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Recently, star variables and the post-crisis nature of cyclical fluctuations have attracted a great deal of interest. In this chapter, the authors investigate different methods of assessing business cycles (BCs) for the European Union in general and the euro area in particular. First, the authors conduct a Monte Carlo (MC) experiment using a broad spectrum of univariate trend-cycle decomposition methods. The simulation aims to examine the ability of the analysed methods to find the observed simulated cycle with structural properties similar to actual macroeconomic data. For the simulation, the authors used the structural model’s parameters calibrated to the euro area’s real gross domestic product (GDP) and unemployment rate. The simulation outcomes indicate the sufficient composition of the suite of models (SoM) consisting of popular Hodrick–Prescott, Christiano–Fitzgerald and structural trend-cycle-seasonal filters, then used for the real application. The authors find that: (i) there is a high level of model uncertainty in comparing the estimates; (ii) growth rate (acceleration) cycles have often the worst performances, but they could be useful as early-warning predictors of turning points in growth and BCs; and (iii) the best-performing MC approaches provide a reasonable combination as the SoM. When swings last less time and/or are smaller, it is easier to pick a good alternative method to the suite to capture the BC for real GDP. Second, the authors estimate the BCs for real GDP and unemployment data varying from 1995Q1 to 2020Q4 (GDP) or 2020Q3 (unemployment), ending up with 28 cycles per country. This analysis also confirms that the BCs of euro area members are quite synchronized with the aggregate euro area. Some major differences can be found, however, especially in the case of periphery and new member states, with the latter improving in terms of coherency after the global financial crisis. The German cycles are among the cyclical movements least synchronized with the aggregate euro area.

This study examines credit score migration rates of farm businesses, testing whether migration probabilities differ across business cycles. Results suggest that agricultural credit ratings are more likely to improve during expansions and deteriorate during recessions. The analysis also tests whether agricultural credit ratings depend on the previous period migration trends. The findings show that credit score ratings exhibit trend reversal where upgrades (downgrades) are more likely to be followed by downgrades (upgrades).

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.

This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data.

This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals.

Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level.

Data are measured as the average price in squared meters, and the resulting index is not quality controlled.

The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices.

This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

This paper examines the time‐series behaviour of house prices for the four Asian markets, namely, Hong Kong, Singapore, Tokyo and Taipei, by using structural time‐series methodology. The paper assumes two types of trend models to characterise and compare the long‐run movement of house prices. It also examines the cyclical pattern hidden in the series. The long‐run trend rate in these markets ranged between approximately 1.6 and 3.2 per cent per annum. Hong Kong, Singapore and Taipei have relatively higher figures, which could be expected in light of the rapidly growing economies. Surprisingly, their cyclical patterns were fairly similar, although causes of the cycles differed. The markets were found to have stochastic cycles of around one year, two to four years and seven to ten years, which were consistent with previous findings on real business cycles commonly observed internationally in other macroeconomic time series. However, the found stochastic nature suggests all these markets are not in a steady state and is still changing.

This study uses parametric hazard models to investigate duration dependence in US stock market cycles over the January 1929 through December 1992 period. Market cycles are determined using the Beveridge‐Nelson (1981) approach to the decomposition of economic time series. The results show that both real and nominal cycles exhibit positive duration dependence. The implication of this finding is that actual prices revert to their permanent or trend level in a non‐random manner as the cyclical component dissipates over time. This process is consistent with mean reversion in price and suggests that predictable periodicity in market cycles may exist. Only limited evidence is obtained that discrete shifts or trends in mean cycle duration exist. The length of market cycles appears not to have changed over the 1929–92 period.

This paper aims to investigate Malaysia’s house prices behaviour by decomposing trend, cycle and stochastic component.

The authors perform an unobserved component model of a structural time series and Markov switching model that covers the period 1999Q1 to 2015Q4.

The results reveal that the variation in house price in Malaysia is best explained by its trend level, with a small role played by the cycle component; this implies the potential for gaining returns on investments in property by investors and households. The results also show that Malaysia’s HPI cycle is between 8 and 17 years which, in relative terms, is twice the length of the growth cycle and the business cycle in the economy. Meanwhile, the overall movement of HPI is forecast to have a marginal price increase up to 2028Q2.

As house prices remained elevated during the year, the house price dynamic is pivotal for understanding the source of changes in house price. With major findings centred on the relationship between house prices and macroeconomic as well as policy variables, little attention has been paid to composing the trend, cycle and seasonal pattern from the house price index, thus understanding the behaviour of house prices’ unobserved components.

Reappraises the stylised facts of the contemporary UK business cycle and the robustness of associated sample moments to detrending under the Hodrick‐Prescott (HP) filter and an unobserved components (UC) model based on the structural time series mode of Harvey and advocated in this context by Harvey and Jaeger. For the majority of series considered, findings broadly confirm the earlier HP‐based results of Blackburn and Ravn, but important differences with previous results are reported for labour productivity, the real wage and the real interest rate. However, under neither detrending method are the anticipated cross‐correlations between output and the pivotal variables in standard real business cycle (RBC) models (labour productivity, real wages, the real interest rate and nominal variables) simultaneously confirmed. Indeed, on balance, these results may be interpreted as more suggestive of an orthodox demand‐led or policy‐induced cycle.

There is no research on understanding the difference in the nature of volatility and what it entails for the underlying relationship between foreign institutional investors (FII) flows and stock market movements. The purpose of this paper is to explore how permanent and transitory shocks dominate the common movement between FII flows and the stock market returns. As emerging markets are a major destination of international portfolio investments, the author uses India as a perfect case study to this end.

The paper uses the permanent-transitory as well as a trend-cycle decomposition approach to gain further insights into the common movement between foreign institutional investors (FII) flows and the stock market.

When the author identifies innovations based on their degree of persistence, transitory shocks dominate stock returns, whereas permanent shocks explain movements in foreign institutional investors (FII) flows. Also, stock returns have a larger cyclical component compared to cycles in foreign flows. The authors find the sharp downward (upward) movement in the stock market (FII flows) cycle in the initial period of the COIVD-19 pandemic was quickly reversed and currently, the stock market (FII flows) is historically above (below) the long-term trend, hinting at a correction in months ahead. The authors find strikingly similar stock market cycles during the global financial crisis and COVID-19 period.

Evidence suggests the presence of long stock market cycles – substantial and persistent deviations of actual price from its fundamental (trend) value determined by the shared relationship with foreign flows. This refutes the efficient market hypothesis and makes a case favoring diversification gains from investing in India. Further, transitory shocks dominate the forecast error of stock market movements. Thus, the Indian market provides profit opportunities to foreign investors who use a momentum-based strategy. The author also finds support for the positive feedback trading strategy used by foreign investors.

There is a need for policymakers to account for the foreign undercurrents while formulating economic policies, given the findings that it is the permanent shocks that mostly explain movements in foreign institutional flows. Further, the author finds only stock markets error-correct in response to any short-term shocks to the shared long-term relationship, highlighting the disruptive (though transitory) role of FII flows.

Unlike existing studies, the author models the relationship between stock market returns and foreign institutional investors (FII) flows by distinguishing between the permanent and transitory movements in these two variables. Ignoring this distinction, as done in existing literature, can affect the soundness of the estimated parameter that captures the nexus between these two variables. In addition, while it may be common to find that stock market returns and FII flows move together, the paper further contributes by decomposing each variable into a trend and a cycle using this shared relationship. The paper also contributes to understanding the impact of COVID-19 on this relationship.