Two Alternative Approaches to Modelling the Nonlinear Dynamics of the Composite Economic Indicator

This paper sets up a common unobserved factor model with smooth transition autoregressive dynamics. This model is compared to the already classical common factor model with regime-switching. Both models' in-sample and out-of-sample performance in terms of capturing and predicting the business cycle turning points is evaluated. The comparison of the model-derived probabilities to the NBER business cycle dating shows statistically equivalent in-sample forecasting accuracy of these techniques. The common factor model with exponential STAR outperforms the model with logistic STAR and that with Markov switching in terms of out-of-sample prediction with up to 3 month horizon.

Unobserved Leading and Coincident Common Factors in the Post-War U.S. Business Cycle

The paper introduces a two-factor model of the common leading and coincident economic indicators. Both factors are unobserved and each of them captures the dynamics of a corresponding group of the observed time series. The common leading factor is assumed to Granger-cause the common coincident factor. This property is used to estimate these two factors simultaneously and hence more efficiently. Two models of the latent leading and coincident factors are studied : a model with linear dynamics and a model with Markov-switching dynamics introduced through the leading factor intercept term. Moreover, a possibility of the individual leading variables having different leads over the common coincident indicator is considered. These models - both with linear and with regime-switching dynamics - were applied to the US monthly macroeconomic time series. The business cycle dating resulting from the nonlinear model closely corresponds to the NBER chronology and leads its turning points by 3-5 months.dynamic factor analysis;Markov switching;leading indicator;coincident indicator;Granger causality

Some Evidence of Decreasing Volatility of the US Coincident Economic Indicator

The paper treats the issue of the decreasing volatility of the U.S. economy which has been observed since the mid-1980s. As a measure of volatility the residual variance of a composite economic indicator is used. This indicator is constructed as a common dynamic factor with Markov switching and hence it incorporates both the comovements of different macroeconomic variables and the asymmetry between the contractions and expansions. Two additional regimes are included capturing the secular shift in the volatility. Furthermore, the mixed frequency is allowed for, permitting the use both of monthly and quarterly component series. The low mean regime probabilities comply to the NBER business cycle dating, while the low variance regime probabilities indicate the beginning of 1984 as a possible date of the structural break in volatility.

Markov-Switching Common Dynamic Factor Model with Mixed-Frequency Data

In this paper, we consider a coincident economic indicator model with regime-switching dynamics and with the time series observed at different frequencies, for instance, at monthly and quarterly frequencies. Until now the only solution was to drop the lower frequency series and to estimate the model based only on the higher frequency series. This approach leads to the significant information losses. We propose an approach allowing to overcome this problem and to estimate a nonlinear dynamic common factor with the missing observations taking advantage of all the information available.Common dynamic factor; Markov switching; Mixed frequency data; Kalman filter; Composite economic indicator

Identifying and Forecasting the Turns of the Japanese Business Cycle

In this paper we identify and try to predict the turning points of the Japanese business cycle. As a measure of the business cycle we use a composite economic indicator (CEI). This indicator is endowed with nonlinear dynamics to capture the asymmetries between different cyclical phases. Two types of nonlinear dynamics are considered : Markov switching and smooth transition autoregression (STAR). The performance of these models in terms of forecasting the business cycle turns is compared. Both types of models produce statistically equivalent in-sample forecasting results, whilst the CEI with exponential STAR tends to outperform the CEI with Markov-switching and logistic STAR in the out-of-sample prediction.composite economic indicator;Markov switching;smooth transition autoregression;turning points;reference cycle;forecasting

Forecasting the Turns of German Business Cycle: Dynamic Bi-factor Model with Markov Switching

In this paper a dynamic bi-factor model with Markov switching is proposed to measure and predict turning points of the German business cycle. It estimates simultaneously the composite leading indicator (CLI) and composite coincident indicator (CCI) together with corresponding probabilities of being in recession. According to the bi-factor model, on average, CLI leads CCI by 3 months at both peaks and troughs. The model-derived recession probabilities of CCI and those of CLI with a lag of 2-3 months capture the turning points of the ECRI's and OECD's reference cycle much better than the dynamic single-factor model with Markov switching.Forecasting turning points; Composite coincident indicator; Composite leading indicator; Dynamic bi-factor model; Markov-switching

Using the Dynamic Bi-Factor Model with Markov Switching to Predict the Cyclical Turns in the Large European Economies

The appropriately selected leading indicators can substantially improve the forecasting of the peaks and troughs of the business cycle. Using the novel methodology of the dynamic bi-factor model with Markov switching and the data for three largest European economies (France, Germany, and UK) we construct composite leading indicator (CLI) and composite coincident indicator (CCI) as well as corresponding recession probabilities. We estimate also a rival model of the Markov-switching VAR in order to see, which of the two models brings better outcomes. The recession dates derived from these models are compared to three reference chronologies: those of OECD and ECRI (growth cycles) and those obtained with quarterly Bry-Boschan procedure (classical cycles). Dynamic bi-factor model and MSVAR appear to predict the cyclical turning points equally well without systematic superiority of one model over anotherForecasting turning points, composite

Stylized Facts Test for the Signal-Extraction Techniques

One of the important tools of the business cycle research are the signal-extraction techniques (SETs). They allow to study both the stylized facts and the turning points of the business cycles. However, these are highly sensitive to the SETs. In this paper we try to see how some of the SETs affect the stylized facts and to compare the performance of several detrending techniques in terms of the distortions they introduce into the first four moments of the extracted business cycles. To accomplish this, the Monte Carlo experiments for various DGPs, including deterministic and stochastic, common and individual trend specifications of the observed time series, were undertaken. The results allow to rank different SETs according to their performance and to reveal the sources of distortions. Finally, we try to improve upon performance of the SETs by constructing mixed, mutlivariate and mixed multivariate filters using univariate detrending techniques as building blocks. It turns out that linear combination of the filters behaves better than the best of SETs of which it is comprised. Multivariate filtering also leads to improvements of the SETs performance.business cycle;signal-extraction technique;stylized facts;Hodrick-Prescott filter;Bandpass filter;Caterpillar filter

Dealing with Structural Changes in the Common Dynamic Factor Model : Deterministic Mechanism

Composite economic indicator is a very useful tool designed to trace and predict the business cycle conditions. In this paper we study possible extensions of this approach intended to cope with the potential data problems caused by various structural breaks affecting both level and volatility of the component series. The structural shifts are introduced in the composite economic indicator model via deterministic dummies capturing breaks in the observed variables’intercepts and in the residual variances of the specific factors. As an illustration the Post-World War II US monthly macroeconomic series are utilized for which different specifications of the single-factor linear and regime-switching model are evaluated.composite economic indicator;Markov switching;structural break;turning points;NBER dating

How helpful are spatial effects in forecasting the growth of Chinese provinces?

In this paper, we make multi-step forecasts of the annual growth rates of the real Gross Regional Product (GRP) for each of the 31 Chinese provinces simultaneously. Beside the usual panel data models, we use panel models that explicitly account for spatial dependence between the GRP growth rates. In addition, the possibility of spatial effects being different for different groups of provinces (Interior and Coast) is allowed for. We find that both pooling and accounting for spatial effects helps substantially improve the forecast performance compared to the benchmark models estimated for each of the provinces separately. It is also shown that the effect of accounting for spatial dependence is even more pronounced at longer forecasting horizons (the forecast accuracy gain as measured by the root mean squared forecast error is about 8% at the 1-year horizon and exceeds 25% at the 13- and 14-year horizon).Chinese provinces; forecasting; dynamic panel model; spatial autocorrelation; group-specific spatial dependence