Non‐Fundamentalness in Structural Econometric Models: A Review

Current economic theory typically assumes that all the macroeconomic variables belonging to a given economy are driven by a small number of structural shocks. As recently argued, apart from negligible cases, the structural shocks can be recovered if the information set contains current and past values of a large, potentially infinite, set of macroeconomic variables. However, the usual practice of estimating small size causal Vector AutoRegressions can be extremely misleading as in many cases such models could fully recover the structural shocks only if future values of the few variables considered were observable. In other words, the structural shocks may be non‐fundamental with respect to the small dimensional vector used in current macroeconomic practice. By reviewing a recent strand of econometric literature, we show that, as a solution, econometricians should enlarge the space of observations, and thus consider models able to handle very large panels of related time series. Among several alternatives, we review dynamic factor models together with their economic interpretation, and we show how non‐fundamentalness is non‐generic in this framework. Finally, using a factor model, we provide new empirical evidence on the effect of technology shocks on labour productivity and hours worked.     

A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering

We introduce a new factor model for log volatilities that considers contributions, and performs dimensionality reduction, at a global level through the market, and at a local level through clusters and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical Tree algorithm to fix the number of factors. We use the factor model to study how the log volatility contributes to volatility clustering, quantifying the strength of the volatility clustering using a new nonparametric integrated proxy. Indeed finding a link between volatility and volatility clustering, we find that a global analysis reveals that only the market contributes to the volatility clustering. A local analysis reveals that for some clusters, the cluster itself contributes statistically to the volatility clustering effect. This is significantly advantageous over other factor models, since it offers a way of selecting factors in a statistical way, whilst also keeping economically relevant factors. Finally, we show that the log volatility factor model explains a similar amount of memory to a principal components analysis factor model and an exploratory factor model.     

On some ‘Austrian’ misreadings of Cantillon's notions of intrinsic value and market price

The article clarifies some aspects of the Cantillonian notions of intrinsic value and of market prices. Furthermore, the major flaws of the ‘Austrian’ interpretations put forward in recent years by Rothbard and Thornton are highlighted. This criticism provides an additional dimension to the view already expressed by Groenewegen against the new edition of Cantillon's Essai by Thornton. Finally, the connection between the Essai and mature classical economics is highlighted and the proposed ‘Austrian’ interpretation of Cantillon is strongly rejected.     

The evolution of financial intermediation: Evidence from 19th‐century Ontario microdata

Microdata for Ontario decedents in 1892 and 1902 is analyzed to help explain the dramatic growth of the Canadian banking system in the late 19th century. Combining data from probate inventories with census data at the township level, we find that the expansion of deposit banking happened at the extensive rather than intensive margin and was correlated with the expansion of the branch network of the banking system, although we cannot assign causation. Wealth and urbanization help to explain the growth of deposit banking but the significance of a dummy variable for 1902 points to other time‐correlated factors such as innovations in transportation and financial innovations that lowered costs and facilitated access to banking services.     

Correlating Lévy processes with self-decomposability: applications to energy markets

Decisions in Economics and Finance - Based on the concept of self-decomposability, we extend some recent multidimensional Lévy models built using multivariate subordination. Our aim is to...     

Capital Flows,Financial Intermediation and Macroprudential Policies

This paper develops an open-economy DSGE model with an optimizing banking sector to assess the role of capital flows, macro-financial linkages, and macroprudential policies. The key result is that macroprudential measures can usefully complement monetary policy. Countercyclical macroprudential polices can help reduce macroeconomic volatility and enhance welfare. The results also demonstrate the importance of capital flows and financial stability for business cycle fluctuations as well as the role of supply side financial accelerator effects in the amplification and propagation of shocks.     

Conditional risk premia in currency markets and other asset classes

The downside risk capital asset pricing model (DR-CAPM) can price the cross section of currency returns. The market-beta differential between high and low interest rate currencies is higher conditional on bad market returns, when the market price of risk is also high, than it is conditional on good market returns. Correctly accounting for this variation is crucial for the empirical performance of the model. The DR-CAPM can jointly rationalize the cross section of equity, equity index options, commodity, sovereign bond and currency returns, thus offering a unified risk view of these asset classes. In contrast, popular models that have been developed for a specific asset class fail to jointly price other asset classes.     

Modelling factor demands with SEM and VAR: an empirical comparison

The empirical analysis of the economic interactions between factors of production, output and corresponding prices has received much attention over the last two decades. Most contributions in this area have agreed on the neoclassical principle of a representative optimizing firm and typically use theory-based structural equation models (SEM). A popular alternative to SEM is the vector autoregression (VAR) methodology. The most recent attempts to link the SEM approach with VAR analysis in the area of factor demands concentrate on single-equation models, whereas no effort has been devoted to compare these alternative approaches when a firm is assumed to face a multi-factor technology and to decide simultaneously the optimal quantity for each input. This paper bridges this gap. First, we illustrate how the SEM and the VAR approaches can both represent valid alternatives to model systems of dynamic factor demands. Second, we show how to apply both methodologies to estimate dynamic factor demands derived from a cost-minimizing capital-labour-energy-materials (KLEM) technology with adjustment costs (ADC) on the quasi-fixed capital factor. Third, we explain how to use both models to calculate some widely accepted indicators of the production structure of an economic sector, such as price and quantity elasticities, and alternative measures of ADC. In particular, we propose and discuss some theoretical and empirical justifications of the differences between observed elasticities, measures of ADC, and the assumption of exogeneity of output and/or input prices. Finally, we offer some suggestions for the applied researcher.      

Dynamic analysis of interventions designed to achieve millennium development goals (MDG): The case of Ghana

An increasing number of countries are orientating their development strategies based on the millennium development goals (MDG), a broad set of directives agreed to by the United Nations (UN) in the year 2000. Developing coherent plans to achieve MDG has been complicated by their multidisciplinary nature, and by the complexity of the system being managed. The “system” here is the socio-economic construct within which populations live and operate. In an effort to support this planning process, various approaches have been developed to help realize MDG within specified budgets. The work described here complements the most commonly used approaches by analyzing the impact of alternative interventions in an integrated socio-economic-environmental framework. In doing so, we utilize system dynamics, which is well-suited to support the analysis of dynamic, complex issues such as those that characterize MDG planning. Such an approach allows us to estimate impacts of MDG-related interventions on the economic and demographic development of countries under study, as well as the possible synergies between and amongst selected interventions, e.g., those involving education and health. Results indicate that failure to account for such factors can lead to sub-optimal strategies. Our objective is thus to provide policy-makers with a more comprehensive view of the outcomes generated by alternative MDG interventions, with emphasis on the ability to finance given strategies.