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No matter its source, financial- or policy-related, uncertainty can feed onto itself, inflicting the real economic sector, altering expectations and behaviours, and leading to identification challenges in empirical applications. The strong intertwining between policy and financial realms prevailing in Europe, and in Euro Area in particular, might complicate the problem and create amplification mechanisms difficult to pin down. To reveal the complex transmission of country-specific uncertainty shocks in a multi-country setting, and to properly account for cross-country interdependencies, we employ a global VAR specification for which we adapt an identification approach based on magnitude restrictions. Once we separate policy uncertainty from financial uncertainty shocks, we find evidence of important cross-border uncertainty spill-overs. We also uncover a new amplification mechanism for domestic uncertainty shocks, whose true nature becomes more blurred once they cross the national boundaries and spill over to other countries. With respect to ECB policy reactions, we reveal stronger but less persistent responses to financial uncertainty shocks compared to policy uncertainty shocks. This points to ECB adopting a more (passive or) accommodative stance towards the former, but a more pro-active stance towards the latter shocks, possibly as an attempt to tame policy uncertainty spill-overs and prevent the fragmentation of the Euro Area financial markets., 52 pages

High-frequency (HF) surprises of relevant asset prices around central bank meetings are extensively employed in the literature to identify the effects of conventional/unconventional monetary policy. This identification strategy assumes that these surprises reflect either a single unconventional ‘monetary shock’ or, as recently suggested, jointly an unconventional monetary shock and a central bank ‘information shock’. In this paper we show that monetary policy in the euro area after 2008 is best characterized by three shocks, not two. Besides the unconventional monetary shock and the information shock, we consider a third shock resulting from the ECB directly managing fragmentation risk in the sovereign bond market. We call this additional shock ‘spread shock’, and show that it permits to solve a puzzle we observe in HF comovement of long term risk free rates and sovereign spreads around press conferences. We identify the dynamic causal effects produced by the three shocks through a proxy-SVAR methodology which, using HF surprises of the euro area risk-free yield curve, stock prices and sovereign spreads, combines sign-restrictions with narrative restrictions and then extracts external variables (instruments) from an admissible identification set. Empirical results, obtained through a daily proxy-SVAR and Local Projections based on monthly data, reveal that the spread shock represents an important ingredient of the transmission mechanism of the monetary policy after the Global Financial Crisis. It reflects ECB’s attempt to offset self-fulling expectations of default in the euro area sovereign debt markets and behaves as a complement, not a substitute of the information shock.

This article deals with the role of time in causal models in the social sciences. The aim is to underline the importance of time-sensitive causal models, in contrast to time-free models. The relation between time and causality is important, though a complex one, as the debates in the philosophy of science show. In particular, an outstanding issue is whether one can derive causal ordering from time ordering. The article examines how time is taken into account in demography and in economics as examples of social sciences in which considerations about time may diverge. We then present structural causal modeling as a modeling strategy that, while not essentially based on temporal information, can incorporate it in a more or less explicit way. In particular, we argue that temporal information is useful to the extent that it is placed in a correct causal structure, thus further corroborating the causal mechanism or generative process explaining the phenomenon under consideration. Despite the fact that the causal ordering of variables is more relevant than the temporal order for explanatory purposes, in establishing causal ordering the researcher should nevertheless take into account the time-patterns of causes and effects, as these are often episodes rather than single events. For this reason in particular, it is time to put time at the core of our causal models.

How large are government spending and tax multipliers? The fiscal proxy-SVAR literature provides heterogenous estimates, depending on which proxies - fiscal or non-fiscal - are used to identify fiscal shocks. We reconcile the existing estimates via a flexible vector autoregressive model that allows to achieve identification in presence of a number of structural shocks larger than that of the available instruments. Our two main findings are the following. First, the estimate of the tax multiplier is sensitive to the assumption of orthogonality between total factor productivity (non-fiscal proxy) and tax shocks. If this correlation is assumed to be zero, the tax multiplier is found to be around one. If such correlation is non-zero, as supported by our empirical evidence, we find a tax multiplier three times as large. Second, we find the spending multiplier to be robustly larger than one across different models that feature different sets of instruments. Our results are robust to the joint employment of different fiscal and non-fiscal instruments.

This paper examines different approaches for assessing causality as typically followed in econometrics and proposes a constructive perspective for improving statistical models elaborated in view of causal analysis. Without attempting to be exhaustive, this paper examines some of these approaches. Traditional structural modeling is first discussed. A distinction is then drawn between model-based and design-based approaches. Some more recent developments are examined next, namely history-friendly simulation and information-theory based approaches. Finally, in a constructive perspective, structural causal modeling (SCM) is presented, based on the concepts of mechanism and sub-mechanisms, and of recursive decomposition of the joint distribution of variables. This modeling strategy endeavors at representing the structure of the underlying data generating process. It operationalizes the concept of causation through the ordering and role-function of the variables in each of the intelligible sub-mechanisms.

Instrumental Variables (IV) are widely used in econometrics to overcome endogeneity problem in regression models, which occurs when regressors are correlated with the stochastic component. Nonetheless, in applied works, practitioners face with instruments that are collectively ``weak'', i.e. poorly correlated with endogenous regressors. Under weak instruments, conventional estimators are no longer consistent and asymptotically normal. Furthermore, bootstrap methods could be useful to improve inference in IV estimation. However, under poorly relevant instruments, the bootstrap is deemed invalid and its use is generally discouraged in applied papers. In this work, we propose a new derivation of bootstrapped IV estimators under weak instruments asymptotics (Stock and Yogo, 2005) using residual--based bootstrap method involving fixed or resampled instruments. We prove that bootstrap counterpart of estimators, conditionally on the data, converges to a random distribution preserving some patterns (non--normality) of weak and irrelevant instruments scenarios. These issues may be also reflected in bootstrap--based confidence sets and hypothesis testing. In this sense, we explore the usefulness of bootstrap methods to provide information on the weakness (or the strength) of the instruments. We consider descriptive indicators and develop new bootstrap-based tests useful to detect weak instruments in IV framework. The method basically relies on Angelini et al. (2016) and allows to test normality of a certain number of (possibly standardized) bootstrap replications. Since conventional normality tests can lose power in presence of more instruments and high endogeneity, we propose new test statistics with the aim to test standard normality on the bootstrap replications. These tests are based on the moments of standard normal and are asymptotically chi--square distributed under the null hypothesis. In conclusion, we find that, in some cases, bootstrapped estimators may be used to test weak identification.

Companies are under an increasing pressure by policy makers to publicize data breaches. Such notification obligations require announcing the loss of personal data collected from customers, because of hacker attacks or other incidents. While notification is likely to impact on firms’ reputation, we know little about the impact such notifications have on consumers with respect to disclosure of their personal data. We present the problem as a dynamic lottery with personal data under the risk of privacy shocks and experimentally study how the privacy breach notification changes an individual’s behavior regarding data disclosure. Our results suggest that the notification induces individuals – disregarding the sensitivity of their data – to disclose more.

The idea that public debt may represent a burden for the economic system as a whole has distant origins and focuses on who and how should pay for debt, and with what consequences on the economy. Nevertheless, particularly influential both for academic research and the implementation of the fiscal corrective policies was the empirical paper proposed by Reinhart and Rogoff in 2010 at the dawn of the crisis. Reinhart and Rogoff (2010), in a large panel of countries, identified a critical threshold of 90% of the debt-to-GDP ratio beyond which debt is harmful to growth. Several countries in the world were fast approaching that threshold or already were well beyond it. Though Reinhart and Rogoff’s work was affected by many flaws, it has spurred buoyant empirical research in search of the general debt thresholds above which growth is jeopardised by public debt. Further works have supported the existence of critical debt-to-GDP ratios under various time and space observational fields, but results of these researches are inconclusive or controversial, as discussed in Chapter 2. Country-specific characteristics and contingencies play in fact a prominent role, thus prompting a branch of literature that attempts to comprehensively understand the debt-growth relationship and its determinants (see for instance Panizza and Presbitero, 2014; Eberhardt and Presbitero, 2015). In contrast with the findings of the broad threshold literature and of many theoretical models, the idea that public debt is always harmful to economic growth has partially been reconsidered in the last few years. Nevertheless, the existence of a linkage between debt and growth has not been rejected: the long-run relationship between such macroeconomic variables is inevitably and broadly affected by heterogeneous factors. However, in retrospect and as emerges in Chapter 1, one may say that the empirical pursuit of the debt-to-GDP threshold harmful to growth lacks deeper foundational work: why should we expect a negative public debt-growth relationship? In addition, if such a relationship exists, why should it take the specific form of a threshold of the debt-to-GDP ratio, and why should we expect this threshold to be equally valid across time and space? These questions are the starting point of this Doctoral Thesis, which is organised as follows. Chapter 1 surveys the theoretical literature concerning public debt and economic growth, aiming at finding a theoretical foundation for the debt-threshold literature. Overall, there is no clear and straightforward answer to the questions of why we should expect a negative public debt-growth relationship in the first place, why it should take the specific form of a threshold of the debt-to-GDP ratio, and why we should expect this threshold to be equally valid across time and space. Or, from another perspective, there are many possible answers and many elements affecting them, thus reflecting the complexity of the argument, as well as the variety of the empirical situations. In particular, the literature that I examine, on the one hand offers a rich variety of explanations and insights to researchers of the debt-growth relationship but, on the other, it does not provide any one-way conclusion: the relationship may be negative, positive, or even no relationship may exist, both from a theoretical and an empirical point of view. Even less is theoretically founded the existence of a general debt-to-GDP threshold above which growth is consistently stifled. Each country’s specific characteristics, circumstances, and events have an overwhelming importance that cannot be encapsulated in a single general law. In Chapter 1, I also present a fiscal model of endogenous growth that may help address the theoretical issues in an orderly and consistent manner along two specific coordinates of debt assessment: sustainability/unsustainability, and efficiency/inefficiency. The thrust of the model is that no meaningful assessment of debt and its effect on growth at any point in time is possible without reference to the whole debt trajectory and the specific state of the economy along the trajectory. Chapter 2 reviews the empirical literature and focuses on the debt-growth relationship from an econometric point of view. As before, it is difficult to derive a univocal conclusion on the nature of such a relationship on the basis of the literature’s findings: the existence of a significant negative relationship between debt and growth is the predominant thinking, though in contrast with the conclusions of several works. For these reasons, the aim of Chapter 2 is to go to the roots of the debt-growth relationship, to investigate whether the outstanding debt and the GDP are linked. To this end, I have adopted a research methodology that differs from the most common employed in the literature on debt-to-GDP thresholds. First, my analysis does not hinge on any specific theory, and it should not be considered as a proof of a specific theoretical statement. Rather, it is based on the approach outlined by Hoover et al. (2008) and aims at understanding "what the data say" without imposing aprioristic theoretical structures. A second methodological choice consistent with this approach is to treat the (growth of the) amount of public debt and (the growth of) GDP as the two genuine primitives, without imposing the debt-to-GDP ratio as a primitive itself. In fact, for this to be possible, the two underlying primitives should display well defend statistical properties, namely cointegration and convergence towards a long-term equilibrium value, which are usually not tested in the literature. Third, I believe that the heterogeneity, or non-generality, of results that I have pointed out before should be taken as an intrinsic feature of the problem at hand, so that a viable strategy is to restrict, rather than expand, the observational field. I have set time and space limits to my dataset by purpose: my analysis is based on a panel dataset including quarterly data for 25 Eastern and Western European countries from 1999Q1 to 2015Q4. The Eurozone represents a unique "field experiment" of a large number of countries where some key conditioning factors of fiscal policy are common and exogenous, namely fiscal targets and rules, monetary policy, and the exchange rate with the rest of the world. The main result is that a long-run equilibrium relationship between GDP and debt exists for some countries ― and debt and GDP tend to adjust towards it ― but it is not generalisable. Where a relationship exists, it does not always imply that the debt-to-GDP ratio may be the appropriate variable for describing it. Moreover, cross-country heterogeneity and the role of the financial crisis and of the austerity periods remain substantial and overwhelming factors. Therefore, a unique equation describing the GDP-debt relationship does not seem to exist, which entails the impossibility to derive a meaningful general debt-to-GDP threshold. Thus far I have focused on the general relationship between debt and growth from both the theoretical and the empirical points of view. Turning to the analysis of the Sovereign Debt Crisis and of the austerity period, Chapter 3 attempts to explain what has driven austerity ― measured as the first difference of the cyclically adjusted structural primary balance ― within a dataset of 28 European countries. In the first part of this chapter I present a correlation analysis that describes the relationship between the variable austerity and each of the considered determinants, that are brought back to four main sets of variables: fiscal discipline, market discipline, fiscal consolidation, and macroeconomic stabilisation. The second part implements a panel econometric analysis based on the principal component factor analysis and on the pooled partial common correlation effect estimator. Results show that the variables and factors of the analysis are not able to fully explain austerity, though an important contribution is provided by the enforcement of the Eurozone fiscal rules (the adoption of excessive deficit procedures) and is partially counterbalanced by the cyclical position of the economy. The last chapter, Chapter 4, aims at gaining insight into the role of debt and government expectations and their impact on growth under uncertainty conditions. In fact, it is possible that the effects of austerity measures in some countries, for instance the so-called PIIGS, were amplified by uncertainty. My ambition is to relate austerity with consumers’ expectations, thus studying whether and when consumers’ beliefs about public debt and government intervention affect their consumption, savings, and tax compliance choices with a direct impact, at the aggregate level, on economic growth. Therefore, Chapter 4 implements a laboratory experiment to study how people react in a generalized framework in which public debt may be unexpectedly reduced. The debt dynamics arises endogenously: within a public good game, taxes are collected from all participants and are used to cover a given level of public expenditure, which is then equally distributed to the same participants at the beginning of each round. If the collected amount of taxes is lower than what the public expenditure would require, a deficit is generated. Moreover, reproducing a forced withdrawal, the outstanding amount of public debt can be reduced upon accessing subjects’ savings. Within this setting, expectations are directly elicited by asking subjects if they believe that public debt is going to be reduced, and if they think that the other subjects believe that public debt is sustainable. Therefore, it is possible to identify whether and how agents’ allocations and expectations are affected by the public debt path. As mentioned above, a peculiarity of my approach is the endogenous dynamics of public debt: not only it avoids introducing predetermined dynamics, but also increases the ecological validity of the experiment. Participants are indeed more psychologically involved in the debt mechanism and they might feel responsible for the raise in debt. On the other hand, an exogenous dynamics could depict public debt and tax compliance as irrelevant. Results show that this experimental framework is characterized by relatively high and often increasing aggregate savings and relatively low and decreasing aggregate consumption. Interestingly, an increase in the debt-reduction expectations and a decrease in the perceived debt sustainability are also found to explain savings and consumption behaviours, as is shown in the econometric part of Chapter 4.

open 3 si Pubblicato come working paper: Geraci, Andrea ; Fabbri, Daniele ; Monfardini, Chiara (2014) Testing exogeneity of multinomial regressors in count data models: does two stage residual inclusion work? Bologna: Dipartimento di Scienze economiche DSE, p. 37. DOI 10.6092/unibo/amsacta/3937 . In: Quaderni - Working Paper DSE (921). ISSN 2282-6483. We study a simple exogeneity test in count data models with possibly endogenous multinomial treatment. The test is based on Two Stage Residual Inclusion (2SRI), an estimation method which has been proved to be consistent for a general class of nonlinear parametric models. Results from a broad set of simulation experiments provide novel evidence on important features of this approach. We find differences in the finite sample performance of various likelihood-based tests, analyze their robustness to misspecification arising from neglected over-dispersion or from incorrect specification of the first stage model, and uncover that standardizing the variance of the first stage residuals leads to better results. An original application to testing the endogeneity status of insurance in a model of healthcare demand corroborates our Monte Carlo findings. partially_open embargoed_20171108 Geraci, Andrea; Fabbri, Daniele; Monfardini, Chiara Geraci, Andrea; Fabbri, Daniele; Monfardini, Chiara

In this paper we discuss general identification results for Structural Vector Autoregressions (SVARs) with external instruments, considering the case in which r valid instruments are used to identify g Ï 1 structural shocks, where r Ï g. We endow the SVAR with an auxiliary statistical model for the external instruments which is a system of reduced form equations. The SVAR and the auxiliary model for the external instruments jointly form a "larger" SVAR characterized by a particularly restricted parametric structure, and are connected by the covariance matrix of their disturbances which incorporates the "relevance" and "exogeneity" conditions. We discuss identification results and likelihood-based estimation methods both in the "multiple shocks" approach, where all structural shocks are of interest, and in the "partial shock" approach, where only a subset of the structural shocks is of interest. Overidentified SVARs with external instruments can be easily tested in our setup. The suggested method is applied to investigate empirically whether commonly employed measures of macroeconomic and financial uncertainty respond on-impact, other than with lags, to business cycle fluctuations in the U.S. in the period after the Global Financial Crisis. To do so, we employ two external instruments to identify the real economic activity shock in a partial shock approach.

The contribution of this paper is two-fold. First, we derive the asymptotic null distribution of the familiar augmented Dickey-Fuller [ADF] statistics in the case where the shocks follow a linear process driven by infinite variance innovations. We show that these distributions are free of serial correlation nuisance parameters but depend on the tail index of the infinite variance process. These distributions are shown to coincide with the corresponding results for the case where the shocks follow a finite autoregression, provided the lag length in the ADF regression satisfies the same o(T1/3) rate condition as is required in the finite variance case. In addition, we establish the rates of consistency and (where they exist) the asymptotic distributions of the ordinary least squares sieve estimates from the ADF regression. Given the dependence of their null distributions on the unknown tail index, our second contribution is to explore sieve wild bootstrap implementations of the ADF tests. Under the assumption of symmetry, we demonstrate the asymptotic validity (bootstrap consistency) of the wild bootstrap ADF tests. This is done by establishing that (conditional on the data) the wild bootstrap ADF statistics attain the same limiting distribution as that of the original ADF statistics taken conditional on the magnitude of the innovations.

This thesis examines three main themes, firms productive efficiency, internationalisation of African firms, and effect of liberalisation policies on market power and market imperfections. The thesis combines two main strands in economics literature in accessing the three main themes of the papers. The first strand regards methodological approaches to estimate a production function from which productive efficiency can be computed. Consistent estimation of productive efficiency is a necessary condition to analyse firm behaviour and their response to trade policies. The thesis critically examines methodologies to estimate productive efficiency. The second strand, international trade and industrial development, analyse firms behaviour in foreign market as well as firms responses to trade liberalisation policies and their overall impact on structural transformation. The two strands of literature examined in this thesis resulted in three independent papers, each of which addresses specific issues along the spectrum of productive efficiency estimation, internationalisation, and market power.

In the analysis of the co-movements between economic series, working with variables sampled at different frequency is a common situation. In the last decade, the researchers try to implement different econometric solutions to use all the information in mixed frequency datasets. As demonstrated in the literature, referring to mixed frequency data allows to mitigate possible problems of identification, estimation and interpretation of the co-movements between different variables. One of the proposed approaches considers the general idea of extend the VAR methodology to mixed frequency data. In this discussion we present a novel procedure for the estimation of Structural MF-VAR processes, by referring to Classical Minimum Distance estimation.

This thesis aims at the study of systemic risk measurement, which became crucial after the 2007 − 2009 financial crisis. The objective of the thesis is twofold: (i) we address the issue of assessing the accuracy of systemic risk measures, (ii) we investigate the role of the long-range dependence in systemic risk forecasting, under both methodological and empirical perspectives. From the methodological point of view, we propose two appropriate loss functions, the Tail Tick Loss function and the Tail Mean Square Error, specifically designed to evaluate the CoVaR and MES accuracy, respectively. Moreover, we introduce a comprehensive model called Asymmetric-Component-GARCH (ACGARCH), which is able to capture both the leverage effect and long-range dependence. An empirical analysis of different bivariate volatility models to the daily returns of 91 US financial institutions in the period 2000 − 2012 confirms the need of employing appropriate loss functions to evaluate systemic risk accuracy and to discriminate among different competing models. Moreover, empirical results encourage the usage of the ACGARCH model in the systemic risk framework.

Building on the literature on regularization and dimension reduction methods, we have developed a quarterly forecasting model for euro area GDP. This method consists in bridging quarterly national accounts data using factors extracted from a large panel of monthly and quarterly series including business surveys and financial indicators. The pseudo real-time nature of the information set is accounted for as the pattern of publication lags is considered. Forecast evaluation exercises show that predictions obtained through various dimension reduction methods outperform both the benchmark AR and the diffusion index model without pre-selected indicators. Moreover, forecast combination significantly reduces forecast error.

We investigate the asymptotic and finite sample properties of the most widely used information criteria for co-integration rank determination in ‘partial’ systems, i.e. in co-integrated Vector Autoregressive (VAR) models where a sub-set of variables of interest is modeled conditional on another sub-set of variables. The asymptotic properties of the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC) and the Hannan-Quinn Information Criterion (HQC) are established, and consistency of BIC and HQC is proved. No- tably, consistency of BIC and HQC is robust to violations of the hypothesis of weak exogeneity of the conditioning variables with respect to the co-integration parameters. More precisely, BIC and HQC recover the true co-integration rank from the partial system analysis also when the conditional model does not convey all information about the co-integration parameters. This result opens up interesting possibilities for practitioners who can determine the co-integration rank in partial systems without being concerned with the weak exogeneity of the conditioning variables. A Monte Carlo experiment which considers large systems as data generating process shows that BIC and HQC applied in partial systems perform reasonably well in small samples and comparatively better than ‘traditional’ approaches for co-integration rank determination. We further show the usefulness of our approach and the benefits of the conditional system anal- ysis to co-integration rank determination with two empirical illustrations, both based on the estimation of VAR systems on U.S. quarterly data. Overall, our analysis clearly shows that the gains of combining information criteria with partial systems analysis are indisputable.

We propose an innovative approach to model and predict the outcome of football matches based on the Poisson Autoregression with eXogenous covariates (PARX) model recently proposed by Agosto, Cavaliere, Kristensen and Rahbek (2016). We show that this methodology is particularly suited to model the goals distribution of a football team and provides a good forecast performance that can be exploited to develop a profitable betting strategy. The betting strategy is based on the idea that the odds proposed by the market do not reflect the true probability of the match because they may incorporate also the betting volumes or strategic price settings in order to exploit bettors’ biases. The out-of-sample performance of the PARX model is better than the reference approach by Dixon and Coles (1997). We also evaluate our approach in a simple betting strategy which is applied to the English football Premier League data for the 2013/2014 and 2014/2015 seasons. The results show that the return from the betting strategy is larger than 35% in all the cases considered and may even exceed 100% if we consider an alternative strategy based on a predetermined threshold which allows to exploit the inefficiency of the betting market.

We extend the available asymptotic theory for autoregressive sieve estimators to cover the case of stationary and invertible linear processes driven by independent identically distributed (i.i.d.) infinite variance (IV) innovations. We show that the ordinary least squares sieve estimates, together with estimates of the impulse responses derived from these, obtained from an autoregression whose order is an increasing function of the sample size, are consistent and exhibit asymptotic properties analogous to those which obtain for a finite-order autoregressive process driven by i.i.d. IV errors. As these limit distributions cannot be directly employed for inference because they either may not exist or, where they do, depend on unknown parameters, a second contribution of the paper is to investigate the usefulness of bootstrap methods in this setting. Focusing on three sieve bootstraps: the wild and permutation bootstraps, and a hybrid of the two, we show that, in contrast to the case of finite variance innovations, the wild bootstrap requires an infeasible correction to be consistent, whereas the other two bootstrap schemes are shown to be consistent (the hybrid for symmetrically distributed innovations) under general conditions.

This paper explores the potential of bootstrap methods in the empirical evalu- ation of dynamic stochastic general equilibrium (DSGE) models and, more generally, in linear rational expectations models featuring unobservable (latent) components. We consider two dimensions. First, we provide mild regularity conditions that suffice for the bootstrap Quasi- Maximum Likelihood (QML) estimator of the structural parameters to mimic the asymptotic distribution of the QML estimator. Consistency of the bootstrap allows to keep the probability of false rejections of the cross-equation restrictions under control. Second, we show that the realizations of the bootstrap estimator of the structural parameters can be constructively used to build novel, computationally straightforward tests for model misspecification, including the case of weak identification. In particular, we show that under strong identification and boot- strap consistency, a test statistic based on a set of realizations of the bootstrap QML estimator approximates the Gaussian distribution. Instead, when the regularity conditions for inference do not hold as e.g. it happens when (part of) the structural parameters are weakly identified, the above result is no longer valid. Therefore, we can evaluate how close or distant is the esti- mated model from the case of strong identification. Our Monte Carlo experimentations suggest that the bootstrap plays an important role along both dimensions and represents a promising evaluation tool of the cross-equation restrictions and, under certain conditions, of the strength of identification. An empirical illustration based on a small-scale DSGE model estimated on U.S. quarterly observations shows the practical usefulness of our approach.

Forecasting-volatility models typically rely on either daily or high frequency (HF) data and the choice between these two categories is not obvious. In particular, the latter allows to treat volatility as observable but they suffer of many limitations. HF data feature microstructure problem, such as the discreteness of the data, the properties of the trading mechanism and the existence of bid-ask spread. Moreover, these data are not always available and, even if they are, the asset’s liquidity may be not sufficient to allow for frequent transactions. This paper considers different variants of these two family forecasting-volatility models, comparing their performance (in terms of Value at Risk, VaR) under the assumptions of jumping prices and leverage effects for volatility. Findings suggest that GARJI model provides more accurate VaR measures for the S&P 500 index than RV models. Furthermore, the assumption of conditional normality is shown to be not sufficient to obtain accurate risk measures even if jump contribution is provided. More sophisticated models might address this issue, improving VaR results.

Breaches of the security of personal data collected by firms are reported almost daily. Companies are under an increasing political pressure to notify individuals whose privacy as been breached. At the moment, we know virtually nothing about the behavioral impact of data breach notifications. We present the results of an experimental study designed to investigate how breach notifications change the individual’s propensity to provide sensitive personal information to firms. In contrast to the theory (where breach notifications have no behavioral effect), our main result shows that notifications induce a sub-group of individuals to disclose less information to a firm, i.e. those with personally sensitive information.

A specific concept of structural model is used as a background for discussing the structurality of its parameterization. Conditions for a structural model to be also causal are examined. Difficulties and pitfalls arising from the parameterization are analyzed. In particu- lar, pitfalls when considering alternative parameterizations of a same model are shown to have lead to ungrounded conclusions in the lit- erature. Discussion of observationally equivalent models related to different economic mechanisms are used to make clear the connection between an economicall meaningful parameterization and an econom- ically meaningful decomposition of a complex model. The design of economic policy is used for drawing some practical implications of the proposed analysis.

The main purpose of this paper is to develop generalized 'State Dependent Models' (SDM) in a multivariate framework for empirical analysis. This significantly extends the existing SDM which only allow univariate analysis following a simple AR process. The extended model enables greater possibility for empirical analysis of economic relationships. The principle advantage of SDM is that it allows for a general form of non-linearity and can be fitted without any specific prior assumption about the form of non-linearity. We describe the general structure of the SDM and the problem of its identification is also considered. Finally, we apply the algorithm to show the impact of sentiment and income when modelling US consumption.

It is well known that with a parameter on the boundary of the parameter space, such as in the classic cases of testing for a zero location parameter or no autoregressive conditional heteroskedasticity (ARCH) effects, the classic nonparametric bootstrap – based on unrestricted parameter estimates – leads to inconsistent testing. In contrast, we show here that for the two aforementioned cases, a nonparametric bootstrap test based on parameter estimates obtained under the null – referred to as ‘restricted bootstrap’ – is indeed consistent. While the restricted bootstrap is simple to implement in practice, novel theoretical arguments are required in order to establish consistency. In particular, since the bootstrap is analysed both under the null hypothesis and under the alternative, non-standard asymptotic expansions are required to deal with parameters on the boundary. Detailed proofs of the asymptotic validity of the restricted bootstrap are given and, for the leading case of testing for no ARCH, a Monte Carlo study demonstrates that the bootstrap quasi-likelihood ratio statistic performs extremely well in terms of empirical size and power for even remarkably small samples, outperforming the standard and bootstrap Lagrange multiplier tests as well as the asymptotic quasi-likelihood ratio test.