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The purpose of the paper is to address money demand instability and investigate the impact of economic uncertainty, stock market uncertainty and monetary uncertainty on money demand in India over the period 2003Q1–2019Q4.

The study checks the stationarity of the variables through standard unit root tests. Based on the mixed order of variables' integration, the authors adopt the autoregressive distributed lag (ARDL) model to confirm the cointegration and check the stability of the money demand function (MDF).

The findings confirm the presence of cointegration and reveal a well-specified MDF, which exhibits stable parameters. Besides the conventional variables, all forms of uncertainties emerge as the essential long-term determinants of money demand. Long-run findings show that people demand more money to avoid the future financial crunch amid high economic, monetary and stock market uncertainties.

The paper recommends, based on the findings, incorporating the monetary aggregates in the monetary policy framework as one of the essential information variables to control the fluctuation in the price level under the current flexible inflation targeting (FIT) regime.

The findings also add to the knowledge of economic agents in terms of the overall response of individuals to changes in different forms of uncertainties, thereby helping to formulate their portfolios more diligently.

The current work is the first of its kind in the Indian context. The incorporation of uncertainty measures in the MDF adds to the existing knowledge on money demand.

Having defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.

Conceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.

The predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.

Better understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

因此,就了解經濟週期和經濟發展而言,流動資金是一個極其重要的概念。本研究擬在安全資產的價格預測方面取得進步。安全資產代表了經濟的實際情況,特別是美國的十年期國債。

流動資金的定義上面已說明了; 為進一步了解經濟波動,本研究擬對流動資金代表性變量的預測能力進行評估。

研究使用作為流動資金代表的概念變項去規劃預測。各機器學習模型的結果會作比較,這會帶來對流動資金變量的預測值的深思,而深思的目的是確定其選擇。

只要在私營部門內流動資金的數據比公營部門的流動資金數據、在預測經濟波動方面貢獻更大時,我們發現、模型的預測能力也會依賴流動資金的來源而存在差異。國際流動資金被視為一個晦澀的概念,而它的定義的標準化,或許應是未來學術研究的焦點。當應用最先進的機器學習模型時,標桿分析法的步驟也施行了。

若我們對有關的變量加深認識,我們就可更深入地理解金融市場的運作。流動資金,雖是金融市場中一個極其重要的變量,但在現存的學術文獻裏,不但沒有明確的定義,而且也沒有被標準化; 就此而言,未來的研究或許可在這方面作進一步的探討。因此,本研究為富有新穎思維的應用研究,研究使用了現代數據科學技術,這可為探討金融市場提供一個全新的視角。

This paper empirically investigates the effect of economic policy uncertainty (EPU) on the UK money demand stability during the inter-war period (1920–1938). Both a narrow definition (M0) and a broad definition (M3) of money are investigated.

The empirical investigation is conducted by employing the autoregressive distributed lag (ARDL) bounds testing approach to cointegration.

Results presented indicate a stable demand for both definitions of money only when EPU is included as one of the determinants of demand function. The EPU imposes a negative effect on the demand for both definitions of money. The causality test results further indicate long- and short-term causality from the determinants (including EPU) to both forms of money demand.

Significant presence of the economic uncertainty weakens the effects of the monetary policy on the economy.

This is a historical economics paper. Given the turmoil and uncertainty associated with the inter-war period, an empirical investigation of UK money demand is an interesting exercise. This is the first such paper.

This paper aims to study, by means of an empirical approach, how monetary policy might affect the distribution of individual income.

After describing the channels through which monetary policy could impinge on income distribution, the authors carry out a panel analysis of 62 countries that control their monetary policy for the period 1996–2015.

Using two possible proxy variables for monetary policy (the monetary aggregate M3 and the real interest rates), the results reveal a significant positive relationship between real interest rates and income inequality measured through the market Gini coefficient and polarization ratios. The findings suggest that central bankers should be more aware of the redistributive effects of monetary policy.

It should be mentioned the major challenge of data limitation in the empirical investigation on the relationship between monetary policies and inequalities.

The empirical evidence presented in this paper supports the premise that central bankers should not ignore the unintended redistributive consequences of their actions. In this regard, it is worth noting that if, in addition to price stability, central banks are also responsible for financial stability; the rationale behind central bank independence needs to be reconsidered.

An outstanding feature of the paper is its sample size and the variety of countries included in the sample, which includes countries from all continents and with very different levels of economic development. Also, unlike papers based on forecasting modeling – e.g. Vector autoregression (VAR) or Structural vector autoregression (SVAR) models, the study follows an explanatory approach, including not only monetary variables, but also a series of regressors that may have a meaningful and significant impact on inequality, according to a wide literature.

This paper aims to revisit the theme of fiscal-monetary coordination in a general equilibrium setup that allows for unconventional monetary policy, monetary policy transmission and developing country characteristics.

This paper uses a calibrated new Keynesian dynamic stochastic general equilibrium (DSGE) model to study fiscal-monetary interaction.

Debt sits at the center of monetary-fiscal interaction. Under high-debt conditions, the inflation-output trade-off rises with an increase in the strictness with which monetary policy targets inflation, undermining the standard prescription of strict inflation targeting. At the same time, the transmission of monetary policy is also impeded, due to which unconventional monetary policy becomes more appropriate. The need for coordination among the policies gets enhanced in the presence of borrowing cost channel. While the presence of borrowing cost channel increases the need for policy coordination regardless of the debt situation, features like higher share of non-Ricardian households and weaker monetary policy transmission affect monetary-fiscal interaction to a greater extent under high-debt environment.

First, this paper uses inflation-output trade-off as a metric, to analyze fiscal-monetary interaction. Second, this paper considers the impact of developing country characteristics (such as a higher share of non-Ricardian households, impeded monetary policy transmission and supply constraints/borrowing cost channel) on fiscal-monetary interaction. Third, the DSGE model developed in this paper incorporates open market operations that could shed light on the role of unconventional monetary policy in the presence of high fiscal deficit and debt, which is particularly relevant in the current context of the COVID-19 pandemic. Fourth, the model also permits an investigation into monetary policy transmission under different debt regimes.

The idea of this study is to provide a solid Financial Condition Index (FCI) that allows the monetary transmission policy to be monitored in a country which in recent decades has suffered from major financial and monetary crises.

The authors construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime. Using monthly data from 1995 to 2017, the authors estimate FCIs with two different methodologies and build the index by taking into account the mechanism of transmission of monetary policy and incorporating the most relevant financial variables.

This study’s results show that, likewise for developing countries as Mexico, an FCI could be a useful tool for managing monetary policy in reducing macroeconomic fluctuations.

Apart from building a predictor of possible financial stress, the authors construct an FCI for a central bank that pursues inflation targeting and to analyse the role of financial asset prices in formulating monetary policy.

1. We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

2. The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

3. FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

4. FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.

We construct three FCIs for Mexico to analyse the role of financial asset prices in formulating monetary policy under an inflation-targeting regime.

The FCIs are based on (1) a vector autoregression model (VAR); (2) an autoregressive distributed lag model (ARDL) and (3) a factor-augmented vector autoregression model (FAVAR).

FCI could become a new target for monetary policy within a hybrid inflation-targeting framework.

FCI could be a good tool for managing monetary policy in developing countries with a low-inflation environment.

This paper aims to examine the response of monetary policy to financial instability in the West African Economic and Monetary Union.

Through annual aggregated data from 1970 to 2019, the empirical strategy is based on the Markov regime-switching model with fixed probabilities.

The results revealed that the monetary policy of the central bank of the West African Economic and Monetary Union is characterized by two regimes (calm and distress) with respect to the trend of financial stability. The authors also found that the occurrence of the calm regime was likely greater than that of the distress regime. In addition, the calm regime is longer than the distress regime. The authors finally revealed that the central bank reacts to financial instability risk by increasing its short-term interest rate when financial instability reaches a threshold.

The limitation of this study is the unavailability of monthly or quarterly data that are more suitable for the methodological approach adopted.

This study is the one to estimate the response of the Central Bank of West African Countries to financial stress using a novel approach based on the Markov-Switching regression.

The purpose of this paper is to gain insights useful to explain the loan puzzle: the unexpected increase of loans to firms in case of a monetary tightening. To this end, the authors develop the analysis using several loan categories distinguished by lender, scope and borrower. This approach helps to unveil significant differences on how those categories respond to the same shock and allow to evaluate possible alternative explanations for such differences.

The paper is empirical. The analysis is based on a large vector auto-regression, estimated using Bayesian techniques and has as object the US economy.

The findings support a supply-side explanation of the loan puzzle, i.e. banks reshuffle their portfolio in favor of short-term business loans after a monetary tightening. Moreover, the authors achieve the following results. First, the analysis shows that loans to small firms increase as well, but less than what observed with large firms: small firms stay between large firms and households. Second, considering advances and other loans allows to conclude that finance companies behave very much as banks. Third, some limited evidence suggests that not just industrial and commercial loans to firms might increase but also more long-term loans, such as mortgages.

The authors develop an analysis, based on state-of-the-art Bayesian techniques, that reveals the differential response of well-distinguished loan categories to several shocks; monetary and real shocks in the first place. After showing their heterogenous response, the authors discuss it in detail, with specific reference to supply and demand factors of credit intrinsic to the transmission mechanism. With respect to previous contributions, the authors consider a plurality of loan categories functional to understand the reason behind each specific response. This allows to conclude in favor of supply factors as an explanation of the unexpected increase of loans to corporate firms in case of a monetary shock.

The implementation of monetary policy by the central bank is an ongoing topic of discussion. This paper aims to explore monetary policy transmission shocks in times of uncertainty using the new World uncertainty index (WUI). The authors investigate the impact of crises, wars and pandemic shocks on selected macroeconomic variables.

The authors use unit root tests, structural vector autoregressive model and the Granger causality test according to Toda–Yamamoto with quarterly data over 1999–2022.

The results of this study show that in the short run, there is a unidirectional relationship between the money market rate and WUI, while the relationship between the latter and the money supply (M2) is bidirectional. The short-term effect runs from WUI to inflation. In the long run, the variance decomposition shows that global uncertainty explains around 12% of inflation pressures. The uncertainty caused by special events in the world creates positive shocks on inflation in Tunisia, which decreases the ability of the central bank to control inflation.

The results have implications over necessary and urgent actions to be implemented for a progressive economic recovery but point to a necessary transition to an inflation-targeting regime.

Examining monetary policy under uncertainty is a recent phenomenon. The authors purposely use a novel WUI by Ahir et al. (2022) that is unexploited in literature.

This study seeks to build a financial uncertainty index for Saudi Arabia. This index serves as a leading indicator of Saudi economic activity and helps to describe economic fluctuations and forecast economic trends.

This study adopts an extension of the Jurado et al. (2015) procedure by combining financial uncertainty factors with their net spillover effects on GDP and inflation to construct an aggregate financial uncertainty index. The authors consider 13 monthly financial variables for Saudi Arabia from January 2010 to June 2021.

The empirical results show that the constructed financial uncertainty estimates are good leading indicators of economic activity. The robustness analysis suggests that the authors’ proposed financial uncertainty estimators outperform the alternative estimates used by other existing approaches to estimate the financial conditions index.

To the best of the authors’ knowledge, this is the first attempt at constructing a financial uncertainty index for Saudi Arabia. This study extends the empirical literature, from which the authors propose a novel conceptual framework for building a financial uncertainty index by combining the approach of Jurado et al. (2015) and the time-varying connectedness network approach proposed by Antonakakis et al. (2020)