This document describes Hanan Naser's research estimating and forecasting Bahrain's quarterly GDP using simple regression and factor models. It finds that a simple regression model augmented with intercept correction provided the most reliable estimates. Several models were able to shorten the lag in official GDP estimates by one week. The research adapts forecasting techniques used for developed countries to the case of Bahrain as an oil-producing developing country.

1. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Estimating and Forecasting Bahrain
Quarterly GDP
Hanan Naser
Department of Economics
The University of Sheffield
March 22, 2012
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2. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
1 Introduction
2 Model
3 Data
4 Empirical Work
5 Results
6 Conclusion
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3. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Introduction
Background
The importance of macroeconomic forecasting
FLASH estimates:
USA and UK ⇒ 25 days after the end of the quarter
Euro area (EUROSTAT) ⇒ 45-48 days.
Types of econometric models:
’Indicator variables’ and ’Factor’ approaches.
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4. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Introduction
Motivation
Simple Regression Approach :
(Trehan (1992,1996), Parigi & Schlitzer (1995), Bovi et al (2000). Camba-
Mendez et al (2001), Irac & Sedillot (2002) and Mourougma & Roma (2002))
Factor Based Model:
(Stock and Watson (1998,2002a, 2002b), Forni and Reichlin (1998), Forni,
Lippi, Hallin and Reichlin (2001a)).
Size and the composition of the data and its impact on factor
estimates Boivin and Ng (2006).
To date, the majority of empirical studies on early estimates of
GDP have focused on developed countries such as UK, USA and
Euro area.
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5. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Introduction
Objectives
Objectives of the study:
Adapt the methodology used in developed countries to obtain
FLASH estimates for an oil producing developing country,
particularly Bahrain.
Shorten the lag period and provide early reliable estimates of
GDP using different models.
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6. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Model
Simple Regression Model
∆yt = c + Σp
i=1αi∆yt−i + Σp
i=0Σk
j=1βixt−i, j + ut (1)
yt ⇒ log of Bahrain GDP
xi,j ⇒ j-th indicator variable (j=1,2,.....k) in logs
c ⇒ intercept
p ⇒ number of lags
∆ ⇒ 1st difference operator
ut ⇒ disturbance ∼ N(0, σ2)
All possible combinations of the q = k(p + 1) + p indicators are used as possible models.
This leads to the constructions of s
i=1
q!
(q−i)!i!
possible models which is 1159 models in
our case
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7. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Model
Factor Based SW Model
Step 1: Estimation
Ft = λ Xt/N (2)
Step 2: Regression
Xt+1 = B Ft + ut+1 (3)
B = (λ
N
λ)−1
λ ΓT
x1 (4)
XT+1|T = B FT (5)
∆yt ⇒ 1st element of X
λ ⇒ N × r matrix of eigenvectors
ΓT
xk = T−1 T
t=k+1 XtXt−k
r ⇒ number of factors
p ⇒ lags of dependent variable chosen using BIC
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8. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Data
Time series variables and sources
Quarterly data ⇒ 1995: Q1 to 2008: Q3
Data published with different delays ⇒ GDP growth
published 90 days after the end of the entire quarter.
All data obtained from IMF except metal and energy prices
obtained from EIA
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9. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Data
Time series variables
Dataset
Industrial Production ⇒ 4
Consumer Prices ⇒ 2
Monetary Aggregates ⇒ 6
Interest rates ⇒ 4
Trade ⇒ 4
Exchange rate ⇒ 2
International prices ⇒ 2
Other Financial Variables ⇒ 41
Total ⇒ 65
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10. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Data
Models Datasets
Simple Regression Approach :
Linear regression between GDP growth and six explanatory
variables including refined petroleum production index(RPPI),
exports (EXPP), metal price index (MI), oil price index (OILI),
consumer price index (CPI) and broad money aggregates (M3).
Factor Based Approach:
Employed 65 variables to extract the factors used in forecasting
equation.
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11. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Empirical Work
Data Treatment
Table 1: IV Correlation coefficients with GDP growth
Indicator Variable Correlation with ∆GDP
∆ lnEXPPP 0.2353
lnRPPI 0.2676
∆ lnMI 0.1178
∆ lnOILI 0.1846
∆ lnCPI 0.1578
∆ lnM1 0.1283
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12. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Empirical Work
Data Treatment
Table 2: Unit Root Test at level
ADF- test
Indicator Variable
With trend Without trend
lnGDP 1.833 -1.127
lnEXPPP 0.885 -2.302
lnRPPI -6.453*** -6.384***
lnMI 0.452 -0.520
lnOILI 1.204 -1.476
lnCPI 0.404 -1.346
lnM3 2.379 -2.006
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13. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Empirical Work
Intercept Correction Model
What is IC?
Intercept correction model (IC)
⇓
Solution to deterministic shifts
Why we use it?
Descriptive ⇒ economic
forecasting
Rationale ⇒ correct
forecasting inaccuracy
⇓
improve the forecasts of econometric models (Clements and
Hendry (1996)).
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14. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Empirical Work and Results
Evaluating Forecast Performance
Evaluating point forecast using root mean square forecast
error RMSFE, residual standard deviation (RSD) and
Pesaran and Timmerman (1992) test.
Utilize the corrected Diebold Mariano (1995) test of Harvey
et al (1997), to evaluate weather two different forecast
models are significantly different from each other or not
using models loss function.
Evaluation of density forecasts using Diebold et al (1998)
test based on probability integral transform (PIT). Testing
standard normality of the cumulative density function (CDF)
using Doornik and Hansen (1994) (DH) test as suggested by
Clements and Smith (2000) and examin independence in the
PIT using Ljung- Box test .
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15. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Results
Results using 6 IV
Table 3: Business Cycle Characteristics using 6 Indicator Variables
Model
Evaluating Forecast Performance
RMSFE RSD DMpval PT DHpval QBOXpval
3IV 0.021547 0.000874 0.9991 -0.3191 0.0647 0.0562
3IV/IC 0.019790 0.000962 0.9992 1.1754
§
¦
¤
¥0.0033 0.5537
SIV 0.040550 0.002765 0.0054 -0.319 0.2769 0.0054
SIV/IC 0.026603 0.001098 0.17280 -0.319 0.2554 0.9916
AR(1) 0.085135 0.008257 0.0000 -1.531 0.9679 0.2791
AR(1)/IC 0.120170 0.018809 0.0000 -0.319 0.6422 0.1687
SW1 0.08069 0.00796 0.9950 -9.990 0.9094 0.7657
SWCORR 0.07560 0.007763 0.9949 1.1754 0.5101 0.2274
SW3 0.075982 0.007667 0.9840 1.5953 0.5948 0.1308
SWCORR3 0.046728 0.002794 0.9982 -0.951 0.1752 0.0691
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16. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Results
Plots of results
Figure 1: Actual and Forecasted GDP growth using 6 IV
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17. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Results
Results using 65 variables
Table 4: Business Cycle Characteristics using 65 variables
Model
Evaluating Forecast Performance
RMSFE RSD DMpval PT DHpval QBOXpval
3IV 0.080551 0.00758 0.0000 0.4742 0.6242 0.6484
3IV/IC 0.10754 0.01547 0.0000 0.8935 0.4075 0.2124
SIV 0.089990 0.00771 0.5330 2.7828 0.4176 0.5330
SIV/IC 0.14414 0.02002 0.0073 1.1754 0.3336 0.0640
AR(1) 0.085135 0.00825 0.0000 -1.531 0.9679 0.2791
AR(1)/IC 0.12017 (9) 0.01881 0.0000 -0.319 0.6422 0.1687
SW1 0.083821 0.00885 0.3773 -9.990 0.9221 0.4144
SWCORR 0.085054 0.00877 0.3521 0.2698 0.9749 0.3551
SW3 0.085041 0.008758 0.3672 1.4755 0.9707 0.1263
SWCORR3 0.088491 0.010834 0.2593 1.4755 0.6870 0.6646
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18. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
Conclusion
Conclusion
The most reliable estimates achieved using simple regression
estimates augmented with intercept correction mode
(3IV/IC). However, it can be considered only if the forecaster
concern about the point forecast.
3IV and SIV/IC are good choices and pass point and density
forecasts.
We can shorten the lag of the official estimates by one week.
Our results support Boivin and Ng (2006) argument, which
says that more information does not always help to produce
more accurate results.
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19. Estimating
and
Forecasting
Bahrain
Quarterly
GDP
Hanan Naser
Outline
Introduction
Model
Data
Empirical Work
Results
Conclusion
The End
Thank You
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