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Forecasting Patent Applications at the European Patent Office: A Bottom-Up Versus Top-Down Approach Prepared for WIPO-OECD Workshop on Statistics in the Patent Field, 11-12 October 2004, Geneva, Switzerland By Frederick L. Joutz Research Program on Forecasting Department of Economics The George Washington University Washington, DC 20052 [email protected] Acknowledgements: This presentation benefited from helpful comments and suggestions by Peter Hingley, Marc Nicolas, and and Costas Mastrogianis. Any errors or omissions are my own. All opinions are mine and independent of the USPTO. Benchmark Forecasts Overview This paper presents preliminary results on forecasting patent applications at the European Patent Office using annual data. A two step framework is used in the modeling. First Filings Secondary or Subsequent filings Two Models are developed. – – An aggregate model A disaggregate regional model (Europe, Japan, US, and Other) Benchmark Forecasts Overview Forecasting patent filings is one of the important issues of the Trilateral Statistical Working Group, WIPO, and the OECD. TSWG, composed of the EPO, JPO, and USPTO. The three offices meet at least once a year to discuss this issue among a host of other patenting issues. TSWG has been holding annual meetings since 1992. The members treat forecasting as an important exercise for planning future resource and manpower requirements and revenues. This paper builds on previous research among the TSWG participants and a recent paper by Hingley and Nicolas (2004). Benchmark Forecasts A Theoretical Model of Patent Application Filing Patents protect more than just intellectual property; they are an intrinsic component of the larger economic picture. This occurs through the process of innovation, technological and scientific change and economic productivity and growth. The process is the result of the demand for and production of “new” knowledge. Schmookler (1954) - industrial invention is economically caused. In his view invention is driven by the interaction of supply and demand forces. Scherer (1983) Pavitt (1982), Hall, Griliches, and Hausman (1986) relationship between R&D effort and patent activities although primarily at the firm level. Griliches (1989). Adams, Kim, Joutz, Trost, and Mastrogianis (1997) Eaton and Kortum (1996 and 1998) and Gardner and Joutz (1996) Benchmark Forecasts A Theoretical Model of Patent Application Filing The most recent advancement of the endogenous growth theory has been the emergence of R&D-based models of growth in the seminal papers of Romer (1990), Grossman and Helpman (1991a, 1991b) and Aghion and Howitt (1992). This class of models agrees with the neoclassical Solow model that capital broadly defined is subject to diminishing returns, and hence the accumulation of capital does not sustain growth in the long run. Instead, technological progress is the source of sustained long run growth in both types of models. The point of departure lies in the way technological progress is viewed. In the neoclassical model, technology evolves exogenously. R&D- based models, the evolution of technology is explicitly and formally modeled as an endogenous process. Technological progress occurs as profit-maximizing firms invest in advanced technologies, and is promoted by the allocation of more productive resources towards R&D. Benchmark Forecasts A Theoretical Model of Patent Application Filing The model involves four variables: Output (Y), capital (K), labor (L), and technology or knowledge (A). There are two sectors: a goods- producing sector where output is produced, and an R&D sector where additions to the stock of knowledge are made. Labor can be freely allocated to either of the two sectors, to produce output (LY) or to produce new knowledge (LA). Hence, the economy is subject to the following resource constraint LY + LA = L, where L represents the total amount of labor in the economy. Specifically, output is produced according to the following production function: (1) Y K ( ALY ) Benchmark Forecasts 1 A Theoretical Model of Patent Application Filing The production function approach to knowledge in is the underpinning of the long-term modeling framework Research labor input is replaced by R&D expenditures as a measure of research effort primarily for data reasons. The production function concept is used in a long-term context for generation of new knowledge. is represented by patent application filings and the level of is calculated as the stock of historical patents At At LA At RDA Benchmark Forecasts Total Filings at the EPO 200000 Applications 160000 120000 80000 40000 0 80 82 84 86 88 90 92 94 96 98 00 02 Benchmark Forecasts Patent Filings at the EPO 80000 70000 Applications 60000 50000 40000 30000 20000 10000 0 80 82 84 86 88 90 F_EP F_JP Benchmark Forecasts 92 94 96 F_OT F_US 98 00 02 EPO Filings - Regional Shares 70 60 percent 50 40 30 20 10 0 80 82 84 86 88 90 SHF_EP SHF_JP Benchmark Forecasts 92 94 96 98 SHF_US SHF_OT 00 02 Stock of Knowledge - US Patents w/ 7% dep. 3000000 2500000 2000000 1500000 1000000 500000 0 1965 1970 1975 1980 1985 AKD_US Benchmark Forecasts 1990 1995 AKT_US 2000 Stock of Knowledge - Japan Patents w/ 7% dep. 5000000 4000000 3000000 2000000 1000000 0 1965 1970 1975 1980 1985 1990 1995 2000 AKD_JP Benchmark Forecasts AKT_JP Research and Development Expenditures - Europe, Japan, and US 280000 240000 $1995 PPP 200000 160000 120000 80000 40000 0 1970 1975 1980 RD_EU Benchmark Forecasts 1985 1990 RD_JP 1995 2000 RD_US The Modeling Procedure Inventors typically first file a patent application in their home country. The first filing represents an indicator of innovative activity. Patent protection on an international scale, perhaps based on preliminary searches, is sought about a year later. The preferred route is through an international or supranational procedure to reduce the duplication costs. Currently the European Patent Organization has 31 contracting member countries. This route is referred to as a subsequent or secondary filing. The forecasting problem is complicated by the fact that there are multiple routes for patent protection applications. Inventors have the option of filing nationally, through the European system, and the International PCT route administered through the World Intellectual Property Organization. However, the primary work load of searches and preliminary examinations from the PCT applications is performed through nine patent offices. The EPO is one of the most important offices authorized or designated to perform this work. This has become increasingly popular as over 90% of the European contracting states are selected when using the PCT route. Benchmark Forecasts Filings at EPO Filings after Domestic Filings with 1 Year Lag .6 .5 Share .4 .3 .2 .1 .0 80 82 84 86 88 90 92 94 96 98 00 02 FEPDOM_EP Benchmark Forecasts FEPDOM_JP FEPDOM_US The Modeling Procedure The model framework proceeds in two stages. See Hingley and Nicolas (2004) for a further exposition of this framework. In the first stage non-EPO and EPC patent applications are filed domestically. The model specification is ADL(p,p), autoregressive distributed lag model based on economic growth theory and the knowledge production function. p p p p i 1 i 1 i 1 i 1 DomFilt 0 1 DomFilt i 1 AKt i 1 RDt i 1 RGDPt i t Benchmark Forecasts The Modeling Procedure These domestic or “first” filings are a strong indicator of subsequent filings at the EPO and used in the second stage. The specification is similar. p p i 1 i 1 SEPOFilt 0 1 DomFilt i 1 SEPOFilt i p i 1 1 p EPOFilt i 1 ERGDPt i ut i 1 •Subsequent (or secondary) filings at the EPO are a function of •past domestic filings, •previous filings at the EPO, •the size of the EPO market, and •economic activity in Europe. Benchmark Forecasts Knowledge Production andDomestic Filing US US US DomFiltUS fUS DomFiltUS , AK , RD , GDP i t 1 t i t i JP JP JP DomFiltJP f JP DomFiltJP , AK , RD , GDP i t 1 t i t i EU EU EU DomFiltEU f EU DomFiltEU , AK , RD , GDP i t 1 t i t i ( Subsequent ) Filings at the EPO US Tot US SEPOFiltUS fUS SEPOFiltUS , DomFil , EPOFil , GDP i t 1 t i t i JP Tot JP SEPOFiltJP f JP SEPOFiltJP , DomFil , EPOFil , GDP i t 1 t i t i EU Tot EU SEPOFiltEU f EU SEPOFiltEU , DomFil , E POFil , GDP i t 1 t i t i Tot EU EPOFiltOT fOT SEPOFiltOT , EPOFil , GDP i t i t i EPOFiltTot EPOFiltEU EPOFiltJP EPOFiltUS EPOFiltOT Benchmark Forecasts The Domestic Filing Model - US Specific model of LFDOM_US, 1968 - 2003 Constant LFDOM_US_1 LFDOM_US_2 LFDOM_US_3 LRD3_US_1 LRD3_US_3 LAKD_US_1 LAKD_US_2 LAKD_US_4 dp9596 Trend Benchmark Forecasts Coeff 22.30339 2.50788 0.41380 0.40580 0.80166 -1.29514 -17.30588 13.08304 0.71821 0.12257 0.05670 StdError 5.68230 0.58479 0.17107 0.15110 0.24127 0.34318 5.35807 4.50410 0.41603 0.02417 0.01446 t-value 3.925 4.289 2.419 2.686 3.323 -3.774 -3.230 2.905 1.726 5.072 3.921 The Domestic Filing Model - US RSS LogLik R^2 HQ T 0.02714 129.42304 0.99424 -6.41018 36 Chow(1986:1) Chow(2000:1) AR 1-4 test ARCH 1-4 test hetero test Benchmark Forecasts sigma AIC Radj^2 SC p value 3.1522 0.0387 1.4191 0.0876 20.9696 0.03295 -6.57906 0.99193 -6.09520 11 prob 0.0515 0.9896 0.2623 0.9851 0.3989 The Domestic Filing Model - US Dynamic analysis – LongRun Coefficients LAKD_US SE LRD3_US SE dp9596 SE Constant SE Trend SE Benchmark Forecasts 1.5058 >>> Greater than Unity 0.0712 0.2120 >>> Elasticity .2 0.0573 -0.0527 0.0196 -9.5826 0.9904 -0.0244 0.0039 The EPO Total Model Specific model of LF_TOT, 1982 - 2001 Coeff 7.83067 0.78506 3.05625 0.62121 -0.38245 -5.88867 Constant LF_TOT_2 LAKT_US lrd31_eu_1 lrd31_eu_2 LGDP3_EU_1 RSS LogLik R^2 HQ T 0.00796 78.29397 0.99822 -7.17108 20 Chow(2000:1) normality test Benchmark Forecasts StdError 3.16504 0.05666 0.31196 0.08990 0.07114 0.98197 sigma AIC Radj^2 SC p value 0.2973 0.1066 t-value 2.474 13.855 9.797 6.910 -5.376 -5.997 0.02384 -7.22940 0.99759 -6.93068 6 prob 0.5948 0.9481 t-prob 0.0268 0.0000 0.0000 0.0000 0.0001 0.0000 The EPO Total Model Dynamic analysis LAKT_US SE lrd31_eu SE LGDP3_EU SE Constant SE Benchmark Forecasts Long-Run Effects 1.4219 >>> Greater than Unity 0.4509 Same as Domestic 0.1111 >>> Elasticity < Domestic 0.0611 -2.7397 1.0579 3.6432 2.2689 The Forecasts The aggregate and domestic models were solved dynamically in a stochastic simulation. 1000 repetitions Gauss-Seidel Method The models were fit through 1998 and then used to forecast until 2002 2003 Below the Aggregate model and European Domestic model results are presented graphically as an example. Actual and Forecasts with Confidence Intervals Percent Deviations from Actual Benchmark Forecasts Actual and Forecasts from the Aggregate Total Model 75000 70000 65000 60000 55000 50000 45000 1997 1998 1999 2000 F_EP F_EP (Baseline Mean) Benchmark Forecasts 2001 2002 2003 F_EP_0MH F_EP_0ML Actual and Forecasts from the Aggregate Domestic Model 124000 120000 116000 112000 108000 1997 1998 1999 2000 FDOM_EU FDOM_EU (Baseline Mean) Benchmark Forecasts 2001 2002 2003 FDOM_EU_0MH FDOM_EU_0ML Percent Deviations from Aggregate Model Total EPO 1.2 0.8 0.4 0.0 -0.4 -0.8 -1.2 -1.6 1997 1998 1999 2000 2001 2002 2003 2002 2003 Domestic EPO 1.0 0.5 0.0 -0.5 -1.0 -1.5 -2.0 1997 Benchmark Forecasts 1998 1999 2000 2001 Summary This paper presents preliminary results on forecasting patent applications at the European Patent Office using annual data. An Aggregate (top-down) Model and a Regional (bottom up) Model are developed. The models are econometric and based on endogenous growth theory. The results suggest this is a promising approach to forecasting patent applications at the EPO and useful for decision making. Benchmark Forecasts