Data and Methodology GDP


Data and Methodology
We use the publicly available quarterly data for Egypt ranging from March 2001 to March 2021.
The data source for all the variables except economic policy uncertainty is the Central Bank of
Egypt (CBE); we use monthly statistical bulletins, quarterly economic reviews, annual reports, and
quarterly time-series data – all retrieved from the official CBE webpage. Data for economic policy
uncertainty (EPU) index has been retrieved from Federal Reserve Bank of St. Louis Economic
Data (FRED), World Economic Uncertainty Index for Egypt. The summary of the variables used
in the research is given in the Table 1 below.
Table 1. Descriptive statistics of the variables
Available Data Qtrs. Mean Median Min. Max. S.D.
Ξ” Real GDP 2003:Q3-2021:Q1 71 1.272 1.210 -7.580 9.330 3.751
Domestic Debt 2001:Q2-2020:Q2 77 78.813 79.800 66.300 96.700 6.808
Total Debt 2001:Q2-2020:Q2 77 103.840 105.100 81.400 131.200 13.260
Fiscal Balance 2003:Q3-2020:Q3 69 -2.194 -2.200 -4.900 0.4000 0.964
Ξ” Money Supply 2003:Q3-2021:Q1 71 4.169 4.030 -1.790 13.330 2.900
Consumption 2001:Q3-2021:Q1 79 78.529 78.080 62.870 91.080 6.848

 Investment 2001:Q3-2021:Q1 79 16.874 16.420 8.200 27.780 3.941
Export 2001:Q3-2021:Q1 79 20.401 19.680 9.440 35.760 7.094
Inflation Rate 2003:Q3-2021:Q1 71 11.134 10.470 3.200 32.150 5.993
Unemployment Rate 2003:Q1-2021:Q1 73 10.656 10.600 7.200 13.400 1.853
EPU 2001:Q1-2021:Q1 81 0.1446 0.100 0.000 1.010 0.180
As can be seen from the Table 1, the overlapping period for all the variables is September 2003 –

 July 2020, 68 quarters in total. Since Egypt uses a fiscal year (FY) that starts in July and ends in
June, the time frame of our research matches FY 2003/04:Q1 – FY 2019/20:Q4.
The raw quarterly data for real GDP exhibits a clear pattern of seasonality: there is a significant
increase in real GDP between the last quarter of a previous fiscal year and the first quarter of a
current fiscal year; we observe this pattern for the entire span of the data series. We apply a simple
deseasonalizing method based on the centered moving average and used the deseasonalized data
to calculate the change in real GDP between quarterly periods.
We consider two debt variables as a potential threshold variable in our paper: domestic debt and
total debt, the latter refers to the sum of domestic debt and external debt. Both variables are
normalized to a GDP level (expressed as debt-to-GDP ratios) and are measured in percentage
points of Egypt’s nominal GDP. It should be noted that the public debt reported by CBE includes
the government’s debt as well as the debt by public economic authorities and the debt accrued to
National Investment Bank of Egypt; however, the share of the government’s debt in public debt is
estimated to fluctuate around 80-90% for the period analyzed in the current research.
The fiscal balance is calculated as the difference between total government revenues and total
government expenditures over nominal GDP, the positive (negative) value for the fiscal balance
implies that the government is running a fiscal surplus (deficit) in the current period. As it can be
seen in the Figure 1 below, the government of Egypt was running a fiscal deficit in all but three
time periods during the time span analyzed in the paper, and the median value for the fiscal balance
to nominal GDP is negative 2.2 percentage points.
Figure 1. Egypt's Fiscal Balance in 2003 September - 2020 September
We chose the standard control variables used in the economic literature: change in money supply
(M1), consumption, investment, export, inflation rate, unemployment rate, and EP uncertainty.
The inflation rate has been calculated as the change in consumer price index (CPI) relative to
corresponding month of previous year, the weights from January 2010 CPI were used for the entire
data span to preserve the consistency for the calculation exercise. Consumption, investment, and
export are normalized to a GDP level and are measured in GDP percentage points.
We resort to Hansen (2000) sample splitting threshold regression model as a methodological base
of our exercise. Consider a following simple regression equation:

 π‘¦π‘¦π‘‘𝑑 = (𝛽𝛽10 + 𝛽𝛽11π‘₯π‘₯𝑑𝑑 + 𝛽𝛽12π‘₯π‘₯𝑑𝑑−1 + +𝛽𝛽13π‘₯π‘₯𝑑𝑑−2 ) 𝐼𝐼[π‘žπ‘žπ‘‘π‘‘ ≤ 𝛾𝛾]
+ (𝛽𝛽20 + 𝛽𝛽21π‘₯π‘₯𝑑𝑑 + 𝛽𝛽22π‘₯π‘₯𝑑𝑑−1 + +𝛽𝛽23π‘₯π‘₯𝑑𝑑−2) 𝐼𝐼[π‘žπ‘žπ‘‘π‘‘ > 𝛾𝛾] + πœ€πœ€π‘‘π‘‘ (1)
where 𝑦𝑦𝑑𝑑 is the dependent variable;
π‘₯π‘₯𝑑𝑑−𝑗𝑗 is a vector of predictor variables, lagged j period(s);
π‘žπ‘žπ‘‘π‘‘ is a threshold variable;
𝛾𝛾 is a threshold value;
𝐼𝐼[π‘žπ‘žπ‘‘π‘‘ ≤ 𝛾𝛾]] is an indicator function that is equal to 1 when π‘žπ‘žπ‘‘π‘‘ ≤ 𝛾𝛾 and equals 0 otherwise;
𝐼𝐼[π‘žπ‘žπ‘‘π‘‘ > 𝛾𝛾]] is an indicator function that is equal to 1 when π‘žπ‘žπ‘‘π‘‘ > 𝛾𝛾 and equals 0 otherwise.
We are testing the null hypothesis 𝐻𝐻0 : = 𝛽𝛽1𝑖𝑖 = 𝛽𝛽2𝑖𝑖 𝑓𝑓𝑓𝑓𝑓𝑓 𝑖𝑖 = 0, 1, 2,3. If the null hypothesis has
been rejected, then the threshold effect has been established. The threshold value 𝛾𝛾 can be found
by estimating equation (1) though finding the minimum one of the sums of squared errors in a
threshold variable. Under the null hypothesis, the distribution of the p-value statistic is uniform,
and this transformation can be calculated through bootstrap.

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