Forecasting GDP all over the world using leading indicators based on comprehensive survey data

Figures & data

Table 1. Best WES indicators across countries and forecast horizons.

	$h=0$		$h=1$		$h=2$
Country	rRMSFE	Indicator	rRMSFE	Indicator	rRMSFE	Indicator
Argentina	0.996	CLI	1.009	SIT	1.001	SIT
Australia	0.974	EXP	0.997	EXP	1.008	EXP
Austria	0.974	CLI	0.984	EXP	0.944	EXP
Belgium	0.961	CLI	1.004	EXP	0.990	EXP
Brazil	0.881	CLI	0.946	CLI	0.953	CLI
Bulgaria	0.927	CLI	0.962	CLI	1.024	CLI
Canada	0.959	EXP	0.993	CLI	0.967	EXP
Chile	0.971	CLI	0.931	CLI	0.857	CLI
China	0.985	CLI	0.998	SIT	0.987	SIT
Czech Republic	0.968	EXP	1.002	EXP	0.983	EXP
Denmark	0.988	CLI	1.016	CLI	1.005	CLI
Estonia	0.858	CLI	0.937	EXP	1.014	EXP
Finland	0.905	EXP	0.877	EXP	0.908	EXP
France	0.944	CLI	0.986	EXP	0.974	EXP
Germany	0.948	CLI	0.960	EXP	0.978	EXP
Hong Kong	0.938	EXP	0.994	EXP	0.991	SIT
Hungary	0.995	CLI	0.992	CLI	0.960	SIT
India	0.997	CLI	1.001	EXP	1.001	CLI
Indonesia	1.115	EXP	1.045	SIT	1.030	CLI
Ireland	0.925	EXP	0.976	EXP	0.984	EXP
Italy	0.966	CLI	0.942	EXP	0.920	EXP
Japan	0.953	EXP	0.978	EXP	0.999	EXP
Latvia	0.847	CLI	0.822	CLI	0.782	EXP
Mexico	0.970	EXP	0.983	EXP	0.967	SIT
Netherlands	0.925	CLI	0.971	CLI	0.996	EXP
New Zealand	0.975	SIT	0.986	SIT	1.002	SIT
Norway	0.941	EXP	0.983	EXP	0.991	EXP
Philippines	0.946	EXP	0.986	EXP	0.986	SIT
Poland	0.976	CLI	0.994	CLI	0.985	CLI
Portugal	0.857	CLI	0.909	CLI	0.998	SIT
Russia	0.966	EXP	0.998	SIT	0.795	SIT
Slovakia	0.920	CLI	0.985	CLI	1.011	EXP
Slovenia	0.933	CLI	0.996	CLI	0.980	SIT
South Africa	0.963	CLI	0.971	CLI	0.931	CLI
South Korea	0.989	EXP	1.017	CLI	0.998	EXP
Spain	0.698	CLI	0.688	CLI	0.913	CLI
Sweden	0.895	EXP	0.942	EXP	0.971	EXP
Switzerland	1.011	CLI	1.019	EXP	1.010	CLI
Taiwan	0.999	CLI	1.002	EXP	0.991	CLI
Thailand	0.944	EXP	1.028	EXP	1.017	SIT
Turkey	0.911	CLI	0.964	CLI	0.994	EXP
United Kingdom	0.975	CLI	0.978	EXP	0.975	EXP
United States	0.951	CLI	0.976	CLI	1.002	SIT
Uruguay	0.841	CLI	0.909	CLI	0.978	SIT
EU-27	0.911	EXP	0.959	EXP	0.829	EXP
Eurozone	0.886	CLI	0.957	EXP	0.898	EXP
World	0.880	EXP	0.982	EXP	0.900	EXP

For each forecast horizon and country or aggregate, the table reports the smallest relative root mean squared forecast error (rRMSFE) of the three possible univariate one-indicator models; the columns ’Indicator’ show these best indicators. The indicators are abbreviated as: $SIT\dots$ WES present economic situation, $EXP\dots$ WES expectations for the next six months and $CLI\dots$ WES economic climate. The benchmark is always the AR$(1)$. A rRMSFE hold in bold face indicates a significant improvement in forecast accuracy due to the Clark-West test at least to the 10% confidence level.

Table 2. Forecast performance after adding the main trading partners.

	$h=0$			$h=1$			$h=2$
	rRMSFE			rRMSFE			rRMSFE
Country	Base	MTP	Model	Base	MTP	Model	Base	MTP	Model
Argentina	0.996	0.966	CLI–3	1.009	1.015	SIT–1	1.001	1.008	SIT–1
Australia	0.974	0.951	EXP–1	0.997	0.996	EXP–1	1.008	0.982	SIT–1
Austria	0.974	0.955	CLI–3	0.984	0.980	EXP–1	0.944	0.939	EXP–1
Belgium	0.961	0.845	CLI–1	1.004	0.899	CLI–1	0.990	0.930	CLI–1
Brazil	0.881	0.860	CLI–1	0.946	0.958	CLI–1	0.953	0.970	EXP–1
Bulgaria	0.927	0.912	CLI–2	0.962	0.947	CLI–3	1.024	0.987	EXP–3
Canada	0.959	0.962	EXP–1	0.993	0.995	EXP–1	0.967	0.963	EXP–1
Chile	0.971	0.956	CLI–1	0.931	0.932	CLI–1	0.857	0.868	CLI–1
China	0.985	0.997	CLI–1	0.998	1.049	SIT–1	0.987	1.030	CLI–1
Czech Republic	0.968	0.991	EXP–2	1.002	1.033	EXP–3	0.983	0.949	EXP–3
Denmark	0.988	0.932	EXP–2	1.016	0.905	EXP–1	1.005	0.996	SIT–1
Estonia	0.858	0.841	CLI–3	0.937	0.895	EXP–3	1.014	1.034	SIT–3
Finland	0.905	0.905	CLI–3	0.877	0.858	EXP–3	0.908	0.874	SIT–3
France	0.944	0.934	CLI–2	0.986	0.966	EXP–1	0.974	0.932	SIT–1
Germany	0.948	0.968	EXP–3	0.960	0.972	EXP–3	0.978	0.951	SIT–3
Hong Kong	0.938	0.935	EXP–2	0.994	0.990	EXP–1	0.991	1.004	SIT–1
Hungary	0.995	0.927	EXP–3	0.992	0.931	EXP–1	0.960	0.941	EXP–1
India	0.997	0.993	EXP–1	1.001	1.007	EXP–1	1.001	1.027	CLI–1
Indonesia	1.115	1.104	EXP–1	1.045	1.090	EXP–1	1.030	1.040	EXP–2
Ireland	0.925	0.917	CLI–1	0.976	0.960	SIT–1	0.984	0.990	EXP–1
Italy	0.966	0.946	EXP–1	0.942	0.921	EXP–1	0.920	0.886	EXP–1
Japan	0.953	0.893	EXP–3	0.978	0.961	EXP–2	0.999	0.994	EXP–1
Latvia	0.847	0.801	CLI–2	0.822	0.781	CLI–2	0.782	0.776	EXP–2
Mexico	0.970	0.958	EXP–3	0.983	0.992	EXP–1	0.967	0.969	SIT–3
Netherlands	0.925	0.898	CLI–3	0.971	0.944	CLI–1	0.996	0.972	EXP–1
New Zealand	0.975	0.984	SIT–2	0.986	0.974	SIT–2	1.002	0.961	SIT–2
Norway	0.941	0.939	EXP–1	0.983	0.970	SIT–1	0.991	1.015	SIT–1
Philippines	0.946	0.937	EXP–2	0.986	0.983	EXP–2	0.986	0.998	SIT–3
Poland	0.976	0.981	CLI–1	0.994	1.003	CLI–1	0.985	0.994	CLI–1
Portugal	0.857	0.804	EXP–1	0.909	0.836	EXP–1	0.998	0.936	EXP–1
Russia	0.966	0.938	EXP–1	0.998	1.025	SIT–1	0.795	0.812	SIT–1
Slovakia	0.920	0.939	CLI–1	0.985	0.987	EXP–1	1.011	1.026	SIT–2
Slovenia	0.933	0.942	CLI–3	0.996	0.947	CLI–3	0.980	0.935	EXP–1
South Africa	0.963	0.945	CLI–2	0.971	0.992	SIT–1	0.931	0.966	SIT–3
South Korea	0.989	0.990	EXP–2	1.017	1.025	CLI–1	0.998	0.993	EXP–1
Spain	0.698	0.701	CLI–1	0.688	0.713	CLI–1	0.913	0.896	CLI–1
Sweden	0.895	0.856	EXP–2	0.942	0.910	EXP–1	0.971	0.934	EXP–1
Switzerland	1.011	0.994	CLI–3	1.019	1.011	EXP–1	1.010	1.010	SIT–1
Taiwan	0.999	0.975	EXP–2	1.002	1.000	EXP–2	0.991	0.996	EXP–1
Thailand	0.944	0.927	EXP–1	1.028	1.033	EXP–1	1.017	1.017	SIT–1
Turkey	0.911	0.935	CLI–1	0.964	1.024	CLI–1	0.994	0.933	EXP–3
United Kingdom	0.975	0.965	EXP–2	0.978	0.985	EXP–2	0.975	0.973	CLI–2
United States	0.951	0.941	EXP–1	0.976	0.947	EXP–1	1.002	0.942	SIT–2
Uruguay	0.841	0.858	CLI–1	0.909	0.939	CLI–1	0.978	1.030	EXP–1
EU-27	0.911	0.896	CLI–3	0.959	0.975	EXP–1	0.829	0.773	SIT–3
Eurozone	0.886	0.839	CLI–2	0.957	0.957	SIT–1	0.898	0.887	EXP–1
World	0.880	0.871	EXP–1	0.982	0.986	SIT–1	0.900	0.871	SIT–3

For each forecast horizon and country or aggregate, the table compares the relative root mean squared forecast error (rRMSFE) of the baseline (column ’Base’) with the smallest rRMSFE of the 9 possible multi-indicator models based on sequentially adding the main trading partners (column ’MTP’); the columns ’Model’ show the abbreviation of this best multi-indicator model. An underline indicates which of the two competitors, baseline model or a model based on main trading partners, produces the smaller rRMSFE. The indicators are abbreviated as: $SIT\dots$ WES present economic situation, $EXP\dots$ WES expectations for the next six months and $CLI\dots$ WES economic climate. Numbers in the model’s name indicate whether a multi-indicator model includes the WES indicators of one (–1), two (–2) or three (–3) main trading partners. The benchmark is always the AR$(1)$. A rRMSFE hold in bold face indicates a significant improvement in forecast accuracy due to the Clark-West test at least to the 10% confidence level.

Table A1. Countries, data sources and main trading partners 2017.

					Main Trading Partners
Country	GDP	Source	Start	WES	First	Second	Third	Source
Argentina	R, SA	OECD	Q1-1993	9	Brazil	US	China	World Bank
Australia	R, SA	OECD	Q1-1990	11	China	Japan	South Korea	World Bank
Austria	R, SA	Eurostat	Q1-1990	13	Germany	US	Italy	Eurostat
Belgium	R, SA	Eurostat	Q1-1990	15	Germany	France	Netherlands	Eurostat
Brazil	R, SA	OECD	Q1-1996	21	China	US	Argentina	World Bank
Bulgaria	R, SA	Eurostat	Q1-2000	14	Germany	Italy	Turkey	Eurostat
Canada	R, SA	OECD	Q1-1990	11	US	China	UK	World Bank
Chile	R, SA	OECD	Q1-1996	9	China	US	Japan	World Bank
China	R, SA	National	Q1-1992	43	US	Hong Kong	Japan	World Bank
Czech Republic	R, SA	Eurostat	Q1-1996	10	Germany	Slovakia	Poland	Eurostat
Denmark	R, SA	Eurostat	Q1-1991	7	Germany	Sweden	UK	Eurostat
Estonia	R, SA	Eurostat	Q1-1997	20	Finland	Sweden	Latvia	Eurostat
Finland	R, SA	Eurostat	Q1-1990	17	Germany	Sweden	US	Eurostat
France	R, SA	Eurostat	Q1-1990	17	Germany	Spain	Italy	Eurostat
Germany	R, SA	Eurostat	Q1-1990	48	US	France	China	Eurostat
Hong Kong	R, SA	National	Q1-1990	8	China	US	India	World Bank
Hungary	R, SA	Eurostat	Q1-1995	11	Germany	Italy	Austria	World Bank
India	R, SA	OECD	Q2-1996	13	US	Hong Kong	China	World Bank
Indonesia	R, SA	OECD	Q1-1990	7	China	US	Japan	World Bank
Ireland	R, SA	Eurostat	Q1-1990	7	US	UK	Belgium	Eurostat
Italy	R, SA	Eurostat	Q1-1990	21	Germany	France	US	Eurostat
Japan	R, SA	OECD	Q1-1990	29	US	China	South Korea	World Bank
Latvia	R, SA	Eurostat	Q1-1997	6	Russia	Estonia	Germany	Eurostat
Mexico	R, SA	OECD	Q1-1990	12	US	Canada	Germany	World Bank
Netherlands	R, SA	Eurostat	Q1-1990	15	Germany	Belgium	UK	Eurostat
New Zealand	R, SA	OECD	Q1-1992	10	China	Australia	US	World Bank
Norway	R, SA	Eurostat	Q1-1990	6	UK	Germany	Netherlands	World Bank
Philippines	R, SA	National	Q1-1998	6	Japan	US	Hong Kong	World Bank
Poland	R, SA	Eurostat	Q1-1996	16	Germany	Czech R.	UK	Eurostat
Portugal	R, SA	Eurostat	Q1-1990	11	Spain	France	Germany	Eurostat
Russia	R, SA	OECD	Q1-2003	19	China	Netherlands	Germany	World Bank
Slovakia	R, SA	Eurostat	Q1-1998	10	Germany	Czech R.	Poland	Eurostat
Slovenia	R, SA	Eurostat	Q1-1998	7	Germany	Italy	Austria	Eurostat
South Africa	R, SA	OECD	Q1-1990	20	China	US	Germany	World Bank
South Korea	R, SA	OECD	Q1-1990	9	China	US	Hong Kong	World Bank
Spain	R, SA	Eurostat	Q1-1990	24	France	Germany	Italy	Eurostat
Sweden	R, SA	Eurostat	Q1-1990	13	Germany	Finland	US	Eurostat
Switzerland	R, SA	Eurostat	Q1-1990	14	Germany	US	China	World Bank
Taiwan	R, SA	National	Q1-1990	10	China	Hong Kong	US	WTO
Thailand	R, SA	National	Q1-1993	8	US	China	Japan	World Bank
Turkey	R, SA	OECD	Q1-1998	11	Germany	UK	US	World Bank
United Kingdom	R, SA	Eurostat	Q1-1990	18	US	Germany	France	Eurostat
US	R, SA	OECD	Q1-1990	27	Canada	Mexico	China	World Bank
Uruguay	R, SA	National	Q1-1997	5	China	Brazil	US	World Bank
EU-27	R, SA	Eurostat	Q1-1995	292	US	China	Switzerland	Eurostat
Eurozone	R, SA	Eurostat	Q1-1995	252	US	China	Switzerland	Eurostat
World	R, SA	–	Q1-1994	807	US	China	Germany	World Bank

For each country or aggregate, the table reports the characteristics of the GDP series, its corresponding data source as well as starting point and the average sample size of the WES between 1990 and 2017. The last four columns show the three main trading partners of each country or aggregate and again the data source from which we obtained the trade data. Abbreviations: SA … seasonally adjusted, R … real terms.

Table B1. Relative performance of other benchmark models.

	$h=0$		$h=1$		$h=2$
Country	AR($2$)	AR($p$)	AR($2$)	AR($p$)	AR($2$)	AR($p$)
Argentina	1.020	1.056	1.020	1.056	1.022	1.133
Australia	1.026	1.054	1.026	1.054	1.027	1.024
Austria	1.028	1.010	1.028	1.010	1.004	0.996
Belgium	0.981	0.978	0.981	0.978	1.078	1.208
Brazil	0.998	0.981	0.998	0.981	0.978	1.015
Bulgaria	1.028	1.219	1.028	1.219	1.081	1.011
Canada	1.022	1.042	1.022	1.042	1.100	1.153
Chile	1.014	1.050	1.014	1.050	1.058	1.055
China	0.994	0.989	0.994	0.989	0.993	1.014
Czech Republic	1.022	1.050	1.022	1.050	1.088	1.070
Denmark	0.990	1.013	0.990	1.013	1.031	1.053
Estonia	0.906	1.031	0.906	1.031	1.195	1.195
Finland	1.013	1.009	1.013	1.009	0.992	1.052
France	1.010	1.000	1.010	1.000	1.039	1.132
Germany	1.007	1.026	1.007	1.026	1.035	1.035
Hong Kong	0.996	1.006	0.996	1.006	1.053	1.112
Hungary	1.034	1.001	1.034	1.001	1.093	1.058
India	1.024	1.063	1.024	1.063	1.011	1.078
Indonesia	0.997	1.215	0.997	1.215	1.105	1.068
Ireland	1.013	1.076	1.013	1.076	0.990	1.052
Italy	1.006	1.000	1.006	1.000	1.025	1.029
Japan	1.016	1.063	1.016	1.063	1.036	1.050
Latvia	0.874	0.884	0.874	0.884	1.064	1.138
Mexico	0.979	0.984	0.979	0.984	1.105	1.123
Netherlands	0.991	1.007	0.991	1.007	1.038	1.032
New Zealand	1.006	1.048	1.006	1.048	1.003	1.036
Norway	1.008	1.023	1.008	1.023	1.043	1.115
Philippines	1.019	1.065	1.019	1.065	0.959	1.011
Poland	1.028	0.983	1.028	0.983	1.134	1.153
Portugal	0.931	0.936	0.931	0.936	1.037	1.087
Russland	0.971	1.017	0.971	1.017	0.724	0.807
Slovakia	1.011	1.054	1.011	1.054	1.030	1.066
Slovenia	1.023	1.035	1.023	1.035	1.132	1.072
South Africa	1.005	1.000	1.005	1.000	1.038	1.149
South Korea	0.999	1.184	0.999	1.184	0.992	1.027
Spain	0.691	0.632	0.691	0.632	1.159	1.292
Sweden	1.000	0.999	1.000	0.999	0.993	1.098
Switzerland	1.001	1.004	1.001	1.004	1.033	1.096
Taiwan	1.017	1.030	1.017	1.030	1.025	1.009
Thailand	1.011	1.054	1.011	1.054	0.997	1.074
Turkey	1.011	1.072	1.011	1.072	1.012	0.934
United Kingdom	1.013	1.004	1.013	1.004	0.998	1.134
United States	0.993	1.021	0.993	1.021	1.026	1.075
Uruguay	0.919	0.959	0.919	0.959	1.049	1.071
EU-27	1.020	1.000	1.020	1.000	0.961	0.980
Eurozone	1.018	1.000	1.018	1.000	1.000	1.030
World	1.022	1.051	1.022	1.051	1.057	1.076

For each forecast horizon and country or aggregate, the table reports the RMSFE of the two additional autoregressive models, AR($2$) and AR($p$), relative to our chosen AR($1$) benchmark.

Table B2. Forecast performance by adding the US, the Eurozone and the World as main trading partners.

	$h=0$			$h=1$			$h=2$
	rRMSFE			rRMSFE			rRMSFE
Country	Base	MTP	UEW	Base	MTP	UEW	Base	MTP	UEW
Argentina	0.996	0.966	0.932	1.009	1.015	1.009	1.001	1.008	0.998
Australia	0.974	0.951	1.007	0.997	0.996	1.006	1.008	0.982	1.011
Austria	0.974	0.955	0.935	0.984	0.980	0.985	0.944	0.939	0.954
Belgium	0.961	0.845	0.918	1.004	0.899	0.982	0.990	0.930	0.947
Brazil	0.881	0.860	0.897	0.946	0.958	0.975	0.953	0.970	0.966
Bulgaria	0.927	0.912	0.846	0.962	0.947	0.887	1.024	0.987	0.957
Canada	0.959	0.962	0.962	0.993	0.995	0.995	0.967	0.963	0.963
Chile	0.971	0.956	0.945	0.931	0.932	0.897	0.857	0.868	0.862
China	0.985	0.997	0.997	0.998	1.049	1.049	0.987	1.030	1.030
Czech Republic	0.968	0.991	0.911	1.002	1.033	0.997	0.983	0.949	0.975
Denmark	0.988	0.932	0.858	1.016	0.905	0.888	1.005	0.996	0.949
Estonia	0.858	0.841	0.840	0.937	0.895	0.928	1.014	1.034	1.023
Finland	0.905	0.905	0.886	0.877	0.858	0.866	0.908	0.874	0.904
France	0.944	0.934	0.894	0.986	0.966	0.977	0.974	0.932	0.943
Germany	0.948	0.968	0.973	0.960	0.972	0.983	0.978	0.951	0.998
Hong Kong	0.938	0.935	0.887	0.994	0.990	1.001	0.991	1.004	1.023
Hungary	0.995	0.927	0.920	0.992	0.931	0.958	0.960	0.941	0.911
India	0.997	0.993	0.993	1.001	1.007	1.007	1.001	1.027	1.021
Indonesia	1.115	1.104	1.102	1.045	1.090	1.103	1.030	1.040	1.017
Ireland	0.925	0.917	0.917	0.976	0.960	0.960	0.984	0.990	0.984
Italy	0.966	0.946	0.834	0.942	0.921	0.911	0.920	0.886	0.889
Japan	0.953	0.893	0.905	0.978	0.961	0.986	0.999	0.994	0.994
Latvia	0.847	0.801	0.818	0.822	0.781	0.791	0.782	0.776	0.752
Mexico	0.970	0.958	0.954	0.983	0.992	0.992	0.967	0.969	0.983
Netherlands	0.925	0.898	0.853	0.971	0.944	0.915	0.996	0.972	0.937
New Zealand	0.975	0.984	0.988	0.986	0.974	0.993	1.002	0.961	1.015
Norway	0.941	0.939	0.938	0.983	0.970	0.951	0.991	1.015	0.997
Philippines	0.946	0.937	0.932	0.986	0.983	0.982	0.986	0.998	0.986
Poland	0.976	0.981	1.019	0.994	1.003	1.011	0.985	0.994	1.002
Portugal	0.857	0.804	0.793	0.909	0.836	0.809	0.998	0.936	0.886
Russia	0.966	0.938	0.891	0.998	1.025	1.000	0.795	0.812	0.780
Slovakia	0.920	0.939	0.968	0.985	0.987	0.991	1.011	1.026	1.024
Slovenia	0.933	0.942	0.890	0.996	0.947	0.945	0.980	0.935	0.922
South Africa	0.963	0.945	0.924	0.971	0.992	0.984	0.931	0.966	0.947
South Korea	0.989	0.990	0.998	1.017	1.025	1.036	0.998	0.993	1.019
Spain	0.698	0.701	0.735	0.688	0.713	0.681	0.913	0.896	0.816
Sweden	0.895	0.856	0.919	0.942	0.910	0.944	0.971	0.934	0.969
Switzerland	1.011	0.994	0.954	1.019	1.011	1.009	1.010	1.010	1.000
Taiwan	0.999	0.975	0.952	1.002	1.000	0.997	0.991	0.996	0.987
Thailand	0.944	0.927	0.927	1.028	1.033	1.033	1.017	1.017	1.017
Turkey	0.911	0.935	0.940	0.964	1.024	0.983	0.994	0.933	0.909
United Kingdom	0.975	0.965	0.961	0.978	0.985	0.990	0.975	0.973	0.983
United States	0.951	0.941	0.916	0.976	0.947	0.947	1.002	0.942	0.928
Uruguay	0.841	0.858	0.819	0.909	0.939	0.903	0.978	1.030	0.961
EU-27	0.911	0.896	0.862	0.959	0.975	0.969	0.829	0.773	0.776
Eurozone	0.886	0.839	0.854	0.957	0.957	0.957	0.898	0.887	0.874
World	0.880	0.871	0.868	0.982	0.986	0.984	0.900	0.871	0.867

For each forecast horizon and country or aggregate, the table compares the relative root mean squared forecast error (rRMSFE) of the baseline (column ’Base’) with the smallest rRMSFE of the 9 possible multi-indicator models based on sequentially adding either the three main trading partners (column ’MTP’) or the US, the Eurozone and the World (column ’UEW’). The benchmark is always the AR$(1)$.