic fashion. The mixed evidence on the BVAR forecasting experiments illustrates that leading indexes properly predict cycle turning points and do not necessarily provide accurate forecasts except at turning points, a view that our results support. Copyright © 2006 John Wiley & Sons, Ltd.     

A nonparametric method for asymmetrically extending signal extraction filters

Two important problems in the X‐11 seasonal adjustment methodology are the construction of standard errors and the handling of the boundaries. We adapt the ‘implied model approach’ of Kaiser and Maravall to achieve both objectives in a nonparametric fashion. The frequency response function of an X‐11 linear filter is used, together with the periodogram of the differenced data, to define spectral density estimates for signal and noise. These spectra are then used to define a matrix smoother, which in turn generates an estimate of the signal that is linear in the data. Estimates of the signal are provided at all time points in the sample, and the associated time‐varying signal extraction mean squared errors are a by‐product of the matrix smoother theory. After explaining our method, it is applied to popular nonparametric filters such as the Hodrick–Prescott (HP), the Henderson trend, and ideal low‐pass and band‐pass filters, as well as X‐11 seasonal adjustment, trend, and irregular filters. Finally, we illustrate the method on several time series and provide comparisons with X‐12‐ARIMA seasonal adjustments. Copyright © 2010 John Wiley & Sons, Ltd.     

Do professional forecasters believe in the Phillips curve? evidence from the G7 countries

This paper uses monthly survey data for the G7 countries for the time period 1989–2007 to explore the link between expectations on nominal wages, prices and unemployment rates as suggested by the wage and price Phillips curves. Four major findings stand out. First, we find that survey participants trust in both types of Phillips curve relationships. Second, we find evidence in favor of nonlinearities in the price Phillips curve. Third, we take into account a kink in the price Phillips curve to indicate that the slope of the Phillips curve differs during the business cycle. We find strong evidence of this feature in the data which confirms recent theoretical discussions. Fourth, we employ our data to the expectations‐augmented Phillips curve model. The results suggest that professional forecasters adopt this model when forecasting macroeconomic variables. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

基层政府服务质量评估体系建立与实证分析——基于美国波多里奇国家质量奖模型

建设服务型政府己成为我国政府管理的核心价值理念，对基层政府服务质量的评估是确保服务型政府建立的关键。作为政府的服务也是服务业的一个组成部分，因此，根据西方国家工商管理理论评估服务行业服务质量的方法——波多里奇质量奖评奖标准引入到政府服务评估中来，建立政府服务质量评估模型并设计基层政府质量评估调查问卷，得出相应调查数据以便支持假设模型：运用克朗巴哈系数确定调查数据的信度后，再运用路径法进行分析并得出结论——所建模型假设成立。最后，运用因子分析方法得出最终政府服务质量评估权重，完成了整个质量评估体系的建立.     

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.     

Forecasting recessions using the yield curve

We compare forecasts of recessions using four different specifications of the probit model: a time invariant conditionally independent version; a business cycle specific conditionally independent model; a time invariant probit with autocorrelated errors; and a business cycle specific probit with autocorrelated errors. The more sophisticated versions of the model take into account some of the potential underlying causes of the documented predictive instability of the yield curve. We find strong evidence in favour of the more sophisticated specification, which allows for multiple breakpoints across business cycles and autocorrelation. We also develop a new approach to the construction of real time forecasting of recession probabilities. Copyright © 2005 John Wiley & Sons, Ltd.