A medium-N approach to macroeconomic forecasting

This paper considers methods for forecasting macroeconomic time series in a framework where the number of predictors, N, is too large to apply traditional regression models but not sufficiently large to resort to statistical inference based on double asymptotics. Our interest is motivated by a body of empirical research suggesting that popular data-rich prediction methods perform best when N ranges from 20 to 40. In order to accomplish our goal, we resort to partial least squares and principal component regression to consistently estimate a stable dynamic regression model with many predictors as only the number of observations, T, diverges. We show both by simulations and empirical applications that the considered methods, especially partial least squares, compare well to models that are widely used in macroeconomic forecasting

A general to specific approach for constructing composite business cycle indicators

Combining economic time series with the aim to obtain an indicator for business cycle analyses is an important issue for policy makers. In this area, econometric techniques usually rely on systems with either a small number of series, N, or, at the other extreme, a very large N. In this paper we propose tools to select the relevant business cycle indicators in a â mediumâ N framework, a situation that is likely to be the most frequent in empirical works. An example is provided by our empirical application, in which we study jointly the short-run co-movements of 24 European countries. We show, under not too restrictive conditions, that parsimonious single-equation models can be used to split a set of N countries in three groups. The first group comprises countries that share a synchronous common cycle, a non-synchronous common cycle is present among the countries of the second group, and the third group collects countries that exhibit idiosyncratic cycles. Moreover, we offer a method for constructing a composite coincident indicator that explicitly takes into account the existence of these various forms of short-run co-movements among variables

Forecasting consumer confidence through semantic network analysis of online news

This research studies the impact of online news on social and economic consumer perceptions through semantic network analysis. Using over 1.8 million online articles on Italian media covering four years, we calculate the semantic importance of specific economic-related keywords to see if words appearing in the articles could anticipate consumers' judgments about the economic situation and the Consumer Confidence Index. We use an innovative approach to analyze big textual data, combining methods and tools of text mining and social network analysis. Results show a strong predictive power for the judgments about the current households and national situation. Our indicator offers a complementary approach to estimating consumer confidence, lessening the limitations of traditional survey-based methods

Remittances and Asset Accumulation in Bangladesh: A Study using Generalized Propensity Score

Drawing on a sample of households in Bangladesh, we utilize the Generalized Propensity Score (GPS) method to investigate the impact of internal and international remittances on households’ net assets. The analysis suggests an inverted U-shaped relationship between the amount of internal remittances and the net assets of households. The effect of international remittances on net assets is similarly inverted U-shaped. The paper also indicates not only the source but also the size of remittances effects the utilization of remittances for asset accumulation

Representation, estimation and forecasting of the multivariate index-augmented autoregressive model

We examine the conditions under which each individual series that is generated by a vector autoregressive model can be represented as an autoregressive model that is augmented with the lags of a few linear combinations of all the variables in the system. We call this multivariate index-augmented autoregression (MIAAR) modelling. We show that the parameters of the MIAAR can be estimated by a switching algorithm that increases the Gaussian likelihood at each iteration. Since maximum likelihood estimation may perform poorly when the number of parameters increases, we propose a regularized version of our algorithm for handling a medium–large number of time series. We illustrate the usefulness of the MIAAR modelling by both empirical applications and simulations

A Vector Heterogeneous Autoregressive Index model for realized volatility measures

This paper introduces a new modelling for detecting the presence of commonalities in a set of realized volatility measures. In particular, we propose a multivariate generalization of the heterogeneous autoregressive model (HAR) that is endowed with a common index structure. The Vector Heterogeneous Autoregressive Index model has the property to generate a common index that preserves the same temporal cascade structure as in the HAR model, a feature that is not shared by other aggregation methods (e.g., principal components). The parameters of this model can be easily estimated by a proper switching algorithm that increases the Gaussian likelihood at each step. We illustrate our approach with an empirical analysis aiming at combining several realized volatility measures of the same equity index for three different markets

Indirect estimation of the monthly transport turnover indicator in Italy

The paper discusses the results of a selection of a set of monthly indicators to be used as predictors of the quarterly index of Italian service turnover. A mixed frequency approach based on sparse temporal disaggregation is used, which outperforms the classical methods of the Chow and Lin family, allowing both a high number of regressors by the LASSO method and stable estimates. The application refers to the turnover in transport, a sector strongly affected in 2020 by the dramatic movements due to the COVID-19 pandemic and the resurgence of inflation at the end of 2021. The monthly indicators are selected from 143 time series: 56 series of business surveys in transport about both the climate and frequency of the answers; 18 series from Assaeroporti about both passengers and cargo flights split by national and international routes; 69 series of monthly turnover in industry split by both sector of economic activity and reference market. The sample spans the months from January 2010 to December 2021 for both seasonally adjusted and unadjusted data. Several aspects of the estimation are considered: the stability of selected indicators over the quarters 2017–2021; their forecasting performance; the reliability of the estimates in terms of their monthly pattern