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Founded 1348

Charles University. Founded 1348. Austria, Linz 16. – 18. 6. . 2003. Johann Kepler University of Linz. Johann Kepler University of Linz. AN ANALYSIS OF. AN ANALYSIS OF. THE CZECH ECONOMY. THE CZECH ECONOMY. IN TRANSITION. IN TRANSITION. Jan Ámos Víšek. Jan Ámos Víšek. FSV UK.

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Founded 1348

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  1. Charles University Founded 1348

  2. Austria, Linz 16. – 18. 6.. 2003 Johann Kepler University of Linz Johann KeplerUniversity of Linz AN ANALYSIS OF AN ANALYSIS OF THE CZECH ECONOMY THE CZECH ECONOMY IN TRANSITION IN TRANSITION Jan Ámos Víšek Jan Ámos Víšek FSV UK Institute of Economic Studies Faculty of Social Sciences Charles University Prague Institute of Economic Studies Faculty of Social Sciences Charles University Prague STAKAN III STAKAN III

  3. Schedule of today talk A motivation for robust regression (already Francis Ysidro Edgeworth …., 1887) Galileo Galilie (1632) Roger Joseph Bocovich (1757) Pierre Simon Laplace (1793) They utilized -regression The least trimmed squares definiton, properties and how to apply The least weighted squares definition

  4. Schedule of today talk (continued) Analyzing the export from and FDI into the Czech republic – 1994 dividing the industries into two groups - why Looking for the sense of division separating market oriented part from the rest Analyzing the export from the Czech republic - 1993-1999 separating market oriented parts from the rest for each year

  5. Why robust methods in regression ? What about to consider a minimal elipsoid containing a priori given number of observations.

  6. Why robust methods in regression ? continued So the solution seems to be simple !

  7. Why robust methods in regression ? continued I am sorry but we have to invent a more intricate solution. (otherwise we lose some useful information)

  8. Recalling that is breakdown point Minimalnumber of observations which can cause that estimator breaks down. So, for the OLS we have the breakdown point equal zero (asymptotically) !

  9. The method of the least squares is seen to be our best course when we have thrown overboard a certain portion of our data - a sort of sacrifice which has often to be made by those who sail the stormy seas of Probability. Francis Ysidro Edgeworth, 1887

  10. The Least Trimmed Squares - Rousseeuw (1983) One of really applicable 50% breakdown point estimator Let us recall that for any the residuals are given as and that the order statisticsare given by . Then for any . The optimal.

  11. The Least Trimmed Squares Continued Advantages -evidently 50% breakdown point - scale- and regression-equivariant - -consistentand asymptotically normal - nowadays easy to evaluate Disadvantage - high subsample sensitivity, i.e. can be (arbitrarily) high First proposal – based on LMS, in fact, the trimmed least squares. Rousseeuw, Leroy (1987) – PROGRESS It did not work satisfactorily, sometimes very bad. Probably still in S-PLUS, e.g..

  12. How to select hreasonably? Number of points of this „cloud“ is . is only a “bit” smaller than

  13. Algorithm for the case when n is large is described in: Víšek, J.Á. (1996): On high breakdown point estimation. Computational Statistics (1996) 11, 137 – 146. Víšek, J.Á. (2000): On the diversity of estimates Computational Statistics and Data Analysis, 34, (2000), 67 – 89. Čížek, P., J. Á. Víšek (2000): Least trimmed squares. XPLORE, Application guide, 49 – 64. One implementation is available in package XPLORE (supplied by Humboldt University), TURBO-PASCAL-version from me, MATLAB version from my PhD-student Libora Mašíček.

  14. Disadvantage of LTS High subsample sensitivity, i.e. can be rather large (without control by design of experiment) Víšek, J.Á. (1999): The least trimmed squares - random carriers. Bulletin of the Czech Econometric Society, 10/1999, 1 - 30. See also Víšek, J.Á. (1996): Sensitivity analysis of M-estimates. Annals of the Instit. of Statist. Math. 48 (1996), 469 – 495. Sensitivity analysis of M-estimates of nonlinear regression model: Influence of data subsets. Annals of the Institute of Statistical Mathematics, 261 - 290, 2002.

  15. Disadvantage of LTS …… Hence The Least Weighted Squares non-increasing Víšek, J.Á. (2002): The least weighted squares I. The asymptotic linearity of normal equations. Bulletin of the Czech Econometric Society, no.15, 31 - 58, 2002. The least weighted squares II. Consistency and asymptotic normality. Bulletin of the Czech Econometric Society, no. 16, 1 - 28, 2002.

  16. The Czech Republic - small open economy relying on international trade The export into EU increased in nineties from 8 billion US$ to 18.4 billion US$, i.e. annually 16.3%.

  17. IN NUMBERS: Export into EU - 70.7 % 1/3 Germany 1/12 Austria European transition economies - 20.8 % 1/12 Slovakia 1/19 Poland “Rest of world” - 8.4 %

  18. HYPOTHESIS There is an increasing segment of economy which is export oriented - as follows from the previous - oriented on EU. In other words: There is an increasing segment of economy which resembles market economy.

  19. DATA ABOUT THE CZECH ECONOMY 91 industries, nearly 40 variables, year 1994 X - export VA - value added W - wages S - sales K - capital BAL - Balasa index US - number of university students HS - number of high school students TFPW - total factor productivity related to wages DP - price development after opening-up FDI - foreigner direct investments Pattern of variables IRS - increasing return from scale R&D - research and development CR3 - market power (concentration) The goal of analysis – to find determinants of of the EXPORT and FDI

  20. SEARCHING FOR MODEL FOR EXPORT After a lot of experiments we arrived at the model · h=54

  21. Break in estimates of coefficients Collecting results into the table …. (7) (8) (9) (7) (9) (8) (8) Subpopulations nested up to size 57 Selected subpopulation Let us call it“main” subpopulation

  22. Industries in “main” subpopulation crude petroleum, natural gas (111+112), non-ferrous ores (120+132), sand, stones (141+142), chemicals, minerals (143-145), processing and preserving fruits and vegetables (153), animal oil, fats (154), dairy (155), grain mill products, starches (156), feeds (157), beverages, beers (159), textile fibre (171), textile products (175), knitted and crocheted products (177), leather clothes (181), other outwears (182), furs (183), leather dressing (191), bags, luggages (192), foot-wear (193), impreg-nation of wood (201), plywood and laminboard (202), wood-products (203-205), paper products (212), petroleum-processing (232), pharmacy, botanical products (244), man-made fibres (247),rubber (251), plastics (252), prod. of glass, ceramics (262), bricks and baked clay (264), cement, lime and plaster (265-266), cutting, shaping and finishing stones, nonmetallic minerals (267-268), tubes (272), casting of metals (275), tanks, reservoirs, containers and boilers (282-283), knives, tools and metal products (284-287), machinery for production of power (291), machi-nery-tools (294), special and industrial machinery (295), domestic appliances (297), office machinery and computers (300), el. motors, generators and transformers (311), lighting equipment, el. lamp (315), radio and tv transmitters(322), radio, tv receivers, video recording (323), medical, surgical equipment (331), optical instru-ments, photo equipment (334), clocks, watches (335), motor vehicles (341), bicycles, motorcycles (354), furniture (361), gold and jewellery (362), sports goods, games, toys (364 – 365), production, distribution of electricity (401).

  23. For “complementary” subpopulation - n = 37 · h=33 Excluded: textile, ready made garment (174), agro- chemistry (242), musical instruments and records (363+223), weapons, ammunition, n.e.c. (296+366)

  24. Industries in “complementary” subpopulation hard coal (101), lignite and peat (102+103), processing meat and meat products (151), processing fish and fish products (152), (other) food products (158), tobacco (160), textile weaving and the finishing of textiles (172+173), textile articles (174), knitted and crocheted materials (176), impregnation of wood (201), pulp and paper (211), publications and prints (221+222), oven-coke (231), basic chemicals (241), pesticide and agro-chemical products (242), paint-coating prod.(243), soap and detergents (245), manufacture of other chemical products (246), glass and glass products (261), iron and steel (271), metallurgy of iron and steel (273), precious and non-ferrous metals (274), structural metal products (281), other general purpose machinery (292), agriculture and forestry machinery (293), el. distr. equipment and control (312), cables and wires(313), other el. equipment (314-316), electronic components (321), measurement and test. devices(332), control equip- ment (333), trailers and semi-trailers (342),motor vehicles parts and accessories (343-355), buildingand repairing ships and boats (351),railway and tramway locomotives and rollingstock (352), air crafts and space crafts (353), music. instruments and records (363+223), weapons, ammunition, n.e.c. (296 +366).

  25. SEARCHING A MODEL FOR Foreigner Direct Investments Again after a lot of experiments we arrived : Model for “main” subpopulation h = 54 Coefficient of determination = 0.9199 Chi-square =7.834 (8) Subpopulations nested up to the size 56 We decided for 54 due to the increase of sum of squares and partially also due to already known results for export.

  26. Model for “complementary” subpopulation h = 36 Coefficient of determination = 0.5988 Chi-square =8.119 (6) Division of the population of 91 industries into the “main” and “complementary” subpopulation is nearly (except of two industries) the same as for export .

  27. Does the division make any sense? (except of the statistical one that the subpopulations allow to built up reasonable models for EXPORT and for FDI) What about a relation between LABOR and CAPITAL ? (In other words, what about to study production functions in the respective subpopulations?)

  28. DEPENDENCE of standardized capital(K / W) All 91 observations on standardized labor(L / S)

  29. DEPENDENCE of standardized capital(K / W) “Main” subpopulation on standardized labor(L / S)

  30. DEPENDENCE of standardized capital(K / W) “Complementary” subpopulation on standardized labor(L / S)

  31. Taking into account previous graphs, we should try to fit: “Main” subpopulation “Complementary” subpopulation (1) (2) Coeffs of determination of model (1) Coeffs of determination of model (2)

  32. Taking into account that IRS was significant factor for FDI-models: CES Estimates for “main” subpopulation Estimates for “complementary” subpopulation

  33. Conclusion from the analysis of 1994-data: There was (already in 1994) a part of economy which had standard production function, i.e. it was probably already market-economy-oriented group of industries.

  34. DATA ABOUT THE CZECH ECONOMY 61 industries, only 8 variables, years 1993 - 99 X - export M - import PI - import prices PE - export prices VA - value added K - capital L - labor DE - debts FDI - foreigner direct investments TAR - tariffs from EU into the Czech republic EU TAR - tariffs from the Czech republic into EU CZ The goal of analysis - to find a model for theEXPORT and for the IMPORT. (only EXPORT will be referred)

  35. Of course, the data were processed as panel data ….. Result: by White estimate …but also per years !

  36. Similar analysis, as was presented for 1994, was carried out for every year starting with 1993 to 1999. As we wanted to see a possible development of common factors (common for all years) in time, we tried to find factors which are significant (or nearly significant) throughout the whole studied period. We arrived at the model

  37. Example of processing data for 1993 p-values of the respective tests For this sizes TSP did not give p-values

  38. Optimal models for individual years 1993 – 1999 All explanatory variables are significant throughout 1993 – 99 except of log(DE/VA) in years 1996 and 1998.

  39. Other characteristics of the “optimal” models for 1993 – 1999 p-values of respective tests

  40. List of atypical industries Size of subpopulation Coffee, tea Tobacco Other food Animal oil Coal, coke Seeds Drinks Meat Sugar Pulp Pelt Corn Iron Gas Year

  41. List of atypical industries continued Size of subpopulation Manufac. plastics Business machine Transport equip. Organ.chemistr. Manufac.oil Parfumery Vegetable oil Metal prod. Pharmacy Iron, steel Leathers Plastics Wood Shoes Year

  42. 55 54 54 54 49 48 37 Saving packages Floating exchange rate Exaggerating a bit we may say: SIZES OF SUBPOPULATIONS Despite the government measures the economy is able to help itself. WHICH WERE SELECTED

  43. THANKS for ATTENTION

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