Essays on macroeconometrics and short-term forecasting

is composed of three chapters. The first two chapters are on nowcasting,

a topic that has received an increasing attention both among practitioners and

the academics especially in conjunction and in the aftermath of the 2008-2009

economic crisis. At the heart of the two chapters is the idea of exploiting the

information from data published at a higher frequency for obtaining early estimates

of the macroeconomic variable of interest. The models used to compute

the nowcasts are dynamic models conceived for handling in an efficient way

the characteristics of the data used in a real-time context, like the fact that due to the different frequencies and the non-synchronicity of the releases

the time series have in general missing data at the end of the sample. While

the first chapter uses a small model like a VAR for nowcasting Italian GDP,

the second one makes use of a dynamic factor model, more suitable to handle

medium-large data sets, for providing early estimates of the employment in

the euro area. The third chapter develops a topic only marginally touched

by the second chapter, i.e. the estimation of dynamic factor models on data characterized by block-structures.

The firrst chapter assesses the accuracy of the Italian GDP nowcasts based

on a small information set consisting of GDP itself, the industrial production

index and the Economic Sentiment Indicator. The task is carried out by using

real-time vintages of data in an out-of-sample exercise over rolling windows

of data. Beside using real-time data, the real-time setting of the exercise is

also guaranteed by updating the nowcasts according to the historical release calendar. The model used to compute the nowcasts is a mixed-frequency Vector

Autoregressive (VAR) model, cast in state-space form and estimated by

maximum likelihood. The results show that the model can provide quite accurate

early estimates of the Italian GDP growth rates not only with respect

to a naive benchmark but also with respect to a bridge model based on the

same information set and a mixed-frequency VAR with only GDP and the industrial production index.

The chapter also analyzes with some attention the role of the Economic Sentiment

Indicator, and of soft information in general. The comparison of our

mixed-frequency VAR with one with only GDP and the industrial production

index clearly shows that using soft information helps obtaining more accurate

early estimates. Evidence is also found that the advantage from using soft

information goes beyond its timeliness.

In the second chapter we focus on nowcasting the quarterly national account

employment of the euro area making use of both country-specific and

area wide information. The relevance of anticipating Eurostat estimates of

employment rests on the fact that, despite it represents an important macroeconomic

variable, euro area employment is measured at a relatively low frequency

(quarterly) and published with a considerable delay (approximately

two months and a half). Obtaining an early estimate of this variable is possible

thanks to the fact that several Member States publish employment data and

employment-related statistics in advance with respect to the Eurostat release

of the euro area employment. Data availability represents, nevertheless, a

major limit as country-level time series are in general non homogeneous, have

different starting periods and, in some cases, are very short. We construct a

data set of monthly and quarterly time series consisting of both aggregate and

country-level data on Quarterly National Account employment, employment

expectations from business surveys and Labour Force Survey employment and

unemployment. In order to perform a real time out-of-sample exercise simulating

the (pseudo) real-time availability of the data, we construct an artificial

calendar of data releases based on the effective calendar observed during the first quarter of 2012. The model used to compute the nowcasts is a dynamic

factor model allowing for mixed-frequency data, missing data at the beginning

of the sample and ragged edges typical of non synchronous data releases. Our

results show that using country-specific information as soon as it is available

allows to obtain reasonably accurate estimates of the employment of the euro

area about fifteen days before the end of the quarter.

We also look at the nowcasts of employment of the four largest Member

States. We find that (with the exception of France) augmenting the dynamic

factor model with country-specific factors provides better results than those

obtained with the model without country-specific factors.

The third chapter of the thesis deals with dynamic factor models on data

characterized by local cross-correlation due to the presence of block-structures.

The latter is modeled by introducing block-specific factors, i.e. factors that

are specific to blocks of time series. We propose an algorithm to estimate the model by (quasi) maximum likelihood and use it to run Monte Carlo

simulations to evaluate the effects of modeling or not the block-structure on

the estimates of common factors. We find two main results: first, that in finite samples modeling the block-structure, beside being interesting per se, can help

reducing the model miss-specification and getting more accurate estimates

of the common factors; second, that imposing a wrong block-structure or

imposing a block-structure when it is not present does not have negative

effects on the estimates of the common factors. These two results allow us

to conclude that it is always recommendable to model the block-structure

especially if the characteristics of the data suggest that there is one.