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As the world economic power shifts from the advanced G7 countries--Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States--to the seven largest emerging market countries (EM7)--Brazil, China, India, Indonesia, Mexico, Russia, and Turkey--the vulnerability of these emerging market countries to exogenous shocks is becoming of growing importance. This paper presents a comprehensive examination of the effects of oil price shocks on real economic activity in the EM7 economies in the context of two classes of empirical models. In general, we find that oil price uncertainty has statistically significant effects on the real output of the EM7 economies and that the relationship between oil prices and economic activity is in general symmetric. We also find that oil price uncertainty has in general a negative effect on world crude oil production. Keywords: Emerging markets, Oil price uncertainty, GARCH-in-Mean VAR, 1. INTRODUCTION The seven largest emerging market economies (henceforth EM7)--Brazil, China, India, Indonesia, Mexico, Russia, and Turkey--could be double the size of their advanced counterparts, the G7--Canada, France, Germany, Italy, [...]

How does oil price uncertainty affect consumer sentiment in advanced economies? Is the response of consumer sentiment to exogenous positive and negative oil price shocks symmetric or asymmetric? Motivated to answer these questions, this paper provides a comprehensive examination of the effects of real oil price shocks on consumer sentiment in the G7 economies in the context of two classes of empirical models. With the application of a bivariate structural GARCH-in-Mean VAR model we find that (in general) oil price uncertainty has a negative and statistically significant effect on consumer sentiment in the G7 countries. Moreover, using a test of symmetry, we find that the relationship between oil prices and consumer sentiment is in general asymmetric. JEL Classification: C32, D12, Q43, O57 [ABSTRACT FROM AUTHOR]

This paper uses neoclassical microeconomic theory to investigate the demand for energy and interfuel substitution in India at the sectoral level. It makes full use of the relevant economic theory and econometrics and generates inference in terms of Allen and Morishima elasticities of substitution that are internally consistent with the data and nonlinear models used. The results indicate that the interfuel substitution elasticities are consistently below unity in the household and power sectors, revealing the limited ability to substitute between major energy commodities in these two sectors. However, significant substitution relationships are found in the industrial and transportation sectors, suggesting that energy price changes in these sectors will significantly shift the demand for energy and consumption. Based on measured elasticities of substitution, we also discuss implications of energy price shocks on inflation and inflation targeting strategies by the central bank. [ABSTRACT FROM AUTHOR]

We use a semiparametric GARCH-in-Mean copula model to examine the volatility dynamics and tail dependence between cryptocurrency markets and financial markets. We do not find any statistically significant tail dependence between the financial and cryptocurrency markets, but we find lower tail dependence between Bitcoin and stock returns. There is lower tail dependence among Bitcoin, Ethereum, and Litecoin, and the lower tail dependence between Ethereum and Litecoin returns is the strongest. The GARCH-in-Mean model shows that the uncertainty effect on cryptocurrency returns is not statistically significant, while uncertainty has a negative and statistically significant effect on Bitcoin returns. The fact that there is no tail dependence between cryptocurrency and the interest rate or the effective exchange rate of U.S. dollar suggests that cryptocurrency could offer safe haven, defined as an asset that is uncorrelated with stocks and bonds. [ABSTRACT FROM AUTHOR]

This paper derives a new set of results that provide corrective measures of overall technical inefficiency that either have been ignored or wrongly assumed in the literature. Using directional distance functions, we argue that overall technical inefficiency is not only a function of input and output technical inefficiencies as previous studies claim but also of the interaction between them. The derivation of the interactive effects between input and output technical inefficiencies (IEIOs) solves the arbitrary decomposition of overall technical inefficiency into input and output components. We also show that the IEIO depends on the choice of the directional vector and whether quantities and prices are taken into consideration. Using exogenous and endogenous directional vectors, we prove these results theoretically and empirically using the US commercial banking data set. Using Bayesian estimation with the monotonicity conditions imposed at each observation, we estimate input and output technical inefficiencies separately using directional input and output distance functions with the three commonly used directional vectors; the unit value, the observed input−output, and the optimal directional vectors. The overall technical inefficiency is estimated using systems of equations to incorporate the interactive effect equation and to address the endogeneity of inputs and outputs. Consistent with the theoretical results, we find significant evidence of the IEIO which has a negative effect on the overall technical inefficiency. This result is robust to alternative directional vectors and model specifications, suggesting that the adjustability of both inputs and outputs is required for the improvement of the efficiency of the US commercial banks. [ABSTRACT FROM AUTHOR]

We use nonparametric and parametric demand analysis to empirically estimate a credit card-augmented monetary asset demand system, based on the Minflex Laurent flexible functional form, and a sample period that includes the 2007–2009 global financial crisis and the COVID-19 pandemic. We also use multivariate copulae in an attempt to capture various patterns of dependence structures. In doing so, we relax the joint normality assumption of the errors of the demand system and estimate the model without having to delete one equation as is usually the practice. We show that the Minflex Laurent copula-based demand system produces a higher income elasticity for credit card transaction services and higher Morishima elasticities between credit card transaction services and monetary assets compared to the traditional estimation of the Minflex Laurent demand system. We also show that credit cards are substitutes for monetary assets and that there is lower tail dependence between the demand for credit card transaction services and transaction balances. [ABSTRACT FROM AUTHOR]

This paper examines correlation and dependence structures between money and the level of economic activity in the USA in the context of a Markov-switching copula vector error correction model. We use the error correction model to focus on the short-run dynamics between money and output while accounting for their long-run equilibrium relationship. We use the Markov regime-switching model to account for instabilities in the relationship between money and output, and also consider different copula models with different dependence structures to investigate (upper and lower) tail dependence. [ABSTRACT FROM AUTHOR]

Although the inflation rates have been low in recent years, the uncertainties that put downward pressure on inflation remain the key uncertainties that economies around the world are facing, and they could be unexpectedly persistent. In this article, we investigate the relation between nominal uncertainty and macroeconomic performance in the G7 and EM7 countries. We use a bivariate VARMA GARCH‐in‐mean asymmetric BEKK model to estimate the response of output growth to inflation uncertainty. We find that inflation uncertainty is persistent in most of the G7 and EM7 countries. The uncertainty surrounding future inflation lowers output growth in most of the G7 countries; however, the effect is mixed in the EM7 countries. [ABSTRACT FROM AUTHOR]

In this paper, we provide and illustrate a solution to the inter‐related problems of estimation of asset demand functions, instability of money demand relations, and monetary aggregation. We use an econometric framework that very flexibly allows for changes in the coefficients of the asset demand functions and then quantifying the implications that this asset demand instability has for monetary policy and monetary aggregation. Our approach allows the estimation of asset demand functions in a systems context, using a flexible functional form for the aggregator function, based on the dual approach to demand system generation. However, instead of assuming that consumer preferences are fixed as in the neoclassical demand theory, we assume Markov regime switching, thus allowing for complicated nonlinear dynamics and sudden changes in the parameters of the asset demand functions and the underlying aggregator function. We use the monthly time series data on monetary asset quantities and their user costs, recently produced for the United States and maintained within the Center of Financial Stability, and the Normalized Quadratic (NQ) flexible functional form with Markov regime switching, to generate inference in terms of a full set of elasticities, simultaneously achieving consistency with the data‐generating process and economic and econometric regularity. We find evidence that our five‐regime NQ model provides a better fit of the actual data than a single regime model, that the asset demand specifications exhibit instability between regimes, but are rather stable within regimes, and that the assets are in general Morishima substitutes with the Morishima elasticities of substitution always being below unity. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of oil market structural shocks on the prices of natural gas liquids (NGLs), including ethane, propane, normal butane, isobutane, and natural gasoline, over the period from January 1985 to April 2020. To identify the structural demand and supply shocks in the crude oil market, we use a vector autoregression model and assume that the innovations to the real price of crude oil are predetermined with respect to the local NGLs markets. Our results show that, in the long run, more than 55% of the variation in the real price of NGLs is explained by the structural shocks in the global crude oil market. We also find that, unlike oil supply shocks, demand-side shocks have permanent and persistent impacts on NGLs’ real prices and should be of main concern to investors aiming to develop gas wells and NGLs producing technologies. [ABSTRACT FROM AUTHOR]