Index
ISBN: 978-1-80071-694-0, eISBN: 978-1-80071-693-3
ISSN: 0573-8555
Publication date: 1 June 2022
This content is currently only available as a PDF
Citation
(2022), "Index", Baltagi, B.H., Moscone, F. and Tosetti, E. (Ed.) The Economics of COVID-19 (Contributions to Economic Analysis, Vol. 296), Emerald Publishing Limited, Leeds, pp. 173-178. https://doi.org/10.1108/S0573-855520220000296016
Publisher
:Emerald Publishing Limited
Copyright © 2022 Badi H. Baltagi, Francesco Moscone and Elisa Tosetti. Published under exclusive licence by Emerald Publishing Limited
INDEX
Approximate Bayesian Computation (ABC)
, 107
Augmented Dickey-Fuller (ADF) procedure
, 5
Autoregressive models with exogenous variables (ARX)
conditional forecasts
, 40–41, 50–52
factor model (FM) approach
, 17, 20, 32
Black, Asian and minority ethnic (BAME) groups
, 132
Business intelligence
, 98–99
Census Household Pulse Survey
, 128
Chicago Fed National Financial Conditions Index (NFCI)
, 73–74
Common mental disorder (CMD)
, 126
Comovement, financial markets
asymmetric correlations
, 77
extreme value theory (EVT)
, 78–79
linear setting
, 76
principal components analysis
, 79–80
Computable General Equilibrium (CGE) models
consumption patterns
, 59
demand- and supply-side shocks
, 62–63
demand-side effects
, 60
economic impact
, 59
input-output models
, 57–58
labour market
, 61–62
labour productivity
, 62
macroeconomic analysis
, 57–58
neo-Keynesian model
, 64
parameters re-calibration
, 60
SAM-multiplier models
, 64
structural change
, 58
taxonomy of
, 65–66, 68
time-dependent
, 58–59
Walrasian frameworks
, 58
web-based communication services
, 59
World Trade Organisation (WTO)
, 62–63
Conditional forecasts
, 53–55
autoregressive models with exogenous variables (ARX)
, 40–41, 50–52
downside scenario
, 40–41, 44, 47
exogenous variables
, 30–31
gross domestic product (GDP)
, 24, 28, 30, 32
intermediate pandemic scenario
, 28, 30, 40–42, 45, 48
macroeconomic variables
, 24, 27, 29, 31
pandemic scenario
, 28, 30
unemployment rate
, 24, 28, 30, 32
upside pandemic scenario
, 28, 40–41, 43, 46, 49
Contact-tracing strategy
, 113
Covariance matrix
, 15
COVID-19 Government Response Tracker
, 7
Cross-sectional asset pricing, financial markets
empirical analysis
, 80
linear setting
, 76
Cyber security
, 101–102
Data privacy
, 101–102
Differences-in-differences (DiD) analysis
, 137
Digital economy
advantages
, 96–97
AI-based technologies
, 101
consumer behaviour
, 97–98
cyber security
, 101–102
data privacy
, 101–102
decision-making process
, 98
digital businesses
, 95–96
digital demand
, 97–98
digital globalisation
, 99–100
digital supply and productivity
, 98–99
ethical concerns
, 101
global disruption
, 96
industrial revolution
, 101
market structure
, 100–101
product selection
, 98
remote work policies
, 100–101
risk assessment strategies
, 101–102
social activities
, 95–96
social distancing policies
, 100–101
World Health Organisation (WHO)
, 95–96
Digital globalisation
, 99–100
Distributed Lag Model
, 108
Econometric models
, 75
factor models
, 6–7
vector autoregressive model with exogenous variables (VARX)
, 7–8
Empirical analysis, financial markets
comovement
, 77–80
cross-sectional asset pricing
, 80
out-of-sample forecasting
, 81–82
Epidemic models. See Uncertainty estimation, Italy
Ethnic inequality, United Kingdom
digital services
, 155
Minority Ethnic Groups (MEGs). See also Minority Ethnic Groups (MEGs)
, 144
UK-Biobank and UK Labour Force Survey (UKLFS)
, 144
UK Household Longitudinal Study (UKHLS)
, 144
vaccination campaign
, 152–155
European Centre for Disease Prevention and Control (ECDC)
, 3
Extreme value theory (EVT)
, 78–79
Factor model (FM) approach
2008-crisis samples
, 9–10
augmented Dickey-Fuller (ADF) procedure
, 5
autoregressive models with exogenous variables (ARX)
, 17, 20, 32
‘business as usual’ recovery strategies
, 1–2
conditional forecasts. See also Conditional forecasts
, 24
covariance matrix
, 15
econometric modelling
, 6–8
eigenvalue gaps over time
, 16
eigenvalues distribution
, 9, 12, 15
European Centre for Disease Prevention and Control (ECDC)
, 3
forecasting error bands
, 18–21
Gershgorin’s circles
, 15–17, 36–37
labour market
, 2
macroeconomic variables
, 3–5
Oxford COVID-19 Government Response Tracker (OxCGRT)
, 5
post-COVID-19
, 2
public expenditure index (PE). See also Public expenditure index (PE)
, 21
rolling window estimation approach
, 8–9, 38–40
Root Mean Square Error (RMSE)
, 17–18, 20, 22
sequential factor estimation
, 9, 11–12, 14
socio-economic resilience
, 1–2
tracking error (TE)
, 8–9, 17
US and EU economies
, 2–3
US employment
, 9, 11
variance
, 9, 11–14, 36, 38
Financial markets
and COVID-19 pandemic
, 72–73
economic indicators
, 71–72, 74
empirical analysis
, 77–82
empirical literature
, 72, 74–75
linear setting
, 76–77
nonlinear dynamics
, 75–76
time series
, 72
Forecasting error bands
, 18–21
Gender inequality
, 130
General Anxiety Disorder 7-item scale (GAD-7)
, 137
12-item General Health Questionnaire (GHQ)
, 135–136, 146
Gershgorin’s circles
, 15–17, 36–37
rolling window size
, 38, 40
Gross Domestic Product (GDP)
, 3, 17
conditional forecasts
, 24, 28, 30, 32
seismology, United States
, 86–87, 90–91
Hamer–Kermack–McKendrick–Soper (SIR) model
, 105–106
applications
, 106
contact-tracing strategy
, 113
differential equations
, 107
Distributed Lag Model
, 108
Hospitalized, Intensive Care, and Deaths
, 107
Irish Health Information Quality Authority
, 112
Istituto Superiore di Sanita
, 112–113
model parameters
, 110
Nelder–Mead optimizer algorithm
, 111
pandemic data
, 110–111
parameter estimation
, 111
recovery rate
, 108
regressive-autoregressive time series model
, 108
SIHCRD model
, 108–111, 114
Susceptible, Infected, and Recovered
, 107
transmission rate
, 108
Harmonised Index of Consumer Prices (HICP)
, 9
Impact of Event Scale (IES) indicators
, 136–137
Incorporated Research Institutions for Seismology (IRIS) database
, 87–88
Irish Health Information Quality Authority
, 112
Italian National Healthcare System
, 160–161
Labour market
, 61–62, 134
Labour productivity
, 62
Linear setting, financial markets
comovement
, 76
cross-sectional asset pricing
, 76
limitations
, 77
out-of-sample forecasting
, 76–77
Lombardy healthcare system
clinical manager
, 169
data source
, 162
emergency management
, 161
geographic regression-discontinuity design
, 166
hospital-based assistance
, 164
infection rates
, 160
intensive care units (ICU)
, 162–163
Italian National Healthcare System
, 160–161
mortality
, 163–164, 167
national and international realities
, 162
national lockdown
, 160
organisational structure
, 162
pandemic emergency
, 169–170
patient-centric care
, 166–167
quality assessment
, 170
quality of cares, non-COVID patients
, 167–168
quantitative evidence
, 161–162
regional pandemic management system
, 166
regional policies
, 165–166
risk perception
, 159
sociodemographic indicators
, 161
spatial autoregressive (SAR) model
, 165
MEGs. See Minority Ethnic Groups (MEGs)
Mental health
12-item General Health Questionnaire (GHQ)
, 135–136
anxiety symptoms
, 127–128
Black, Asian and minority ethnic (BAME) groups
, 132
‘business-as-usual’ policy
, 128
Census Household Pulse Survey
, 128
common mental disorder (CMD)
, 126
costs
, 131–132
counselling demand
, 126–127
COVID-19 effect
, 118, 120, 125
demographic dimensions
, 129–132
differences-in-differences (DiD) analysis
, 137
domestic violence
, 131–132
face-to-face survey
, 133
General Anxiety Disorder 7-item scale (GAD-7)
, 137
Impact of Event Scale (IES) indicators
, 136–137
labour market
, 134
lockdown and social distance
, 118
non-pharmaceutical strategies
, 117–118
policy responses
, 133
pre-COVID-19 baseline data
, 135
psychological distress
, 127–128
remote learning
, 134
risk factors
, 128–129
Shapley- Shorrocks decomposition
, 138
social media
, 119
UK Household Longitudinal Study (UKHLS)
, 135–136
Minority Ethnic Groups (MEGs)
alcohol consumption
, 147
Black minorities
, 145, 147–148
COVID-19 outcomes
, 148–150
empirical evidence
, 151
employment rate
, 144–145
labour market
, 151–152
lockdown restrictions
, 145
mental health and well-being
, 146–147
self-employment
, 145
social mobility
, 152
sociodemographic and health factors
, 150
socioeconomic heterogeneities
, 145–146
total registered hospital death
, 148–149
UK Biobank data
, 150–151
Nelder–Mead optimizer algorithm
, 111
Out-of-sample forecasting, financial markets
empirical analysis
, 81–82
linear setting
, 76–77
Oxford COVID-19 Government Response Tracker (OxCGRT)
, 5
Personal Consumption Expenditure (PCE) index
, 3
Principal components analysis
, 79–80
Probability Density Function (PDF)
, 88–89
Public expenditure index (PE)
downside scenario
, 21–22
growth factor
, 23
historical data and variables level
, 23–24
intermediate scenario
, 21–22
monthly growth
, 23–24
pandemic scenarios
, 21–22
unemployment rate
, 24
upside scenario
, 21–22
Quality of cares, non-COVID patients
, 167–168
Regional pandemic management system
, 166
Regressive-autoregressive time series model
, 108
Rolling window estimation approach
, 8–9, 38–40
Root Mean Square Error (RMSE)
, 17–18, 20, 22
Seismology, United States
Corona economic crisis
, 87
economic analysis
, 85–86
economic indicators
, 85–86
fixed effects panel data estimation
, 90–91
Gross Domestic Product (GDP)
, 86–87, 90–91
human-induced ground vibrations
, 88–89
Incorporated Research Institutions for Seismology (IRIS) database
, 87–88
macroeconomic analysis
, 91–92
Probability Density Function (PDF)
, 88–89
seismic noise
, 86–87
seismic sensors
, 86–87
seismic stations
, 87–88
vibration index (VI)
, 86–87, 89–90
Sequential factor estimation
, 9, 11–12, 14
Shapley-Shorrocks decomposition
, 138
Small and Medium Enterprises (SMEs)
, 100
Social distancing policies
, 100–101
Social dysfunction
, 130
S&P 500 index
, 72–73
Spatial autoregressive (SAR) model
, 165
Spatio-temporal models
, 105–106
Spedali Civili
, 168
Stock market
, 75
Susceptible, Infected, Hospitalized, Intensive Care, Recovered and Deaths (SIHCRD) model
, 108–111, 114
Tracking error (TE)
, 17
factor model (FM) approach
, 8–9, 17
UK-Biobank and UK Labour Force Survey (UKLFS)
, 144
UK Household Longitudinal Study (UKHLS)
, 135–136, 144, 146–147, 151–152
Uncertainty estimation, Italy
Approximate Bayesian Computation (ABC)
, 107
convenience sampling
, 106
COVID-19 diffision
, 110–112
Hamer–Kermack–McKendrick–Soper model
, 105–106
single discrete-time equation
, 107
spatio-temporal models
, 105–106
stochastic epidemic models. See Hamer-Kermack-McKendrick-Soper (SIR) model
Unemployment rate
, 3, 9, 17
conditional forecasts
, 24, 28, 30, 32
US Department of Health and Human Services
, 72–73
Vaccination campaign
first dose
, 153
pre-existent health conditions
, 154
second dose
, 153–154
socioeconomic inequality
, 152
survey data
, 154–155
Variance
, 9, 11–14, 36, 38
rolling window size
, 38–39
Vector autoregressive model with exogenous variables (VARX)
, 2–3, 7–8, 35–36
Vibration index (VI)
, 86–87, 89–90
Vix index
, 73–74
Web-based communication services
, 59
World Health Organisation (WHO)
, 73, 95–96
World Trade Organisation (WTO)
, 62–63
- Prelims
- Understanding Economic Instability during the Pandemic: A Factor Model Approach
- General Equilibrium Analyses of COVID-19 Impacts and Policies: An Historical Perspective
- The Relationship between COVID-19 and the Financial Market
- Exploring the Economic Effects of COVID-19 in the United States through the Seismograph
- COVID-19 and Digital Economy: The Journey towards a Digital Transformation in New Normal: How to Prepare for the Future
- Estimating Uncertainty in Epidemic Models: An Application to COVID-19 Pandemic in Italy
- The Effects of the Pandemic on Mental Health
- Ethnicity and Inequality during the COVID-19 Pandemic in the UK
- The Impact of COVID-19 Pandemic on the Healthcare System in Lombardy
- Index