Empirical Macroeconomics and Statistical Uncertainty: Spatial and Temporal Disaggregation of Regional Economic Indicators

121 Pages·2020·14.16 MB·English

by  Mateusz Pipień, Sylwia Roszkowska

Empirical Macroeconomics

and Statistical Uncertainty
This book addresses one of the most important research activities in empir-
ical macroeconomics. It provides a course of advanced but intuitive methods
and tools enabling the spatial and temporal disaggregation of basic macro-
economic variables and the assessment of the statistical uncertainty of the
outcomes of disaggregation.
The empirical analysis focuses mainly on GDP and its growth in the con-
text of Poland. However, all of the methods discussed can be easily applied
to other countries. The approach used in the book views spatial and temporal
disaggregation as a special case of the estimation of missing observations (a
topic on missing data analysis). The book presents an econometric course
of models of Seemingly Unrelated Regression Equations (SURE). The main
advantage of using the SURE specification is to tackle the presented research
problem so that it allows for the heterogeneity of the parameters describing
relations between macroeconomic indicators. The book contains model spe-
cification, as well as descriptions of stochastic assumptions and resulting
procedures of estimation and testing. The method also addresses uncertainty
in the estimates produced. All of the necessary tests and assumptions are
presented in detail. The results are designed to serve as a source of invaluable
information making regional analyses more convenient and – more import-
antly – comparable. It will create a solid basis for making conclusions and
recommendations concerning regional economic policy in Poland, particu-
larly regarding the assessment of the economic situation.
This is essential reading for academics, researchers, and economists with
regional analysis as their field of expertise, as well as central bankers and
policymakers.
Mateusz Pipień is an associate professor in the Department of Empirical
Analyses of Economic Stability, Cracow University of Economics, Poland.
Sylwia Roszkowska is an assistant professor in the Department of
Macroeconomics, University of Lodz, Poland.
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58 Empirical Macroeconomics and Statistical Uncertainty
Spatial and Temporal Disaggregation of Regional Economic Indicators
Mateusz Pipień and Sylwia Roszkowska
For more information about this series, please visit
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Empirical Macroeconomics

and Statistical Uncertainty
Spatial and Temporal Disaggregation
of Regional Economic Indicators
Mateusz Pipień and Sylwia Roszkowska
First published 2021
by Routledge
2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN
and by Routledge
52 Vanderbilt Avenue, New York, NY 10017
Routledge is an imprint of the Taylor & Francis Group, an informa business
© 2021 Mateusz Pipień and Sylwia Roszkowska
The right of Mateusz Pipień and Sylwia Roszkowska to be identified
as authors of this work has been asserted by them in accordance with
sections 77 and 78 of the Copyright, Designs and Patents Act 1988.
All rights reserved. No part of this book may be reprinted or
reproduced or utilised in any form or by any electronic, mechanical,
or other means, now known or hereafter invented, including
photocopying and recording, or in any information storage or retrieval
system, without permission in writing from the publishers.
Trademark notice: Product or corporate names may be trademarks
or registered trademarks, and are used only for identification and
explanation without intent to infringe.
British Library Cataloguing-i n- Publication Data
A catalogue record for this book is available from the British Library
Library of Congress Cataloging-i n- Publication Data
Names: Pipień, Mateusz, author. | Roszkowska, Sylwia, author.
Title: Empirical macroeconomics and statistical uncertainty:
spatial and temporal disaggregation of regional economic
indicators / Mateusz Pipień and Sylwia Roszkowska.
Description: Abingdon, Oxon; New York, NY: Routledge, 2021. |
Series: Routledge studies in the European economy |
Includes bibliographical references and index.
Subjects: LCSH: Regional economics–Mathematical models. |
Economic indicators–Mathematical models. |
Macroeconomics–Mathematical models.
Classification: LCC HT388 .P56 2021 (print) |
LCC HT388 (ebook) | DDC 338.9001/5118–dc23
LC record available at https://lccn.loc.gov/2020013906
LC ebook record available at https://lccn.loc.gov/2020013907
ISBN: 978- 0- 367- 45671- 9 (hbk)
ISBN: 978- 1- 003- 02471- 2 (ebk)
Typeset in Times New Roman MT Std
by Newgen Publishing UK
Contents

List of figures vi

List of tables viii

1 Introduction 1

2 Importance of regional data for policy evaluation 4

3 A review of official statistics describing economic
conditions in NUTS- 2 regions in Poland 13

4 Basic properties of the model of Seemingly Unrelated
Regression Equations 26

5 NUTS- 2 disaggregation of the Polish GDP: preliminary
analyses within SURE 35
diag
6 NUTS- 2 disaggregation of the Polish GDP: including
other explanatory variables 53

7 Concluding remarks 102

Bibliography 104

Index 109
Figures

3.1 NUTS-2 regions in Poland 14
3.2 GDP per inhabitant in 2017, current prices in euro 15
3.3 GDP per inhabitant as a percentage of Polish average in 2017 16
3.4 Employment structure by economic sectors in 2017 17
3.5 Value added by economic sectors in 2017 18
3.6 Shares of employment and value added by economic
sectors in Polish regions in 2017 19
3.7 Price dynamics in 2017 (2003 = 100) 20
3.8 Price dynamics in 2003– 2017 (2003 = 100) 21
3.9 Unemployment rate and long- term unemployed in 2017 22
3.10 Cyclical component of employment (CF filter) 23
5.1 Estimated values of quarterly regional GDP, obtained
on the basis of Equation (5.2) 39
5.2 Estimated values of the year- on- year rate of changes in
quarterly regional GDP, obtained on the basis of Equation
(5.5) together with 95% confidence intervals 44
5.3 Estimated values of the year- on- year rate of changes in
quarterly regional GDP, obtained on the basis of Equation
(5.7) together with 95% confidence intervals 48
6.1 Estimated values of logarithms of quarterly regional GDP
(per worker) and 95% confidence intervals; see Equation (6.4) 56
6.2 Estimated values of quarterly regional annual GDP (per
worker) logarithmic growth rates and 95% confidence
intervals; see Equation (6.6) 58
6.3 Statistically significant point estimates of parameters of
Equation (6.8) in the case of two alternative stochastic
structures M0 (abbreviation OLS) and M1 (abbreviation Zellner) 78
6.4 Estimated values of logarithms of quarterly regional GDP
per worker and 95% confidence intervals obtained in case of
model M0; see Equation (6.10) 79
6.5 Estimated values of quarterly regional annual GDP per
worker logarithmic growth rates and 95% confidence
intervals obtained in case of model M0; see Equation (6.14) 81
Figures vii
6.6 Estimated values of logarithms of quarterly regional GDP
per worker and 95% confidence intervals obtained in the case
of model M1; see Equation (6.12) 90
6.7 Estimated values of quarterly regional annual GDP per
worker logarithmic growth rates and 95% confidence
intervals obtained in case of model M1; see Equation (6.16) 92
6.8 Estimated contemporaneous correlations of the error term
obtained in the case of model M; see matrix R given by
1
Equation (4.6) 100
newgenprepdf
Tables
3.1 Main data sources for regional economic analysis 24
5.1 Estimated values of quarterly regional GDP, obtained on the
basis of Equation (5.2) 42
5.2 Estimated values of the year- on- year rate of changes in
quarterly region GDP, obtained on the basis of Equation (5.5) 46
5.3 Estimated values of the year- on- year rate of changes in
quarterly regional GDP, obtained on the basis of Equation (5.7) 50
6.1 Estimated values of quarterly regional GDP per worker;
see Equation (6.4) 60
6.2 Estimated values of quarterly regional annual GDP per
worker logarithmic growth rates; see Equation (6.6) 62
6.3 Results of estimation of parameters of Equation (6.8) in the
case of two alternative stochastic structures M0 and M1 70
6.4 Estimated values of logarithms of quarterly regional
GDP per worker obtained in the case of model M0 84
6.5 Estimated values of quarterly regional annual GDP per
worker logarithmic growth rates obtained in the case of model M0 86
6.6 Estimated values of logarithms of quarterly regional GDP per
worker obtained in the case of model M1 94
6.7 Estimated values of logarithms of quarterly regional annual
GDP logarithmic growth rates obtained in the case of model M1 96
1 I ntroduction

Research on the geographic diversity of macroeconomic categories is currently
one of the most important issues of empirical macroeconomics. Precise deter-
mination of the economic diversity of different areas of the country provides
more opportunities for analysing and understanding a given phenomenon.
Research on the nature of fluctuations in economic activity includes analyses
of spatial diversification, focusing on income, wages, and other variables. The
results from this stream of research may be crucial in assessing the impact
on fiscal policy (e.g., Hallett, 2017; Carniti et al., 2018), monetary policy
(e.g., Anagnostou & Gajewski, 2018; Lea, 2018), and macroprudential policy
(Rubio, 2014).
Spatial and temporal disaggregation methods for the time series of basic
macroeconomic variables have been intensively studied for over a half- century.
Disaggregation was one of the first fields of economic research where missing
data analysis was employed. In time, the search for methods enabling accurate
spatial and temporal estimation of macroeconomic variables developed into a
separate field of econometrics, because of their fundamental importance for
the statistical analysis of national accounts.
The first articles where annual data were converted into quarterly estimates
were written by Lisman and Sandee (1964), Boot, Feibes, and Lisman
(1967), Denton (1971). They presented a general approach and some spe-
cific procedures involving constrained minimisation of a quadratic form in
the differences between revised and unrevised series. Unfortunately, empirical
applications are omitted in these initial proposals.
Based on Autoregressive Integrated Moving Average (ARIMA) models
(Wei & Stram, 1986, 1990; Guerrero & Martínez, 1995) and factor models
(Angelini et al. 2006; Marcellino, 2007), the preliminary approaches were
developed into a dynamic method where higher frequency of the selected time
series of economic variables could be obtained based on sets of additional
variables and validating the numerical results estimated in practice.
Chow and Lin (1971) presented methods that utilise a generalized linear
regression model with a set of explanatory variables of the same frequency
as the studied one. Their approach served as a basis for the most popular
data interpolation approach that was often modified into a range of related