VAR models and methods for monetary and health economics

In recent years vector autoregressive (VAR) models have become the main econometric tool to test if may exist a relationship between variables and to assess the effects of policy. This thesis studies three different identification approaches starting from reduced-form VAR models (including sample period, set of endogenous variables, deterministic terms and lag length). We use in the case of VAR models Granger Causality test to verify the ability of one variable to predict another one, in the case of cointegrating relationship we use VECM models to jointly estimate long-run and short-run coefficients from data and in the case of small dataset and problem of overfitting we use Bayesian VAR models with impulse response functions and variance decomposition to analyze the effect of shocks on the macroeconomic variables. For this, the empirical studies are carried out using specific datasets and different assumptions. The three VAR models approaches have been used: first to study decisions on monetary policy for discriminating among Post-Keynesian analyses of monetary theory and policy and more specifically the so-called “solvency rule” (Brancaccio and Fontana 2013, 2015) and nominal GDP targeting rule in the Euro Area (paper 1); second to extend the evidence of endogenous money hypothesis by evaluating the effects of banks’ securitization on monetary transmission mechanism in the United States (paper 2); third to evaluate the effects of ageing on health care expenditure in Italy in terms of policy implications (paper 3). The thesis is introduced in Chapter 1, which outlines the context, motivation and aim of this research. Furthermore the structure and a summary of the approach as well as the main findings of the remaining chapters are described. Chapter 2 examines by using a VAR model in first differences with quarterly data of Eurozone whether decisions on monetary policy can be interpreted in terms of a “monetary policy rule” with specific reference to the so-called “nominal GDP targeting rule” (McCallum 1988; Hall and Mankiw 1994; Woodford 2012). The results indicate a causal relation proceeding from deviation between the growth rates of nominal GDP and target GDP to variation in three month market interest rate. The same analysis do not, however, appear to confirm the existence of a significant inverse causal relation from variation in the market interest rate to deviation between the nominal and target GDP growth rates. Similar results were obtained on replacing the market interest rate with ECB refinancing interest rate. This confirmation of only one of the two directions of causality does not support an interpretation of monetary policy based on the nominal GDP targeting rule and gives rise to doubt in more general terms as to the applicability of the Taylor rule and all of the conventional rules of monetary policy to the case in question. The results appear, instead to be more in line with other possible approaches, such as those based on some Post-Keynesian and Marxist analyses of monetary theory and policy and more specifically the so-called “solvency rule” (Brancaccio and Fontana 2013, 2015). These lines of research challenge the simplistic argument that the scope of monetary policy consists in the stabilization of inflation, real GDP or nominal income around a “natural equilibrium” level. Rather, they suggest that central banks actually follow a more complex purpose, which is the political regulation of financial system with particular reference to the relations between creditors and debtors and the related solvency of economic units. Chapter 3 analyzes loans supply by explicitly accounting for the money endogeneity arising from securitization bank’s activity over the period 1999-2012. Although there is a large body of literature that investigates the endogeneity of money supply this approach has rarely been adopted to investigate money endogeneity in a short-term and long term study of the United States during the two main crises: the dot-com bubble burst (1998-1999) and the sub-prime mortgage crisis (2008-2009). Specifically, we consider the effects of financial innovation on lending channel by using the loans series adjusted for securitization to investigate whether the American banking system is incentive to seek the cheapest sources of financing as securitization, which affects its response to restrictive monetary policy (Altunbas et al., 2009). The analysis is based on the aggregate M1 and M2. In the study period the Federal Reserve uses M1, M2 money supply as its monetary target. Employing VECM models, we examine a long-run relationship among level variables and evaluate the effects of money supply by measuring how much the monetary policy stance affects short-run deviations from long-run relationship. The results show that securitization influences the impact of loans on M1 and M2. This implies money supply endogeneity in favor of structuralist approach and motivates agents to increase securitization with a preemptive motive to hedge against policy shocks. Chapter 4 investigates the relationship between per capita health care expenditure, per capita GDP, aging index and life expectancy in Italy over the period 1990-2013 by employing Bayesian VAR models and annual data drawn from OECD and EUROSTAT database. The impulse response functions and variance decomposition analysis find evidence of a positive relationship from per capita GDP to per capita health care expenditure, from life expectancy to per capita health care expenditure and from aging index to per capita health care expenditure. The impact of ageing on health expenditure is significant and stronger than the other variables. Overall, our findings suggest that disabilities closely associated with ageing may be the main driver of health expenditure in the short medium-run. A good health care management contributes to improve patient welfare without increasing total health expenditure. However, policies that improve health status of the elderly might be necessary for a lower per capita demands on health and social services.