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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.

In this paper the techniques of zero-non-zero (ZNZ) patterned vector autoregressive modelling are utilized to examine two issues associated with the European single currency – the euro. First, “Granger causality” is employed to examine the causal linkages between the euro exchange rate, the euro area money supply and the gross domestic product (GDP) growth in the euro area. Second, we examine the hypothesis that the euro has become a major influence on international stock markets by testing for the causal relationships between movements in the euro exchange rate, the U.K. pound exchange rate and the London stock market index.

In the context of Dynamic Factor Models, we compare point and interval estimates of the underlying unobserved factors extracted using small- and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the maximum likelihood Kalman filter and smoother factor estimates are highly correlated with those obtained using hybrid procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny.

This chapter investigates how market competition relates to firm corporate social responsibility (CSR) investment strategy. Using separate measures to capture different dimensions of competition, we find that firms are likely to invest more (less) in socially responsible initiatives when competition from existing rivals (potential entrants) is high. We also find that industry leaders are more likely to engage in more CSR when higher levels of competition exist, while followers primarily choose to strengthen other aspects of their competitiveness instead. Finally, analyzing the impact of CEO overconfidence on CSR engagement, our study finds novel evidence suggesting that firms with overconfident CEOs tend to underestimate the intensity of competition and are less sensitive to the impact of market competition on CSR engagement, relative to rational CEOs.

This chapter estimates a regime switching Taylor Rule for the European Central Bank (ECB) in order to investigate some potential nonlinearities in the forward-looking policy reaction function within a real-time framework. In order to compare observed and predicted policy behavior, the chapter estimates Actual and Perceived regime switching Taylor Rules for the ECB. The former is based on the refi rate set by the Governing Council while the latter relies on the professional point forecasts of the refi rate performed by a large investment bank before the upcoming policy rate decision. The empirical evidence shows that the Central Bank’s main policy rate has switched between two regimes: in the first one the Taylor Principle is satisfied and the ECB stabilizes the economic outlook, while in the second regime the Central Bank cuts rates more aggressively and puts a higher emphasis on stabilizing real output growth expectations. Second, the results point out that the professional forecasters have broadly well predicted the actual policy regimes. The estimation results are also robust to using consensus forecasts of inflation and real output growth. The empirical evidence from the augmented Taylor Rules shows that the Central Bank has most likely not responded to the growth rates of M3 and the nominal effective exchange rate and the estimated regimes are robust to including these additional variables in the regressions. Finally, after the bankruptcy of Lehman Brothers the policy rate has switched to a crisis regime as the ECB has focused on preventing a further decline in economic activity and on securing the stability of the financial system.

This is a study of forecasting models that aggregate monthly times series into bimonthly and quarterly models using the 1,428 seasonal monthly series of the M3 competition of Makridakis and Hibon (2000). These aggregating models are used to answer the question of whether aggregation models of monthly time series significantly improve forecast accuracy. Through aggregation, the forecast mean absolute deviations (MADs) and mean absolute percent errors (MAPEs) were found to be statistically significantly lower at a 0.001 level of significance. In addition, the ratio of the forecast MAD to the best forecast model MAD was reduced from 1.066 to 1.0584. While those appear to be modest improvements, a reduction in the MAD affects a forecasting horizon of 18 months for 1,428 time series, thus the absolute deviations of 25,704 forecasts (i.e., 18*1,428 series) were reduced. Similar improvements were found for the symmetric MAPE.

The United States (U.S.) has been described as the root of the global financial crisis. The events of the financial, sovereign debt, and Euro crisis and the accompanying economic turmoil that have spread throughout most of the Western world have been traced back to the excessive consumer borrowing, sub-prime mortgage lending and ultimately the housing bubble in the United States. Its burst in 2008 created a shock that overshadowed prior recession and fiscal stress of governmental entities in the United States. Deriving over 90% of their own tax revenues from property taxes, local governments in Michigan have been hit even more excessively. However, the cases analysed in this chapter not only tell a unique story of deep shock and legacy costs, but also of creative ways of surviving the crisis, exerting different patterns of financial resilience. In general, state regulations restricted buffering the impact, and some cities additionally suffered from their geographical vicinity to and economic dependency on Detroit, a city that stands for the turbulence of the U.S. automobile industry. After first deploying buffering capacities that still existed, two cases saw the crisis as an opportunity to address their vulnerabilities (reactive adapters), an opportunity that was not recognised in the case of a constrained adapter. In contrast, one case showed strong anticipatory and coping capacities that have been built up in the past, equipping the local government to operate in a lean and efficient way, and to proactively adapt to arising shocks.

In this paper, the gamma test is used to determine the order of lag-k tail dependence existing in financial time series. Using standardized return series, statistical evidences based on the test results show that jumps in returns are not transient. New time series models which combine a specific class of max-stable processes, Markov processes, and GARCH processes are proposed and used to model tail dependencies within asset returns. Estimators for parameters in the models are developed and proved to be consistent and asymptotically joint normal. These new models are tested on simulation examples and some real data, the S&P 500.