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This paper aims to examine the Treasury bond yields response to monetary policy shocks in Tunisia under a heterogeneous economic environment.

Using a traditional fixed coefficient model, the impact of monetary policy changes on the term structure of interest rates for the whole period from January 2006 to December 2016 is estimated first. Then the stability of this relationship by distinguishing two sub-periods around the revolution of January 2011 is studies. To investigate how the relationship between the monetary policy and the Treasury yield curve evolves over time, a time-varying parameter model is estimated.

The results show that the impact of monetary policy is more pronounced at the short end of the yield curve relative to the longer end. Furthermore, this impact declines significantly across all maturities following the revolution and exhibits wide time variation. This evidence supports the negative influence of high levels of uncertainty on monetary policy effectiveness and highlights the desirability of more active monetary policy, especially in turbulent environment.

The impact of uncertainty on the effectiveness of monetary policy shocks needs to be explored further in future research to understand the structural sources of uncertainty and their dynamic interactions with monetary policy and risk aversion in asset markets.

A more active role of the central bank to influence the yield curve mainly through Treasury bond purchases covering medium and long maturities may be warranted. Communication also needs to be reinforced to ensure predictability of the monetary policy stance.

This paper extends the empirical literature on the pass-through of monetary policy to interest rates for an emerging country in context of transition by estimating a state-space model to test the time-varying behavior and examine the influence of increased economic uncertainty on monetary policy effectiveness.

The studies on international housing markets have not modeled frequency domain and focused only on the time domain. The purpose of the present research is to fill this gap by using the state-of-the-art econometric technique of wavelets to understand how differences in the horizon of analysis across time impact international housing markets’ relationship with some of the key macroeconomic variables. The purpose is to also analyze the direction of causation in the relationships.

The author uses the novel time–frequency analysis of international housing markets’ linkages to the macroeconomic drivers. Unlike conventional approaches that do not distinguish between time and frequency domain, the author uses wavelets to study house prices’ relationship with its drivers in the time–frequency space. The novelty of the approach also allows gaining insights into the debates that deal with the direction of causation between house price changes and macroeconomic variables.

Results show that the relationship between house prices and key macroeconomic indicators varies significantly across countries, time, frequencies and the direction of causation. House prices are most related to interest rates at the higher frequencies (short-run) and per capita income growth at the lower frequencies (long-run). The role of industrial production and income growth has switched over time at lower frequencies, particularly, in Finland, France, Sweden and Japan. The stock market’s nexus with the housing market is significant mainly at high to medium frequencies around the recent financial crisis.

The present research implies that in contrast to the existing approaches that are limited to the only time domain, the frequency considerations are equally, if not more, important.

Results show that interested researchers and analysts of international housing markets need to account for the both horizon and time under consideration. Because the factors that drive high-frequency movements in housing market are very different from low-frequency movements. Furthermore, these roles vary over time.

The insights from the present study suggest policymakers interested in bringing social change in the housing markets need to account for the time–frequency dynamics found in this study.

The paper is novel on at least two dimensions. First, to the best of the author’s knowledge, this study is the first to propose the use of a time–frequency approach in modeling international housing market dynamics. Second, unlike present studies, it is the first to uncover the direction of causation between house prices and economic variables for each frequency at every point of time.

Financial market holds superior information that can give insights into the future trajectory of economic growth. Further, identifying sectors that hold the key to future economic growth is important for all economies, but particularly relevant to emerging markets. However, unlike existing studies, the paper provides new insights into the forward-oriented nexus between financial markets and economic growth.

This paper takes a forward-looking approach of using financial market information to predict future economic growth. The authors use ARDL modeling approach to predict economic growth using the information from stock market sectoral returns.

The authors find that sectoral stock returns significantly improve economic growth forecasts. However, the forecasting superiority is not uniform across sectors and horizons. Auto, consumers' spending, materials and realty sectors provide the most forecasting gains. In contrast, banking, capital goods and industrial sectors provide superior forecasts, but only at horizons beyond one year. The authors also find that the forecast superiority of sectors at longer horizons is inversely related to volatility.

Research highlights the need for sector-focused policy actions in driving economic growth. Further, the findings of the paper identify sectors that drive short-, medium- and long-term economic growth.

There is a significant heterogeneity among different sectors and across horizons in predicting economic growth. Results suggest that targeted policy actions in sectors like materials, metals, oil and gas, and realty are key in driving economic growth. Further, policies geared toward the grassroots industries are at least as beneficial as the large-scale industries. Evidence also suggests the need for an active fiscal policy to address infrastructural bottlenecks in primary industries like basic materials and energy. Evidence nevertheless does not undermine the role of monetary policy actions.

Unlike any paper till date, the innovation of the paper is that it takes an ARDL modeling approach to measure stock market sectoral returns' ability to forecast economic growth several months ahead in the future. Though the paper considers the Indian case, the innovation and contribution extents to the entire field of economic studies.

The purpose of this paper is to assess the possibility of asymmetric impact of monetary policy on housing permits issued in each state of the USA.

The methodology and approach are based on the linear ARDL and nonlinear ARDL approach to error-correction modeling and asymmetric cointegration.

The linear models predict that money supply impact housing permits in 28 states in the short run and only nine states in the long run. However, the asymmetric effects are far more pervasive, highlighting the restrictive nature of the linear model. The results from the nonlinear model show at least one lag of positive and/or negative changes in money supply significantly impacts housing permits in nearly all states. Even in the long run, housing permits in 32 states share a long-run relationship with positive and/or negative changes in money supply. The authors also find contractionary monetary policy has a greater influence on housing permits in most states compared to expansionary policy.

For the first time, the authors use state-level data and asymmetric approach to assess the impact of monetary policy on house permits issued in each state of the USA.

The objective of the paper is to explore the out-of-sample forecasting connections in income growth across the globe.

An autoregressive distributed lag (ARDL) framework is employed and the forecasting performance is analyzed across several horizons using different forecast combination techniques.

Results show that the foreign country's income provides superior forecasts beyond what is provided by the country's own past income movements. Superior forecasting power is particularly held by Belgium, Korea, New Zealand, the UK and the US, while these countries' income is rather difficult to predict by global counterparts. Contrary to conventional wisdom, improved forecasts of income can be obtained even for longer horizons using our approach. Results also show that the forecast combination techniques yield higher forecasting gains relative to individual model forecasts, both in magnitude and the number of countries.

The forecasting paths of income movement across the globe reveal that predictive power greatly differs across countries, regions and forecast horizons. The countries that are difficult to predict in the short run are often seen to be predictable by global income movements in the long run.

Even while it is difficult to predict the income movements at an individual country level, combining information from the income growth of several countries is likely to provide superior forecasting gains. And these gains are higher for long-horizon forecasts as compared to the short-horizon forecast.

In evaluating the forward-looking social implications of economic policy changes, the policymakers should also consider the possible global forecasting connections revealed in the study.

Employing an ARDL model to explore global income forecasting connections across several forecast horizons using different forecast combination techniques.

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.