Leading Indicators for Euro‐area Inflation and GDP Growth*

In this paper, we evaluate the role of a set of variables as leading indicators for Euro‐area inflation and GDP growth. Our leading indicators are taken from the variables in the European Central Bank's (ECB) Euro‐area‐wide model database, plus a set of similar variables for the US. We compare the forecasting performance of each indicator ex post with that of purely autoregressive models. We also analyse three different approaches to combining the information from several indicators. First, ex post, we discuss the use as indicators of the estimated factors from a dynamic factor model for all the indicators. Secondly, within an ex ante framework, an automated model selection procedure is applied to models with a large set of indicators. No future information is used, future values of the regressors are forecast, and the choice of the indicators is based on their past forecasting records. Finally, we consider the forecasting performance of groups of indicators and factors and methods of pooling the ex ante single‐indicator or factor‐based forecasts. Some sensitivity analyses are also undertaken for different forecasting horizons and weighting schemes of forecasts to assess the robustness of the results.     

THE ESTIMATION OF HUMAN CAPITAL BY ADMINISTRATIVE ARCHIVES IN A STATIC AND LONGITUDINAL PERSPECTIVE: THE CASE OF MILAN

Abstract This paper focuses on the estimation of the latent variable human capital (HC) at disaggregated level (worker) by available routinely institutional data flows. In particular we utilize the Lombardy region administrative archive ‘Employment Centers of the Province of Milan’, collecting information about careers of workers in the private sector of the Milan area, and administrative flows collecting mandatory workers' individual income tax returns, filed with the National Internal Revenue Service. First, we propose and empirically estimate HC scores in a static (referred to 2004) framework, by means of a realistic measurement model within causal relationships among endogenous and exogenous (investment) HC indicators. Furthermore, the model also specifies a set of (concomitant) indicators that, not belonging to HC investment indicators, have causal impact on endogenous variables and on HC scores, too. Second, we propose a longitudinal analysis (period 2000–2004) aimed to investigate how workers' earned income growth rates vary over workers' educational levels and other personal characteristics. The empirical results of both analyses confirm the characteristics of the Italian job market, denoted by marked inequalities, and knowledge regarding the process of school to work transition, characterized by a weak incidence of education on longitudinal trajectories of earned income.     

Short-term forecasting of GDP with a DSGE model augmented by monthly indicators

DSGE models are useful tools for evaluating the impact of policy changes, but their use for (short-term) forecasting is still in its infancy. Besides theory-based restrictions, the timeliness of data is an important issue. Since DSGE models are based on quarterly data, they suffer from the publication lag of quarterly national accounts. In this paper we present a framework for the short-term forecasting of GDP based on a medium-scale DSGE model for a small open economy within a currency area. We utilize the information available in monthly indicators based on the approach proposed by Giannone et al. (2009). Using Austrian data, we find that the forecasting performance of the DSGE model can be improved considerably by incorporating monthly indicators, while still maintaining the story-telling capability of the model.     

Nowcasting and forecasting global financial sector stress and credit market dislocation

We introduce a new international model for the systematic distress risk of financial institutions from the US, the European Union, and the Asia-Pacific region. Our proposed dynamic factor model can be represented as a nonlinear, non-Gaussian state space model with parameters that we estimate using Monte Carlo maximum likelihood methods. We construct measures of global financial sector risk and of credit market dislocation, where credit market dislocation is defined as a significant and persistent decoupling of the credit risk cycle from macro-financial fundamentals in one or more regions. We show that, in the past, such decoupling has preceded episodes of systemic financial distress. Our new measure provides a risk-based indicator of credit conditions, and as such, complements earlier quantity-based indicators from the literature. In an extensive comparison with such quantity-based systemic risk indicators, we find that the behaviour of the new indicator is competitive with that of the best quantity-based indicators.     

Extracting Nonlinear Signals from Several Economic Indicators

We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully nonlinear multivariate specification (one‐step approach) with the ‘shortcut’ of using a linear factor model to obtain a coincident indicator, which is then used to compute the Markov switching probabilities (two‐step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, especially in the vicinity of turning points, although its gains diminish as the quality of the indicators increases. Additionally, we also obtain decreasing returns of adding more indicators with similar signal‐to‐noise ratios. Using the four constituent series of the Stock–Watson coincident index, we illustrate these results for US data. Copyright © 2014 John Wiley & Sons, Ltd.     

Intra- and inter-group composite indicators using the BoD model

We extent the radial DEA model with a single constant input, often referred to as the Benefit-of-the-Doubt (BoD) model, to account for environmental or contextual differences. Based on this we estimate three types of composite indicators, namely the pooled that uses the entire sample of the evaluated units, the intra-group that accounts for within group differences, and the inter-group that reflects between group differences. In addition, we provide theoretically consistent aggregation rules for these types of composite indicators. We applied the proposed methodology to analyze the financial performance of public and private hospitals in Greece by means of financial ratios referring to their liquidity performance and to examine the extent of differences in the inter-group composite indicators between the two types of ownership both at the individual and the aggregate level.     

A composite indicator of social inclusion for EU based on the inverted BoD model

In this paper, after reviewing the existing modeling approaches for reverse indicators, we opt for the use of the inverted Benefit-of-the-doubt (IBoD) model when all the considered indicators are reverse, as is the case with composite indicators related to social inclusion. Using EU data for 2014 we provide comparative empirical results using the IBoD model and three data transformations employed by previous studies. We also provide a thorough analysis of the performance of EU countries during the period 2011–2020 in terms of the composite indicator obtained from the IBoD model. Our empirical results indicate that relative performance across the EU has neither improved nor deteriorated and there is evidence supporting local instead of global convergence in social inclusion with certain groups of countries.     

Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering

This paper analyzes patterns in the earnings development of young labor market entrants over their life cycle. We identify four distinctly different types of transition patterns between discrete earnings states in a large administrative dataset. Further, we investigate the effects of labor market conditions at the time of entry on the probability of belonging to each transition type. To estimate our statistical model we use a model‐based clustering approach. The statistical challenge in our application comes from the difficulty in extending distance‐based clustering approaches to the problem of identifying groups of similar time series in a panel of discrete‐valued time series. We use Markov chain clustering, which is an approach for clustering discrete‐valued time series obtained by observing a categorical variable with several states. This method is based on finite mixtures of first‐order time‐homogeneous Markov chain models. In order to analyze group membership we present an extension to this approach by formulating a probabilistic model for the latent group indicators within the Bayesian classification rule using a multinomial logit model. Copyright © 2011 John Wiley & Sons, Ltd.     

Regression Models with Data‐based Indicator Variables*

Ordinary least squares estimation of an impulse‐indicator coefficient is inconsistent, but its variance can be consistently estimated. Although the ratio of the inconsistent estimator to its standard error has a t‐distribution, that test is inconsistent: one solution is to form an index of indicators. We provide Monte Carlo evidence that including a plethora of indicators need not distort model selection, permitting the use of many dummies in a general‐to‐specific framework. Although White's (1980) heteroskedasticity test is incorrectly sized in that context, we suggest an easy alteration. Finally, a possible modification to impulse ‘intercept corrections’ is considered.     

Systemic financial risk indicators and securitised assets: an agent-based framework

The paper presents an agent-based model of a credit economy which includes a securitisation process and a bailout mechanism for bank bankruptcies. Within this framework, banks are able to sell mortgages to a financial vehicle corporation, which finances its activity by creating mortgage-backed securities and selling them to a mutual fund. In turn, the mutual fund collects liquidity by selling shares to households and remunerates them with a monthly interest. The impact of this mechanism is analysed by means of computational experiments for different levels of banks’ securitisation propensity. Furthermore, we study a set of systemic risk indicators which have the aim of assessing the imbalances in the financial system. Two of them are the mortgage-to-GDP ratio and the capital adequacy ratio, which are constructed to detect only the on-balance sheet changes in banks’ credit exposure. We consider two additional indicators, similar to the previous ones with the only difference that they are also able to account for the off-balance sheet items. Moreover, we adopt an indicator, the so-called “virtuous–unvirtuous cycle” indicator, which, besides off-balance assets, targets also the GDP. The results show that higher securitisation propensity weakens the financial stability of banks with relevant effects on different sectors of the economy. Most importantly, the analysis of systemic risk reveals the important issue of designing suitable systemic risk indicators for predicting incoming financial crises, finding that an essential feature of these indicators should be to integrate banks’ off-balance sheet assets.

     

SUSTAINABILITY AND WELLBEING: A SCIENTOMETRIC AND BIBLIOMETRIC REVIEW OF THE LITERATURE

Interdisciplinary research on measuring the progress towards sustainability and well-being (SaW) from different perspectives and in various contexts has developed dramatically over recent decades. This growth in the literature has not only added an enormous number of dimensions to the SaW debate, but the sheer scale of the expansion has challenged researchers to be able to conduct a comprehensive analysis of the available SaW indicators. In this work, we have proposed a hybrid method comprised of sophisticated scientometric analysis to summarise scientific developments in the massive text corpus of the SaW literature in conjunction with a more traditional literature review to categorise the ‘fuzzy’ details that remain. Scientometric analysis highlights that the developed OECD countries play a vital role in the development and applications of SaW indicators and we describe key developments in this regard via a range of graphical approaches. Using an extensive collection of existing SaW indicators, the analysis is then summarised in a matrix of ranked indicators which serve as a powerful tool to compare, contrast, filter and select indicators for SaW assessment with minimum redundancies between indicators. The approach undertaken in this study is intended to be flexible and can be extended and applied to other fields of research.     

Constructing a generalized model of the human development index

The human development index (HDI) has been a well-known yardstick of the United Nations Development Programme (UNDP) to measure human development (HD) from a multi-dimensional perspective, since its introduction in the first Human Development Report (HDR) in 1990. Although there have been several modifications in the computation of this index, we suggest a new perspective in a multi-criteria decision-making framework in this paper. The new methodology addresses some of the limitations of the existing approach without violating the guidelines of the UNDP. As a first step, we revisit the concept of the HDI and then integrate the average shortfall factor with the HDI for the construction of GHDI--a generalized model of the HDI. Then we propose GHDI^R--a relative measure of the GHDI relative to a benchmark HD. This index is year-specific, and we determine it based on the goals of the human development indicators as fixed by the UNDP as well as the individual performance of all the countries under consideration for the year concerned. For this purpose, we employ a multi-criteria decision-making model using a two-step methodology. Initially, we develop a multi-objective model to create the benchmark HD. Next, we assess the GHDI^R with a multi-index ranking model. We implement these procedures for the countries based on their empirical data on human development indicators as presented in the HDR (2014) and compare the HDI with the GHDI and GHDI^R.     

PREDICTING NEGATIVE EFFECTS OF THE SECOND INTIFADA: AN EX-POST EVALUATION OF SOME MODELS

In 2003, the World Bank (WB), the International Monetary Fund (IMF) and de Boer and Missaglia (DBM) constructed models for the estimation of the 2002 macro-economic indicators of the economy of Palestine. In 2007, IMF and WB provided the consensus estimates of these figures using data that are more up-to-date and more complete than those available in 2003. This note proposes an ex-post evaluation of the predictive performance of the models of WB, DBM and IMF. A comparison of the models of WB and DBM, which are both micro-founded computable general equilibrium models using the same data, reveals that DBM strongly outperforms WB. We argue that the shortening of the time horizon and the quantity adjustment following the dramatic shock explain why our model performs much better. A comparison of DBM with IMF (a simple macro-founded income-expenditure model) also shows that our model performs better.     

基于注意力机制的LSTM股价趋势预测研究

针对中国股票市场,提出了一种基于注意力机制的LSTM股价趋势预测模型。选取42只中国上证50从2009年到2017年的股票数据为实验对象,根据股票市场普遍认可的经验规则,分别对每个技术指标进行量化处理得到股票涨跌的趋势数据,并和交易数据混合作为预测模型的输入,然后使用基于注意力机制的LSTM模型提取股价趋势特征进行预测。实验结果表明:引入股票离散型趋势数据到预测模型中,能够在已有交易数据和技术指标的基础上提升预测精确度,与传统的机器学习模型SVM和单一的LSTM模型相比,基于注意力机制的LSTM模型具有更好的预测能力。     

Forecasting the New York State economy: The coincident and leading indicators approach

Currently there are no reliable summary indicators of the economic and fiscal condition of states and localities. This deficiency has hampered the efforts of policy makers at the sub-national level to monitor changes in the economic environment and predict how those changes will impact the fiscal health of governments. This paper attempts to fill this analytical vacuum by providing summary indicators of economic and fiscal health for New York State. The models developed are based on the single-index methodology developed by Stock and Watson [(1991). A probability model of the coincident economic indicators. In K. Lahiri and G. H. Moore (eds.), Leading economic indicators: new approaches and forecasting records (pp. 63–85). New York: Cambridge University Press]. This approach allows us to date New York business cycles and compare local cyclical behavior with the nation as a whole. We develop a leading index of economic indicators which predicts future movements in the coincident indicator. The Stock and Watson approach is used to create a fiscal indicator which acts as a summary indicator of revenue performance for New York. In addition, we explore the ability of our economic indicator series to predict future changes in state revenues. We find that changes in the leading indicator series have significant predictive power in forecasting changes in our revenue index.     

MULTIVARIATE VOLATILITY MODELING OF ELECTRICITY FUTURES

We model the dynamic volatility and correlation structure of electricity futures of the European Energy Exchange index. We use a new multiplicative dynamic conditional correlation (mDCC) model to separate long‐run from short‐run components. We allow for smooth changes in the unconditional volatilities and correlations through a multiplicative component that we estimate nonparametrically. For the short‐run dynamics, we use a GJR‐GARCH model for the conditional variances and augmented DCC models for the conditional correlations. We also introduce exogenous variables to account for congestion and delivery date effects in short‐term conditional variances. We find different correlation dynamics for long‐ and short‐term contracts and the new model achieves higher forecasting performance compared \to a standard DCC model. Copyright © 2012 John Wiley & Sons, Ltd.     

A UNIFIED PRESENTATION OF COMPETITION ANALYSIS IN TWO-SIDED MARKETS

The goal of this paper is to present quantitative tools to manage competition policy analysis in two-sided platforms, based on a common framework for transaction and non-transaction platforms. We explore tools for relevant market definition [Critical Loss Analysis and a small but significant non-transitory increase in price (SSNIP) test], unilateral effects indicators [Upward Pricing Pressure (UPP) and Gross Upward Pricing Pressure Index (GUPPI)] and tests for exclusionary practices. We review dispersed results in the literature and fill the gaps where appropriate. We highlight the required changes from the usual one-sided market framework and tools. While discussions of antitrust tools can be found in specialized forums devoted to advances in competition policy analysis, we organize the material in an integrated framework.     

Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence

Policymakers, firms, and investors closely monitor traditional survey-based consumer confidence indicators and treat them as an important piece of economic information. To obtain a daily nowcast of monthly consumer confidence, we introduce a latent factor model for the vector of monthly survey-based consumer confidence and daily sentiment embedded in economic media news articles. The proposed mixed-frequency dynamic factor model uses a Toeplitz correlation matrix to account for the serial correlation in the high-frequency sentiment measurement errors. We find significant accuracy gains in nowcasting survey-based Belgian consumer confidence with economic media news sentiment.     

Unemployment and liquidity constraints

We present a dynamic framework for the interaction between borrowing (liquidity) constraints and deviations of actual hours from desired hours, both measured by discrete‐valued indicators, and estimate it as a system of dynamic binary and ordered probit models with panel data from the Panel Study of Income Dynamics. We analyze a household's propensity to be liquidity constrained by means of a dynamic binary probit model. We analyze qualitative aspects of the conditions of employment, namely whether the household head is involuntarily overemployed, voluntarily employed, or involuntarily underemployed or unemployed, by means of a dynamic ordered probit model. We focus on the possible interaction between the two types of constraints. We estimate these models jointly using maximum simulated likelihood, where we allow for individual random effects along with an autoregressive process for the general error term in each equation. A novel feature of our method is that it allows for the random effects to be correlated with regressors in a time‐invariant fashion. Our results provide strong support for the basic theory of constrained behavior and the interaction between liquidity constraints and exogenous constraints on labor supply. Copyright © 2007 John Wiley & Sons, Ltd.     

Comparing levels of anti-Muslim attitudes across Western countries

In this study we describe differences of the ethnic majorities?? attitudes toward Muslims across Western countries. Using data from the Pew Global Attitudes Survey (2005), we were able to increase and test cross-cultural comparability of anti-Muslim attitudes. We constructed a single factor CFA model with three indicators, which was tested for scalar equivalence. Our results indicate that anti-Muslim attitudes differ significantly across the countries in our analysis. Germany and the Netherlands turned out to display relatively high levels of anti-Muslim attitudes, whereas these levels were relatively low in Great-Britain and the USA. We conclude our study with giving some post-hoc explanations for the differences found across countries.