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This paper examines several central issues in the empirical modeling of money demand. These issues include economic theory, data measurement, parameter constancy, the opportunity cost of holding money, cointegration, model specification, exogeneity, and inferences for policy. Review of these issues at a general level is paralleled by discussion of specific empirical applications, including some new results on the demand for narrow money in the United Kingdom. [ABSTRACT FROM AUTHOR]

Several studies have developed empirical models of U.K. money demand using the century of annual and phase-average data in Friedman and Schwartz (1982). The current paper evaluates key models from those studies, employing tests of constancy and encompassing. The evidence strongly favors an annual model from Ericsson, Hendry, and Prestwich (1998a), whereas models based on the phase-average data fare poorly. [ABSTRACT FROM AUTHOR]

This paper evaluates an empirical model of U. K. money demand developed by Milton Friedman and Anna J. Schwartz in Monetary Trends in the United States and the United Kingdom. Testing reveals misspecification and hence the potential for an improved model. Using recursive procedures on their annual data, we obtain a better-fitting, constant, dynamic error-correction (cointegration) model. Results on exogeneity and encompassing imply that our money-demand model is interpretable as a model of money but not of prices, since its constancy holds only conditionally on contemporaneous prices. [ABSTRACT FROM AUTHOR]

Presents an update of the empirical model developed by Hendry and Ericsson of the demand for broad money in the United Kingdom over 1878-1975. Factors accounted for in the update of the model; Appropriation of measures of opportunity cost and credit deregulation; Details on the models parameters; Identification of the issues which policy implications are provided for.

The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a daily FTSE adjusted price series, commencing in April 2009 and terminating in March 2021, and a corresponding daily S&P500 Index adjusted-price series obtained from Yahoo Finance. The data period includes all the gyrations caused by the Brexit vote in the UK, beginning with the vote to leave in 2016 and culminating in the actual agreement to withdraw in January 2020. It was then followed by the impact of the global spread of COVID-19 from the beginning of 2020. The results of the analysis suggest that movements in the contemporaneous levels of daily S&P500 Index levels have very significant effects on the behaviour of the levels of the daily FTSE 100 Index. They also suggest that negative movements have larger impacts than do positive movements in S&P500 levels, and that long-term multiplier impacts take about 10 days to take effect. These effects are supported by the results of quantile regression analysis. A key result is that weak form market efficiency does not apply in the second period. [ABSTRACT FROM AUTHOR]

In this paper, we apply Bayesian averaging of classical estimates (BACE) and Bayesian model averaging (BMA) as an automatic modeling procedures for two well-known macroeconometric models: UK demand for narrow money and long-term inflation. Empirical results verify the correctness of BACE and BMA selection and exhibit similar or better forecasting performance compared with a non-pooling approach. As a benchmark, we use Autometrics—an algorithm for automatic model selection. Our study is implemented in the easy-to-use gretl packages, which support parallel processing, automates numerical calculations, and allows for efficient computations. [ABSTRACT FROM AUTHOR]