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This paper aims to present a chronology of Indian business cycles in the post-reform period. In India, earlier, macroeconomic shocks were about droughts and oil prices. Economic reforms have led to an interplay of a market economy, financial globalisation and decisions of private firms to undertake investment and hold inventory. This has changed the working of the business cycle and has raised concerns about business-cycle stabilisation. In the backdrop of these developments, the macroeconomics research agenda requires foundations of measurement about business-cycle phenomena. One element of this is the identification of dates of business-cycle turning points.

This paper uses the growth-cycle approach to present the chronology of business cycles. The paper uses the Christiano–Fitzgerald (CF) filter to extract the cyclical component and shows the robustness of the findings to the contemporary methods of cycle extraction. It then applies the Bry–Boschan algorithm to identify the dates of peaks and troughs.

The paper finds three periods of recession. The first recession was from 1999-Q4 to 2003-Q1; the second recession was from 2007-Q2 to 2009-Q3; and the third recession ran from 2011-Q2 till 2012-Q4. These results are robust to the choice of filter and to the choice of the business-cycle indicator. These dates suggest that, on average, expansions in India are 12 quarters in length and recessions run for 9 quarters. The paper offers evidence of change in the nature of cycles.

Dates of business-cycle turning points are a critical input for academic and policy work in macroeconomics. The paper offers robust estimation of the business-cycle turning points in the post-reform period using contemporary techniques of cycle extraction. This work helps lay the foundations for downstream macroeconomics research by academicians and policymakers.

The purpose of this paper is to assess the behaviour of economic sentiment indicators at rent‐growth turning points and indicators' ability to forecast such turning points. More specifically, the paper looks at whether early signals are generated for forthcoming periods of negative and positive office rent growth. The analysis aims to complement structural model forecasting in the real estate market with short‐term forecasting techniques designed to predict turning points.

The objective of this study is achieved by deploying a probit model to examine the ability of economic sentiment indicator series to signal the direction of office rents and the strength of movement in this direction. The main advantage of this approach is that it is geared towards predicting turning points. Probit models are non‐linear in nature, and as such they can capture more effectively the likely asymmetric adjustments when turning points occur than linear methodologies would. The analysis is applied to three major office centres – La Défense, London City, and Frankfurt – to examine whether the results will differ by geography.

The findings reveal that the probit methodology utilising information from economic sentiment indicators generates advance signals for periods of contraction and expansion in office rents across all three markets: La Défense, London City, and Frankfurt. The lead times for La Défense and Frankfurt are longer than those for London City and range between three and nine months. The evidence in this paper clearly supports the appeal of sentiment indicators and probit analysis to inform forecasting and risk assessment processes.

Acknowledging the limitations of structural models and related methodologies and the lack of adequate research on turning‐point prediction in the real estate market, this study forecasts episodes of negative and positive office rent growth applying appropriate techniques and data that lead economic activity, are of monthly frequency, and are not revised historically. The paper raises awareness of a forecasting approach that should complement structural models and judgmental forecasting, given its suitability for short‐term forecasting and for signalling turning points in advance.

Several official institutions (NBER, OECD, CEPR, and others) provide business cycle chronologies with lags ranging from three months to several years. In this paper, we propose a Markov-switching dynamic factor model that allows for a more timely estimation of turning points. We apply one-step and two-step estimation approaches to French data and compare their performance. One-step maximum likelihood estimation is confined to relatively small data sets, whereas two-step approach that uses principal components can accommodate much bigger information sets. We find that both methods give qualitatively similar results and agree with the OECD dating of recessions on a sample of monthly data covering the period 1993–2014. The two-step method is more precise in determining the beginnings and ends of recessions as given by the OECD. Both methods indicate additional downturns in the French economy that were too short to enter the OECD chronology.

The purpose of this paper is to expand our understanding of processes governing commercial property cycles, and to provide tools, which enable identification of property cycles’ turning points’ location.

This paper is divided into three parts. The first looks at the demand-supply dynamics and the location of two characteristic cyclic points, the market bottom and the cycle commencement. In the second part a property relevant formula for entropy is derived, and its relation to the cycle overheated stage and the market peak is studied. In the third part, we discuss still another characteristic point of the cycle, which relates to the stage when developers elect to undertake new projects. This analysis is done by employing the chaos theory, and its relation to the cyclic evolution.

It is found that some markets cycle, while others fluctuate only. A clear method for distinguishing among these is provided. The bottom of a cycle may overlap or be time separated from the start of a subsequent cycle. Market peaks are characterised by a sharp decrease in financial component to entropy for top quality building grades. A cycling market is characterised by crossing of a distinct vacancy rate during the cycle progression.

The tools developed in the paper allow for clear characterisation of the market types and their cyclic behaviour. This in turn allows for timely characterisation of the market state and for short time-frame forecasting. The depth of a cycle may be calculated and the subsequent correction level estimated.

The paper utilises cross-field approach by taking methods from both physics and mathematics and applying them to property markets. It breaks new ground both in property research and in applied mathematics by showing how the current frontier in pure mathematics may be applied to property.

The purpose of this paper is to study synchronization in stock index cycles across 82 countries and the linkage between macroeconomic and financial integration and stock market synchronization.

The author document the synchronization structure of the world equity index cycles and its evolution over time. The author examine the explanatory power of various economic and financial variables on cycle comovements.

Trade openness, capital openness, and an EU membership contribute to higher stock index cycle synchronization. Additionally, the macroeconomic and financial variables have asymmetric impacts on countries of different development levels.

The author is the first to thoroughly chronicle the turning points, i.e., bear and bull regimes, of world equity indexes and empirically examine determinants of their cyclical comovement across nations.

Financial analysts have been found to be overconfident. The purpose of this paper is to study the ramifications of that overconfidence on the dispersion of earnings estimates as a predictor of the US business cycle.

Whether aggregate analyst forecast dispersion contains information about turning points in business cycles, especially downturns, is examined by utilizing the analyst earnings forecast dispersion metric. The primary analysis derives from logit regression and Markov switching models. The analysis controls for sentiment (consumer confidence), output (industrial production), and financial indicators (stock returns and turnover). Analyst data come from Institutional Brokers Estimate System, while the economic data are available at the Federal Reserve Bank of St Louis Economic Data site.

A rise in the dispersion of analyst forecasts is a significant predictor of turning points in the US business cycle. Financial analyst uncertainty of earnings estimate contains crucial information about the risks of US business cycle turning points. The results are consistent with some analysts becoming overconfident during the expansion period and misjudging the precision of their information, thus over or under weighting various sources of information. This causes the disagreement among analysts measured as dispersion.

This is the first study to show that analyst forecast dispersion contributions valuable information to predictions of economic downturns. In addition, that dispersion can be attributed to analyst overconfidence.

This study investigates whether cyclical turning points in the U.S. and U.K. stock markets are unevenly distributed over the year, that is, whether they are more likely to occur during certain months of the year. In examining this form of periodic seasonality, a Markov switching‐model is applied to U.S. and U.K. stock market chronologies of monthly peak and trough dates for the periods May 1835 through March 2000 and May 1836 through September 2000, respectively. In order to provide some evidence on robustness with respect to the sample data, results are obtained for the entire sample periods as well as for various sub‐. For both markets, the evidence indicates that while the probability of moving from an expansion to a contraction does not depend on the month of the year, the probability of switching from a contraction is greater for some months. Additionally, the durations of contractions, but not expansions, are dependent on the month of the year in which they begin.

We analyze historical business cycles as a sum of short‐ and medium‐term cycles defined for a particular class of unobserved component models. By associating the trend with the low frequencies of the pseudo‐spectrum in the frequency domain, manipulation of the spectral bandwidth will allow us to define subjective trends with specific properties. In this paper, we show how these properties can be exploited to anticipate business cycle turning points, not only historically but also in a true ex‐ante exercise. This procedure is applied to US pre‐Second World War GNP quarterly data taking as reference the NBER and Romer’s business cycle datings.