Coincident Indicators Research Articles

Credit extension in South Africa: A business cycle perspective for the period 1985 to 2009

This paper investigates and quantifies the relationship between the macroeconomic business cycle and bank-granted credit in South Africa for the period 1985 to 2009. The main question that this research seeks to answer is what role do banks play in amplifying the business cycle and what is the impact of this on the macroeconomy? The outcomes of the econometric model support the hypothesis that a positive relationship exists between bank-extended credit and the business cycle. The vector autoregression technique was used to prove the relationship between credit and the underlying cycle. The analysis shows that a two-way relationship exists between credit and the coincident indicator, credit and insolvencies and credit and prime. Results from the vector error correction model show a significant short-run relationship of equilibrium in the cointegrating equation between credit and the coincident indicator. This corroborates the underlying theory that credit is a unifying variable that rapidly responds to shocks emanating from the dynamic interaction of cointegrating variables in the economy.

Dating NBER Recessions with Philadelphia Fed State Coincident Economic Indices (Redux) Aug ’79 to Sept ‘11

We extrapolate Novak’s diffusion-based recession dating model to include the 2008 great recession to gauge its “out of sample” effectiveness. We then explore an alternative method to diffusion in using state coincident indices that produces a superior recession dating model, and an optimised US national coincident economic indicator (CEI). We examine 12 US states that are the most sensitive to recession starts and ends - our “canaries in the coal mine.”

Systemic Risk Diagnostics: Coincident Indicators and Early Warning Signals

A macro-prudential policy maker can manage risks to financial stability only if current and future risks can be reliably assessed. We propose a novel framework to assess financial system risk. Using a dynamic factor framework based on state-space methods, we model latent macro-financial and credit risk components for a large data set comprising the U.S., the EU-27 area, and the rest of the world. Controlling for global, region-specific, and industry effects, we construct coincident measures ('thermometers') and forward looking indicators of financial distress and the likelihood of financial melt-down. We find that credit risk conditions can significantly and persistently de-couple from macro-financial fundamentals. Such decoupling can serve as an early warning signal for macro-prudential policy.

Comparing China’s GDP statistics with coincident indicators

We use factor analysis to summarize information from various macroeconomic indicators, effectively producing coincident indicators for the Chinese economy. We compare the dynamics of the estimated factors with GDP, and compare our factors with other published indicators for the Chinese economy. The estimated factors and the published coincident indicators match the GDP dynamics well and discrepancies are very short. The largest discrepancies may correspond to shocks affecting the growth process.

Comparing China's GDP Statistics with Coincident Indicators

We use factor analysis to summarize information from various macroeconomic indicators, effectively producing coincident indicators for the Chinese economy. We compare the dynamics of the estimated factors with GDP, and compare our factors with other published indicators for the Chinese economy. The indicator data match the GDP dynamics well and discrepancies are very short. The periods of discrepancies seem to correspond to shocks affecting the growth process as neither autoregressive models for GDP itself nor various coincident indicators are able to forecast them satisfactorily.

Using adaptive network-based fuzzy inference system to forecast automobile sales

Improving the sales forecasting accuracy has become a primary concern for automobile industry. Here, we only focus on new automobile sales in Taiwan. The data set is based on monthly sales, and the data can be divided into three styles of automobile sales. To address our concern, we developed a sales forecasting methodology that considers several variables such as current automobile sales quantity, coincident indicator, leading indicator, wholesale price index and income. First, we use the stepwise regression to select most influential variables as our input variables. Then, we input the influential variables and sales in adaptive network-based fuzzy inference system (ANFIS) to obtain the forecast. Finally, we compare our model with two forecasting models: autoregressive integrated moving average model (ARIMA) and artificial neural network (ANN). Empirical results demonstrate that the application of the ANFIS model outperforms the other two models. In addition, we modified the historical and holdout periods to improve forecasting accuracy while considering the impact from the financial tsunami in 2008.

Should Transportation Output Be Included as Part of the Coincident Indicators System?

With the increasing importance of the service-providing sectors, information from these sectors has become essential to the understanding of contemporary business cycles. This paper explores the usefulness of the transportation services output index (TSI) as an additional coincident indicator in determining the peaks and troughs of U.S. economy. The index represents a service sector that plays a central role in facilitating economic activities between sectors and across regions, and can be useful in monitoring the current state of the economy. We evaluate the marginal contribution of the TSI in identifying cyclical turning points in the context of four currently used NBER indicators. The TSI is found to have advantages over the composite index of coincident indicators in identifying turning points, and has been of critical importance in recent recessions.

Systemic Risk Diagnostics: Coincident Indicators and Early Warning Signals

Systemic Risk Diagnostics: Coincident Indicators and Early Warning Signals

The Industrial Cycle of Milan as an Accurate Leading Indicator for the Italian Business Cycle

A coincident business cycle indicator for the Milan area is built on the basis of a monthly industrial survey carried out by Assolombarda, the largest territorial entrepreneurial association in Italy. The indicator is extracted from three time series concerning the production level and the domestic and foreign order book as declared by some 250 Assolombarda associates.

Econometric analysis of bank lending and business cycles in South Africa

This article examines econometric relationships between bank lending and business cycle in South Africa. Two long-run economic relationships are hypothesized between total credit and the variables, namely, coincidental indicators, spread between lending and borrowing rates, money supply, stock price index, inflation and banking sector specific factors included in the model. Of these variables, only coincident indicators, changes in money supply as well as capital, and reserve are found to exert some influence on short-run total credit availability.

Assessing Aggregate Comovements in France, Germany and Italy. Using a Non Stationary Factor Model of the Euro Area

The objective of the paper is to investigate to what extent business cycles co-move in Germany, France and Italy. We use a large-scale database of non-stationary series for the euro area in order to assess the effect of common versus idiosyncratic shocks, as well as transitory versus permanent shocks, across countries over the 1980:Q1 to 2003:Q4 period. We apply the method-ology proposed by Bai (2004) and Bai and Ng (2004) to construct a coincident indicator of the euro area business cycle to which national developments appear to be increasingly correlated at business cycle frequencies (8 to 32 quarters), while more significant differences appear at lower frequencies which measures potential growth. The indicator is also shown to be related to extra euro area economic developments.

Two Probabilistic Cyclical Turning Point Indicators for the French Economy (Deux Indicateurs Probabilistes de Retournement Cyclique Pour L’Économie Française) (French)

This paper proposes two new coincident probabilistic cyclical indicators developed by the Bank of France in order to follow, on a monthly basis, the French economic activity. The first one is an indicator which aims at detecting the turning points of the acceleration cycle while the second one is dedicated to the follow-up of recession phases in the industrial sector. Both indicators are based on the methodology of Markov-Switching models and use only for input the Bank of France monthly business survey. An historical validation since 1998 points out to the interest and the complementarity of both indicators for the short-term economic diagnosis. This kind of indicators provides with an original and additional conjonctural qualitative information by comparison with more classical quantitative tools aiming at estimating the GDP growth rate.

An Application of the Stock/Watson Index Methodology to the Santa Fé Economy

The Stock/Watson index methodology is applied to the Santa Fe economy to estimate a coincident index for the State. A coincident index is estimated using the Kalman filter and smoother on a set of coincident indicators. This study is the first step in a broader purpose of building a coincident index for each of the provinces that constitutes the region called “Region Centro”.

LATCOIN: determining medium to long-run tendencies of economic growth in Latvia in real time

This paper presents a method of estimating the current state of Latvia’s economy. The evaluation object is medium to long-run growth of real GDP, but not actual GDP itself, which helps to filter out various one-off effects and focus on medium and long-run tendencies. Our indicator, called LATCOIN (Latvia’s Business Cycle Coincidence Indicator), could be viewed as a simple adaptation of new EUROCOIN for Latvia with some changes in methodology. LATCOIN is a monthly estimate of the medium to long-run growth of Latvia’s real GDP, which is produced on the 9th working day of the next month. Using a large panel of macroeconomic variables, a few smooth unobservable factors describing the economy are constructed. Further, these factors are used for the estimation of LATCOIN.

Systemic Risk Diagnostics: Coincident Indicators and Early Warning Signals

We propose a novel framework to assess financial system risk. Using a dynamic factor framework based on state-space methods, we construct coincident measures ( JEL Classification: G21, C33

Business cycles in the euro area defined with coincident economic indicators and predicted with leading economic indicators

AbstractClusters of cyclical turning points in the coincident indicators help us identify and date euro area recessions and recoveries in the past several decades. In the USA and some other countries, composite indexes of coincident indicators (CEI) are used to date classical business cycle turning points; also indexes of leading indicators (LEI) are used to help in the difficult task of predicting these turning points. This paper reviews a selection of the available data for monthly and quarterly euro area coincident and leading indicators. From these data, we develop composite indexes using methods analogous to those tested in the US CEI and LEI published by The Conference Board. We compare the resulting business cycle chronology with the existing alternatives and evaluate our selection of leading indicators in the context of how well they predict current economic activity and its major fluctuations for the euro area. Copyright © 2009 John Wiley & Sons, Ltd.

Survey data as coincident or leading indicators

AbstractIn this paper we propose a monthly measure for the euro area gross domestic product (GDP) based on a small‐scale factor model for mixed‐frequency data, featuring two factors: the first is driven by hard data, whereas the second captures the contribution of survey variables as coincident indicators. Within this framework we evaluate both the in‐sample contribution of the second survey‐based factor, and the short‐term forecasting performance of the model in a pseudo‐real‐time experiment. We find that the survey‐based factor plays a significant role for two components of GDP: industrial value added and exports. Moreover, the two‐factor model outperforms in terms of out‐of‐sample forecasting accuracy the traditional autoregressive distributed lags (ADL) specifications and the single‐factor model, with few exceptions. Copyright © 2009 John Wiley & Sons, Ltd.

Identifying business cycle turning points with sequential Monte Carlo methods: an online and real‐time application to the Euro area

AbstractWe propose a new approach for detecting turning points and forecasting the level of economic activity in the business cycle. We make use of coincident indicators and of nonlinear and non‐Gaussian latent variable models. We thus combine the ability of nonlinear models to capture the asymmetric features of the business cycle with information on the current state of the economy provided by coincident indicators. Our approach relies upon sequential Monte Carlo filtering techniques applied to time‐nonhomogenous Markov‐switching models. The transition probabilities are driven by a beta‐distributed stochastic component and by a set of exogenous variables. We illustrate, in a full Bayesian and online context, the effectiveness of the methodology. We also measure its ability to identify turning points and to forecast the European business cycle on both realtime and last‐revised data. Copyright © 2009 John Wiley & Sons, Ltd.

A Coincident Index, Common Factors, and Monthly Real GDP*

The Stock–Watson coincident index and its subsequent extensions assume a static linear one-factor model for the component indicators. This restrictive assumption is unnecessary if one defines a coincident index as an estimate of monthly real gross domestic products (GDP). This paper estimates Gaussian vector autoregression (VAR) and factor models for latent monthly real GDP and other coincident indicators using the observable mixed-frequency series. For maximum likelihood estimation of a VAR model, the expectation-maximization (EM) algorithm helps in finding a good starting value for a quasi-Newton method. The smoothed estimate of latent monthly real GDP is a natural extension of the Stock–Watson coincident index.

WHERE ARE WE NOW? THE UK RECESSION AND NOWCASTING GDP GROWTH USING STATISTICAL MODELS

There is, therefore, a demand for forecasts of where the economy is now this quarter or even this month. One option is to construct a composite coincident indicator, which summarises the information in many series all believed to tell us something about the economy's prospects. Such an indicator is often interpreted as representing the underlying state of the economy; see Carriero and Marcellino (2007) for a review in a UK context. Another option, which has the attraction of being directly observable and verifiable, is to nowcast GDP growth itself, since GDP represents an overall measure of the status of the economy. Here we focus on nowcasts of quarterly GDP growth.