Alessio Moneta

Causal inference by independent component analysis: theory and applications

Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this study, we present a recently developed method for estimating such models, which uses non-normality to recover the causal structure underlying the observations. We show how the method can be applied to both microeconomic data (to study the processes of firm growth and firm performance) and macroeconomic data (to analyse the effects of monetary policy).

Graphical Models for the Identification of Causal Structures in Multivariate Time Series Models

In this paper we present a semi-automated search procedure to deal with the problem of the identification of the contemporaneous causal structure connected to a large class of multivariate time series models. We refer in particular to multivariate models, such as vector autoregressive (VAR) and dynamic factor (DF) model, in which the background or theoretical knowledge is not sufficient or enough reliable to build a structural equations model. VAR models deal with a small number of time series models (the maximum number is typically between 6 and 8), while DF models deal with a large number of time series, possibly larger than the number of observation (T ) over time. Both VAR and DF models have proven to be very efficient in the macroeconomic and financial literature to address different empirical issues, such as forecasting, summarizing the statistical properties of the data, and building economics indicators (of business cycles, for instance). Moreover, DF models can be used in the financial literature to estimate insurable risk and in the macroeconomic literature to learn about aggregate behavior on the basis of microeconomic data (sectors, regions). (Forni and Lippi (1997), Forni et al. (2000), and Stock and Watson (2001) are useful references).

A Method for Agent-Based Models Validation

This paper proposes a new method for empirically validate simulation models that generate artificial time series data comparable with real-world data. The approach is based on comparing structures of vector autoregression models which are estimated from both artificial and real-world data by means of causal search algorithms. This relatively simple procedure is able to tackle both the problem of confronting theoretical simulation models with the data and the problem of comparing different models in terms of their empirical reliability. Moreover the paper provides an application of the validation procedure to the Dosi et al. (2015) macro-model.

Causality in macroeconometrics: some considerations about reductionism and realism

This paper investigates the varieties of reductionism and realism about causal relations in macroeconometrics. There are two issues, which are kept distinct in the analysis but which are interrelated in the development of econometrics. The first one is the question of the reducibility of causal relations to regularities, measured in statistics by correlations. The second one is the question of the reducibility of causes among macroeconomic aggregates to microeconomic behaviour. It is argued that there is a continuum of possible positions between realism and reductionism for both the questions, but, as far as the second question is concerned, the dominant position of mainstream macroeconometrics is strongly reductionist. The paper defends an integrative approach that emphasizes the gradual nature of many real world cases.

Causal models and evidential pluralism in econometrics

Social research, from economics to demography and epidemiology, makes extensive use of statistical models in order to establish causal relations. The question arises as to what guarantees the causal interpretation of such models. In this paper we focus on econometrics and advance the view that causal models are ‘augmented’ statistical models that incorporate important causal information which contributes to their causal interpretation. The primary objective of this paper is to argue that causal claims are established on the basis of a plurality of evidence. We discuss the consequences of ‘evidential pluralism’ in the context of econometric modelling.

Escaping Satiation Dynamics: Some Evidence from British Household Data

Summary The tendency of sectoral demand to satiate has long been argued to be a key driver of the structural change in an economy (Pasinetti 1981; Saviotti 2001). This literature raises the question as to what extent cross-sectional patterns of household expenditure can be used to make inferences about how the demand for goods and services will grow over time. Moreover, if indeed satiation does take place, then firms and entrepreneurs could react to this situation by innovating goods and services in order to overcome stagnation in demand growth (Witt 2001). We empirically investigate this ‘satiation-escape’ hypothesis by examining the inter-temporal dynamics of Engel curves and their derivatives, which reflect how household spending on a good changes with income. Taking into account changes in the price level, we investigate whether Engel curves that exhibit cross-section satiation tend to exhibit over time an upwards shift in the satiation level jointly with a shift in position and shape. Evidence suggests that this is actually the case.

Causal inference by independent component analysis

Structural vector-autoregressive models are potentially very useful tools for guiding both macro- and microeconomic policy. In this study, we present a recently developed method for estimating such models, which uses non-normality to recover the causal structure underlying the observations. We show how the method can be applied to both microeconomic data (to study the processes of firm growth and firm performance) and macroeconomic data (to analyse the effects of monetary policy).

Can Graphical Causal Inference Be Extended to Nonlinear Settings

This paper assesses an extension of the method for graphical causal inference proposed by Spirtes et al. and Pearl to nonlinear settings. We propose nonparametric tests for conditional independence based on kernel density estimation and study their relative performance in a Monte Carlo study. Our method outperforms Fischer’s z test for nonlinear settings while subject to the so-called curse of dimensionality. We do show, however, how the latter can be overcome by using local bootstrapping.

The Janus-Faced Nature of Debt: Results from a Data-Driven Cointegrated SVAR Approach

In this paper, we investigate the causal effects of public and private debts on U.S. output dynamics. We estimate a battery of Cointegrated Structural Vector Autoregressive models, and we identify structural shocks by employing Independent Component Analysis, a data-driven technique which avoids ad-hoc identification choices. The econometric results suggest that the impact of debt on economic activity is Janus-faced. Public debt shocks have positive and persistent influence on economic activity. In contrast, rising private debt has a milder positive impact on GDP, but it fades out over time. The analysis of the possible transmission mechanisms reveals that public debt crowds in private consumption and investment. In contrast, mortgage debt fuels consumption and output in the short-run, but shrinks them in the medium-run.

Dynamic increasing returns and innovation diffusion: bringing Polya Urn processes to the empirical data

The patterns of innovation diffusion are well approximated by the logistic curves. This is the robust empirical fact confirmed by many studies in innovations dynamics. Here, we show that the logistic pattern of innovation diffusion can be replicated by the time-dependent stochastic process with positive feedbacks along the diffusion trajectory. The dynamic increasing returns process is modelled by Polya Urns. So far, Urn models have been mostly used to study the [path-dependent] limit properties. On the contrary, this work focuses on the transient [finite time] properties studying the conditions under which urn models capture the logistic trajectories which often track empirical diffusion process. As examples, we calibrate the process to match several cases of diffusion of motor ships in European countries.