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One of the most significant changes in monetary economics at the beginning of the twenty-first century has been the virtual disappearance of what was once a dominant focus, the role of money in monetary policy, and parallelly, the disappearance of the liquidity preference-money supply (LM) curve. Economists used to consider monetary policy with the help of the LM curve as part of the analytical framework which captures the demand for money. However, the workhorse model of modern monetary theory and policy, the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework, only comprises the dynamic investment-savings (IS) curve, the New Keynesian (NK) Phillips curve, and a monetary policy rule. The monetary policy rule is generally known as the Taylor rule. It relates the nominal interest rate to the output-gaps and inflation-gaps, but typically not to either the quantity or the growth rate of money. This change in the modern monetary model reflects how the central banks make monetary policy now. This study provides a detailed discussion on the role of money in monetary policy formulation in the context of the NK and the New Monetarist perspectives. The pros and cons of abandonment of money or the LM curve from monetary policy models have been discussed in detail.

The objective of this chapter is to study the symmetric and asymmetric impact of macroeconomic variables on the Indian stock prices (SPs) of the Bombay Stock Exchange index. This chapter further investigates whether the asymmetric impact of macroeconomic variables on SP is due to the impact of any tail events like the global financial recession. An autoregressive distribution lag and non-autoregressive distribution lag approach is used for the full sample covering the period from January 2000 to June 2019 and later this sample is further subdivided into before and after the crisis period to study the variations in result. The findings show that macroeconomic variables and SP have a symmetric relation in the long run whereas an asymmetric relationship in the short run when the whole sample is analyzed. However when data are segregated into “before and after” crisis period this relationship turns to be asymmetric in long run too, meaning that in the long run, the negative and positive changes in a macroeconomic variable do not affect SPs similarly.

The aim of this chapter is to examine the role of real exchange rates in the relationship between tourist arrival and economic growth in Malaysia over the period of 2000–2018. We disaggregate Malaysian tourists into six geographical regions, namely Asia, Singapore, Europe, Pacific region, Americas, and Africa. Using a non-linear autoregressive distributed lag model, we find that the appreciation of real exchange rates with positive growth of economy plays a prominent role in influencing international tourist arrivals from Singapore, other Asian countries, Pacific region, Europe, and Americas. Our study suggests that real appreciation is important in providing some insights into the effectiveness of growth-led-tourism policies. In line with this, some implications are provided at the end of this chapter.

Monte Carlo simulations are a very powerful way to demonstrate the basic sampling properties of various statistics in econometrics. The commercial software package Stata makes these methods accessible to a wide audience of students and practitioners. The purpose of this chapter is to present a self-contained primer for conducting Monte Carlo exercises as part of an introductory econometrics course. More experienced econometricians that are new to Stata may find this useful as well. Many examples are given that can be used as templates for various exercises. Examples include linear regression, confidence intervals, the size and power of t-tests, lagged dependent variable models, heteroskedastic and autocorrelated regression models, instrumental variables estimators, binary choice, censored regression, and nonlinear regression models. Stata do-files for all examples are available from the authors' website http://learneconometrics.com/pdf/MCstata/.

Significant evidence in the literature points to money demand instability and therefore inaccurate forecasting. In view of this issue, this chapter seeks to use a method, innovative for money demand literature, that is, the machine learning model to predict money demand. Specifically, this chapter uses Random Forest Regression to predict money demand using monthly data in the Indian context over the period April-1996 to December-2018 using the variables usually used in literature. The chapter finds that in money demand prediction, the Random Forest Regression performs fairly well. The results are also compared to traditional models and it is found that the Random Forest Regression model has the potential to enhance the prediction of money demand over what traditional models predicts.

Productivity growth in Italy has been persistently anemic and lagged that of the euro area over the period 1999–2015, while the indebtedness of its corporate sector increased. Using the ORBIS firm-level database, this chapter studies the long-term impact of persistent corporate-debt accumulation on the productivity growth of Italian firms, and investigates whether total factor productivity (TFP) growth varies with the level of corporate indebtedness. The authors employ a novel estimation technique proposed by Chudik, Mohaddes, Pesaran, & Raissi (2017) to account for dynamics, bi-directional feedback effects, cross-firm heterogeneity, and cross-sectional dependence arising from unobserved common factors (e.g., oil price shocks, labor and product market frictions, and the stance of the global financial cycle). Filtering out the effects of unobserved common factors and controlling for firm-specific characteristics, the authors find significant negative effects of persistent corporate-debt build-up on firms’ TFP growth on average, and weak evidence of a threshold level of corporate debt, beyond which productivity growth drops off significantly. The results have strong policy implications, for example the design of the tax system should discourage persistent corporate-debt accumulation, and effective and timely frameworks to reduce corporate-debt overhangs are essential.

Purpose: The advent of the fintech revolution has brought a tremendous increase in the dissemination of digital financial services. Although digital financial services increase financial inclusion through financial intermediation, it also increases the chances of systematic risk.

Need: In the quest to satisfy the curious minds, the authors have examined the influence of digital financial services on banking stability and efficiency.

Methodology: To achieve the above objectives, the authors have used the Auto-Regressive Distribution Lag (ARDL) estimation technique on the annual data set of India and the United States from 2004 to 2018. In addition, to estimate the long-run cointegration, the ARDL bound approach is also used.

Findings: The empirical analysis concludes that in the short run, the expansion of digital financial services in India in the form of internet-based transactions and mobile money transactions creates a negative and significant impact on banking efficiency and stability. Meaning, banking sector efficiency and stability fall by 0.09% and 0.05% with a 1% increase in digital financial services. However, in the long run, digital financial services enhance banking stability and efficiency in India. Besides, the study also reveals that in a developed country like the United States, both in the short run and long run, expansion of digital financial services helps in improving banking efficiency and stability. Furthermore, in context to control variables, the findings suggest that in the short run, industrial productivity has a negative influence on the Indian banking sector efficiency and stability, compared to the positive impact in the long run. This is unlike the United States, where both in the long-run and short-run, industrial productivity has a positive influence on the banking sector’s efficiency and stability.

Practical implication: The findings reveal several policy implications and suggest policy synergies between digital financial services, banking stability and efficiency.