Appendices. Chile models. Appendix
|
|
- Harry Griffith
- 6 years ago
- Views:
Transcription
1 Appendices Appendix Chile models Table 1 New Philips curve Dependent Variable: DLCPI Date: 11/15/04 Time: 17:23 Sample(adjusted): 1997:2 2003:4 Included observations: 27 after adjusting endpoints Kernel: Bartlett, Bandwidth: Fixed (2), No prewhitening Convergence achieved after: 18 weight matrices, 19 total coef iterations Instrument list: DLCPI(-1 TO -4) SHLAB GDPGAP DLWPXC DLUSD Variable LABGAP DLCPI(1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Table 2 Non-linear estimation of the structural parameters c(1) share of firms which do not change prices; c(2) the discount factor Date: 11/17/04 Time: 08:48 Sample(adjusted): 1997:2 2003:4 Included observations: 27 after adjusting endpoints Kernel: Bartlett, Bandwidth: Fixed (2), No prewhitening Convergence achieved after: 14 weight matrices, 15 total coef iterations C(1)*DLCPI - (1-C(1))*(1-C(2)*C(1))*LABGAP-C(1)*C(2)*DLCPI(1) Instrument list: DLCPI(-1 TO -4) SHLAB GDPGAP DLUSD DLWPXC C(1) C(2) S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Table 3 Hybrid Philips curve estimation Date: 11/17/04 Time: 10:32 Sample(adjusted): 1997:2 2003:4 Included observations: 27 after adjusting endpoints Kernel: Bartlett, Bandwidth: Fixed (2), No prewhitening Convergence achieved after: 28 weight matrices, 29 total coef iterations (C(1)+C(3)*(1-C(1)*(1-C(2))))*DLCPI -(1-C(3))*(1-C(1))*(1-C(1)*C(2)) *LABGAP -C(1)*C(2)*DLCPI(1) -C(3)*DLCPI(-1) Instrument list: DLDEFL96SA(-1 TO -4) SHLAB GDPGAP DLWPXC DLUSD SI
2 C(1) C(3) C(2) S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Data description: Data sources: IFS; Statistical office of Chile ( ), Central Bank of Chile ( Quarterly data E-views variable Description Stationarity cpi CPI index I(2) dlcpi Dlcpi=lcpi-lcpi(-1) I(0) gdp96 GDP at 1996 prices I(1) gdpc GDP current prices I(2) M1 M1 aggregate current prices I(2) empl People employed I(2) policy_rate Policy rate of the Central bank I(1) wage Nominal average monthly wage I(1) wpxc World price index I(1) *Unit root tests performed with ADF Monthly data E-views variable Description Stationarity cpi Monthly CPI I(1) policy rate CB policy rate wage Average monthly wage (nominal) I(2) m1 m1 aggregate (nominal) I(2) *All unit root tests were performed by a combined ADF and KPSS test. 2
3 Switzerland Appendix S1: DLPGDP first difference of the log GDPdeflator DLPGDPSA first difference of the log GDPdeflator (s.a.) DLUSD first difference of the log USD/CHF DLWPXC first difference of the log world commodity price index GDPGAP trend deviation of the real seasonally adjusted GDP (in 2000 prices) IS interest rate spread (lending minus deposits rate) DLM3 first difference of the log M3 (broad money aggregate) Appendix S2 (SW estim. of eq. Baseline NKPC) Appendix S3 (SW estim of non-linear GMM with mom. Cond. (3) ) Specification (3) Date: 11/17/04 Time: 18:00 Sample(adjusted): 1982:1 2003:4 Included observations: 88 after adjusting endpoints Convergence achieved after: 23 weight matrices, 24 total coef iterations C(1)*DLPGDPSA-(1-C(1))*(1-C(1)*C(2))*GDPGAP-C(1)*C(2) *DLPGDPSA(1) Instrument list: DLPGDPSA(-1 TO -7) IS GDPGAP DLWPXC DLUSD C(1) C(2) S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Specification (4) Date: 11/17/04 Time: 18:00 Sample(adjusted): 1981:2 2003:4 Included observations: 91 after adjusting endpoints Convergence not achieved after: 499 weight matrices, 500 total coef iterations DLPGDPSA-((1-C(1))*(1-C(1)*C(2))/C(1))*GDPGAP-C(1)*C(2) *DLPGDPSA(1) Instrument list: DLPGDPSA(-1 TO -4) IS GDPGAP DLWPXC DLUSD C(1) C(2) S.E. of regression Sum squared resid
4 Durbin-Watson stat J-statistic Appendix S4 (SW estim of hybrid NKPC with BOTH mom. Conds) Specification (6) Date: 11/17/04 Time: 18:15 Sample(adjusted): 1982:1 2003:4 Included observations: 88 after adjusting endpoints Convergence achieved after: 15 weight matrices, 16 total coef iterations (C(1)+C(3)*(1-C(1)*(1-C(2))))*DLPGDPSA-(1-C(3))*(1-C(1))*(1- C(1)*C(2)) *GDPGAP-C(1)*C(2)*DLPGDPSA(1)-C(3)*DLPGDPSA(-1) Instrument list: DLPGDPSA(-1 TO -7) IS GDPGAP DLWPXC DLUSD C(1) C(3) C(2) S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Specification (7) Date: 11/12/04 Time: 18:03 Sample(adjusted): 1982:1 2003:4 Included observations: 88 after adjusting endpoints Convergence achieved after: 20 weight matrices, 21 total coef iterations DLPGDPSA-(1-C(3))*(1-C(1))*(1-C(1)*C(2))/(C(1)+C(3)*(1-C(1)*(1- C(2)))) *GDPGAP-C(1)*C(2)/(C(1)+C(3)*(1-C(1)*(1-C(2))))*DLPGDPSA(1) -C(3)/(C(1)+C(3)*(1-C(1)*(1-C(2))))*DLPGDPSA(-1) Instrument list: DLPGDPSA(-1 TO -7) IS GDPGAP DLWPXC DLUSD C(3) C(1) C(2) S.E. of regression Sum squared resid Durbin-Watson stat J-statistic
5 Appendices for Thailand Appendix T1: Thailand Variables Description Code Description of the Variable Measure Data Period Frequency No of observations cpi Consumer Price Index, CPI 2000 = :1-2004:3 quarterly 159 dlcpi Difference of the logs of the CPI ~ (% deviation) / 100, for small 1965:2-2004:3 quarterly 158 values dlcpisa Difference of the logs of the CPI seasonally adjusted :2-2004:3 quarterly 158 dlm1 Difference of the logs of M :2-2004:2 quarterly 157 dlm3 Difference of the logs of M :2-2004:2 quarterly 157 dlpgdpsa Difference of the logs of the seasonally adjusted GDP deflator :2-2004:2 quarterly 45 dlusd Difference of the logs of USD :2-2004:3 quarterly 158 dlwage Difference of the logs of wages :2-2004:3 quarterly 22 dlwagesa Difference of the logs of wages, seasonally adjusted :2-2004:3 quarterly 22 dlwpxc Difference of the logs of WPXC :2-2003:4 quarterly 135 empl Employment Thousands, p.a. 1998:1-2004:3 quarterly 27 emplsa Employment, seasonally adjusted Thousands, p.a. 1998:1-2004:3 quarterly 27 gdp Gross Domestic Product, GDP Billions of Baht 1993:1-2004:2 quarterly 46 gdpgap Trend deviation of the real seasonally adjusted GDP Millions of Baht, 2000 prices 1993:1-2004:2 quarterly 46 gdpr Real GDP Millions of Baht, 2000 prices 1993:1-2004:2 quarterly 46 gdprsa Real GDP, seasonally adjusted :1-2004:2 quarterly 46 gdprsahp H-P filter applied to the seasonally adjusted real GDP :1-2004:2 quarterly 46 gdpsa Seasonally adjusted GDP Billions of Baht 1993:1-2004:2 quarterly 46 is Interest spread = lending - deposit banking rates % 1977:1-2004:3 quarterly 111 lcpi log of CPI 1965:1-2004:3 quarterly 159 lcpisa log of CPI, s.a. 1965:1-2004:3 quarterly 159 lm1 log of M1 1965:1-2004:2 quarterly 159 lm3 log of M3 1965:1-2004:2 quarterly 159 lpgdp log of GDP deflator 1993:1-2004:2 quarterly 46 lpgdpsa log of GDP deflator, s.a. 1993:1-2004:2 quarterly 46 lppi log of PPI 1965:1-2004:3 quarterly 159 lusd log of USD 1965:1-2004:3 quarterly 159 lwage log of wages 1999:1-2004:3 quarterly 23 lwagesa log of wages, s.a. 1999:1-2004:3 quarterly 23 lwpxc log of WPXC 1970:1-2003:4 quarterly 136 m1 M1, "Money" from IFS Billions of Baht, e.p. 1965:1-2004:2 quarterly 158 m3 M3, "Money + Quasi Money" from IFS Billions of Baht, e.p. 1965:1-2004:2 quarterly 158 pgdp GDP deflator 2000 = :1-2004:2 quarterly 46 ppi Producer Price Index, PPI 2000 = :1-2004:3 quarterly 159 shgap Labor Share Gap = trend deviation of shlabsa (% / 100) 1999:1-2004:2 quarterly 22 shlabsa Labor Share, s.a. = (emplsa*wagesa)/(gdpsa* ) (% / 100) 1999:1-2004:2 quarterly 22 shlabsahp H-P filter of Labor Share, s.a. (% / 100) 1999:1-2004:2 quarterly 22 usd Official Exchange Rate, p.a. Baht / USD 1965:1-2004:3 quarterly 159 wage Wages Baht, p.a. 1999:1-2004:3 quarterly 23 wagesa Wages, s.a. Baht, p.a. 1999:1-2004:3 quarterly 23 wpxc World Price Index of Commodities, WPXC 2002 = :1-2003:4 quarterly 136 Notes: All the data is at quarterly frequency 5
6 Code Description of the Variable Measure Data Period Frequency p.a. - period average e.p. - end of period s.a. - seasonally adjusted No of observations Appendix T2: Thailand Unit Root Tests Variables ADF in levels ADF in 1 differences ADF in 2 differences Determined order t-statistic p-value` t-statistic p-value` t-statistic p-value` of integration GDPGAP I(0) IS I(0) LCPI I(1) LM I(1) LM I(2) LPGDP I(1)* LPGDPSA I(1) LPPI I(1) LUSD I(1) LWPXC I(1) SHGAP I(0) Notes: `MacKinnon (1996) one-sided p-values ``The test is performed with or without a constant and/or a trend term according to the most appropriate specification for the variable. Generally the trend term is excluded and the constant not. ADF test-statistic is significant at 1%, * ADF test-statistic is significant at 5% Appendix T3: Reduced form of the NKPC Dependent Variable: DLPGDPSA Date: 11/17/04 Time: 14:29 Sample(adjusted): 1993:4 2003:4 Included observations: 41 after adjusting endpoints Convergence achieved after: 8 weight matrices, 9 total coef iterations Instrument list: C DLPGDPSA(-1 TO -2) IS DLWPXC DLUSD Variable GDPGAP -5.41E E DLPGDPSA(1) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Test for the validity of the overidentifying restrictions: test statistic=2.17, p-value=0.30, i.e. the overidentifying restrictions are valid. Appendix T4: Non-linear GMM, specification 1 6
7 Date: 11/17/04 Time: 18:36 Sample(adjusted): 1994:4 2003:4 Included observations: 37 after adjusting endpoints Convergence achieved after: 38 weight matrices, 39 total coef iterations C(1)*DLPGDPSA-(1-C(1))*(1-C(1)*C(2))*GDPGAP-C(1)*C(2) *DLPGDPSA(1) Instrument list: C DLPGDPSA(-1 TO -6) IS DLWPXC DLUSD C(1) E C(2) S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Test for the validity of the overidentifying restrictions: test statistic=3.10, p-value=0.07, i.e. the overidentifying restrictions are valid. Appendix T5: Non-linear GMM, specification 2 Date: 11/17/04 Time: 18:38 Sample(adjusted): 1994:4 2003:4 Included observations: 37 after adjusting endpoints Convergence achieved after: 17 weight matrices, 18 total coef iterations DLPGDPSA-(1-C(1))*(1-C(1)*C(2))/C(1)*GDPGAP-C(2)*DLPGDPSA(1) Instrument list: C DLPGDPSA(-1 TO -6) IS DLWPXC DLUSD C(1) E C(2) S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Test for the validity of the overidentifying restrictions: test statistic=3.10, p-value=0.07, i.e. the overidentifying restrictions are valid. 7
8 Appendix T6: Wald Coefficient Restrictions Test Wald Test: Equation: EQ02A Test Statistic Value df Probability F-statistic (1, 35) Chi-square Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. (1 - C(1)) * (1 - C(1)*C(2)) / C(1) -9.92E E-08 Delta method computed using analytic derivatives. Appendix T7: Hybrid NKPC, specification 1 Date: 11/17/04 Time: 19:17 Sample(adjusted): 1994:4 2003:4 Included observations: 37 after adjusting endpoints Convergence achieved after: 94 weight matrices, 95 total coef iterations (C(1)+C(3)*(1-C(1)*(1-C(2))))*DLPGDPSA-(1-C(3))*(1-C(1))*(1-C(1)*C(2)) *GDPGAP-C(1)*C(2)*DLPGDPSA(1)-C(3)*DLPGDPSA(-1) Instrument list: C DLPGDPSA(-1 TO -6) IS DLWPXC DLUSD C(1) E C(3) C(2) S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Test for the validity of the overidentifying restrictions: test statistic=3.10, p-value=0.12, i.e. the overidentifying restrictions are valid. 8
9 Appendix T8: Hybrid NKPC, specification 2 Date: 11/18/04 Time: 11:37 Sample(adjusted): 1994:3 2003:4 Included observations: 38 after adjusting endpoints Convergence achieved after: 54 weight matrices, 55 total coef iterations DLPGDPSA-(1-C(3))*(1-C(1))*(1-C(1)*C(2))/(C(1)+C(3)*(1-C(1)*(1- C(2)))) *GDPGAP-C(1)*C(2)/(C(1)+C(3)*(1-C(1)*(1-C(2))))*DLPGDPSA(1) -C(3)/(C(1)+C(3)*(1-C(1)*(1-C(2))))*DLPGDPSA(-1) Instrument list: C DLPGDPSA(-1 TO -5) IS DLWPXC DLUSD C(3) C(1) E C(2) S.E. of regression Sum squared resid Durbin-Watson stat J-statistic Test for the validity of the overidentifying restrictions: test statistic=3.61, p-value=0.27, i.e. the overidentifying restrictions are valid. 9
10 Appendix TF1: Correlogram of DLCPI Date: 11/18/04 Time: 13:50 Sample: Included observations: 158 Autocorrelation Partial Correlation AC PAC Q-Stat Prob. *****. ***** ****. * *** ***. * ** * * * * * *. * * * *. * *. * * Appendix TF2: ARMA(6,5) Dependent Variable: DLCPI Method: Least Squares Date: 11/18/04 Time: 14:02 Sample(adjusted): 1966:4 2004:3 Included observations: 152 after adjusting endpoints Convergence achieved after 14 iterations Backcast: 1965:3 1966:3 Variable C AR(1) AR(2) AR(3) AR(4) AR(5) AR(6) MA(5) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) Inverted AR Roots i i i i -.99 Inverted MA Roots i i i i
11 Appendix TF3: ARMA Dynamic, In-Sample Forecast Forecast: DLCPI_ARMA Actual: DLCPI Forecast sample: 2003:1 2004:2 Included observations: 6 Root Mean Squared Error Mean Absolute Error Mean Abs. Percent Error Theil Inequality Coefficient Bias Proportion Variance Proportion Covariance Proportion :1 2003:2 2003:3 2003:4 2004:1 2004:2 DLCPI_ARMA Appendix TF4: ARMAX(3,3,3) Dependent Variable: DLCPI Method: Least Squares Date: 11/18/04 Time: 16:32 Sample(adjusted): 1966:4 2004:3 Included observations: 152 after adjusting endpoints Convergence achieved after 24 iterations Backcast: 1966:1 1966:3 Variable C DLM1(-1) DLM1(-2) DLM1(-3) AR(1) AR(2) AR(3) MA(1) MA(2) MA(3) R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood F-statistic Durbin-Watson stat Prob(F-statistic) Inverted AR Roots i i Inverted MA Roots i i 11
12 Appendix TF5: ARMAX(3,3,3) Dynamic, In-Sample Forecast Forecast: DLCPI_ARMAX Actual: DLCPI Forecast sample: 2003:1 2004:2 Included observations: 6 Root Mean Squared Error Mean Absolute Error Mean Abs. Percent Error Theil Inequality Coefficient Bias Proportion Variance Proportion Covariance Proportion :1 2003:2 2003:3 2003:4 2004:1 2004:2 DLCPI_ARMAX Appendix TF6: VAR Vector Autoregression Estimates Date: 11/18/04 Time: 14:37 Sample(adjusted): 1993:2 2004:2 Included observations: 45 after adjusting endpoints Standard errors in ( ) & t-statistics in [ ] DLCPI GDPGAP DLUSD DLM1 DLCPI(-1) ( ) ( ) ( ) ( ) [ ] [ ] [ ] [ ] GDPGAP(-1) 4.95E E E-07 (2.0E-08) ( ) (1.8E-07) (2.7E-07) [ ] [ ] [ ] [ ] DLUSD(-1) ( ) ( ) ( ) ( ) [ ] [ ] [ ] [ ] DLM1(-1) ( ) ( ) ( ) ( ) [ ] [ ] [ ] [ ] C ( ) ( ) ( ) ( ) [ ] [ ] [ ] [ ] R-squared Adj. R-squared Sum sq. resids E S.E. equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D. dependent Determinant Residual Covariance
13 Log Likelihood (d.f. adjusted) Akaike Information Criteria Schwarz Criteria Appendix TF7: AR Roots Graph 1.5 Inverse Roots of AR Characteristic Polynomial Appendix TF8: VAR Lag order selection criteria VAR Lag Order Selection Criteria Endogenous variables: DLCPI GDPGAP DLUSD DLM1 Exogenous variables: C Date: 11/18/04 Time: 16:54 Sample: 1965:1 2005:4 Included observations: 41 Lag LogL LR FPE AIC SC HQ NA * * * * * * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion 13
14 Appendix TF9: Serial Correlation LM Test VAR Residual Serial Correlation LM Tests H0: no serial correlation at lag order h Date: 11/18/04 Time: 16:55 Sample: 1965:1 2005:4 Included observations: 45 Lags LM-Stat Prob Probs from chi-square with 16 df. Appendix TF10: Residual Heteroskedasticity Test VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares) Date: 11/18/04 Time: 16:57 Sample: 1965:1 2005:4 Included observations: 45 Joint test: Chi-sq df Prob Individual components: Dependent R-squared F(8,36) Prob. Chi-sq(8) Prob. res1*res res2*res res3*res res4*res res2*res res3*res res3*res res4*res res4*res res4*res
A.) Testy na jednotkové korene - DF test
PRÍLOHY PRÍLOHY 2 A.) Testy na jednotkové korene - DF test ADF Test Statistic -1.461475 1% Critical Value* -3.6496 Dependent Variable: D(LOGC95) ADF Test Statistic -5.708024 1% Critical Value* -3.6576
More informationProcedia - Social and Behavioral Sciences 195 ( 2015 ) World Conference on Technology, Innovation and Entrepreneurship
Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Sciences 195 ( 215 ) 776 782 World Conference on Technology, Innovation and Entrepreneurship Technological Progress,
More informationConstruction of SARIMAXmodels
SYSTEMS ANALYSIS LABORATORY Construction of SARIMAXmodels using MATLAB Mat-2.4108 Independent research projects in applied mathematics Antti Savelainen, 63220J 9/25/2009 Contents 1 Introduction...3 2 Existing
More informationU.S. Employment Growth and Tech Investment: A New Link
U.S. Employment Growth and Tech Investment: A New Link Rajeev Dhawan and Harold Vásquez-Ruíz Economic Forecasting Center J. Mack Robinson College of Business Georgia State University Preliminary Draft
More informationWeb Appendix: Online Reputation Mechanisms and the Decreasing Value of Chain Affiliation
Web Appendix: Online Reputation Mechanisms and the Decreasing Value of Chain Affiliation November 28, 2017. This appendix accompanies Online Reputation Mechanisms and the Decreasing Value of Chain Affiliation.
More informationForecasting Exchange Rates using Neural Neworks
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 6, Number 1 (2016), pp. 35-44 International Research Publications House http://www. irphouse.com Forecasting Exchange
More informationThe Relative Performance of Conditional Volatility Models
Master Thesis 15 ECTS Autumn, 2014 The Relative Performance of Conditional Volatility Models - An Empirical Evaluation on the Nordic Equity Markets Author: Kristoffer Blomqvist Supervisor: Bujar Huskaj
More informationINNOVATION AND ECONOMIC GROWTH CASE STUDY CHINA AFTER THE WTO
INNOVATION AND ECONOMIC GROWTH CASE STUDY CHINA AFTER THE WTO Fatma Abdelkaoui (Ph.D. student) ABSTRACT Based on the definition of the economic development given by many economists, the economic development
More informationLampiran 1: Data Investasi Perusahaan GE, US, GM dan WEST
Lampiran 1: Data Investasi Perusahaan GE, US, GM dan WEST Tahun GE US I F C I F C 1935 33.10 1170.60 97.80 209.90 1362.40 53.80 1936 45.00 2015.80 104.40 355.30 1807.10 50.50 1937 77.20 2803.30 118.00
More informationReal-time Forecast Combinations for the Oil Price
Crawford School of Public Policy CAMA Centre for Applied Macroeconomic Analysis Real-time Forecast Combinations for the Oil Price CAMA Working Paper 38/2018 August 2018 Anthony Garratt University of Warwick
More informationPrices of digital cameras
Prices of digital cameras The August 2012 issue of Consumer Reports included a report on digital cameras. The magazine listed 60 cameras, all of which were recommended by them, divided into six categories
More informationReal-time conditional forecasting with Bayesian VARs. VARs: An application to New Zealand
Real-time conditional forecasting with Bayesian VARs: An application to New Zealand Economics Department - Reserve Bank of New Zealand 9 CEF Conference Overview Methodology Data VAR Large Large structural
More informationSimulated Statistics for the Proposed By-Division Design In the Consumer Price Index October 2014
Simulated Statistics for the Proposed By-Division Design In the Consumer Price Index October 2014 John F Schilp U.S. Bureau of Labor Statistics, Office of Prices and Living Conditions 2 Massachusetts Avenue
More informationAnalysis of Economic Data
Analysis of Economic Data CHUNG-MING KUAN Department of Finance & CRETA National Taiwan University September 14, 2014 C.-M. Kuan (Finance & CRETA, NTU) Analysis of Economic Data September 14, 2014 1 /
More informationFebruary 24, [Click for Most Updated Paper] [Click for Most Updated Online Appendices]
ONLINE APPENDICES for How Well Do Automated Linking Methods Perform in Historical Samples? Evidence from New Ground Truth Martha Bailey, 1,2 Connor Cole, 1 Morgan Henderson, 1 Catherine Massey 1 1 University
More informationAlberta Reliability Standard Frequency Response and Frequency Bias Setting BAL-003-AB-1.1
1. Purpose The purpose of this reliability standard is to: (a) require sufficient frequency response from the ISO to maintain Interconnection frequency within predefined bounds by arresting frequency deviations
More informationMiguel I. Aguirre-Urreta
RESEARCH NOTE REVISITING BIAS DUE TO CONSTRUCT MISSPECIFICATION: DIFFERENT RESULTS FROM CONSIDERING COEFFICIENTS IN STANDARDIZED FORM Miguel I. Aguirre-Urreta School of Accountancy and MIS, College of
More informationStock Market Indices Prediction Using Time Series Analysis
Stock Market Indices Prediction Using Time Series Analysis ALINA BĂRBULESCU Department of Mathematics and Computer Science Ovidius University of Constanța 124, Mamaia Bd., 900524, Constanța ROMANIA alinadumitriu@yahoo.com
More informationMay 10, 2016, NSF-Census Research Network, Census Bureau. Research supported by NSF grant SES
A 2016 View of 2020 Census Quality, Costs, Benefits Bruce D. Spencer Department of Statistics and Institute for Policy Research Northwestern University May 10, 2016, NSF-Census Research Network, Census
More informationThe following definitions are derived from the online help of VentureSource. 1023
Appendix A Definitions from VentureSource The following definitions are derived from the online help of VentureSource. 1023 A.1 Venture Financing Round Types Seed Round: Seed rounds are initial rounds
More informationPOWER ADDED EFFICIENCY MODEL FOR MESFET CLASS E POWER AMPLIFIER USING JACKKNIFE RESAMPLING
POWER ADDED EFFICIENCY MODEL FOR MESFET CLASS E POWER AMPLIFIER USING JACKKNIFE RESAMPLING Fouziah Md. Yassin *, Noraini Abdullah, Zainodin H. J and Saturi Baco Physics with Electronics Programme, Mathematics
More informationTHE RELATIONSHIP BETWEEN PRIVATE EQUITY AND ECONOMIC GROWTH
ISSN 1392-1258. ekonomika 2015 Vol. 94(1) THE RELATIONSHIP BETWEEN PRIVATE EQUITY AND ECONOMIC GROWTH Karolis Gudiškis *, Laimutė Urbšienė Vilnius University, Lithuania Abstract. The purpose of this paper
More informationThe Effect of Technical and Non-technical Aid on the Economic Growth of Bangladesh and other Developing Countries
The Effect of Technical and Non-technical Aid on the Economic Growth of Bangladesh and other Developing Countries Project Management Coordinator Hifab International AB MS in Economics, North South University
More informationROBUST DESIGN -- REDUCING TRANSMITTED VARIATION:
ABSTRACT ROBUST DESIGN -- REDUCING TRANSMITTED VARIATION: FINDING THE PLATEAUS VIA RESPONSE SURFACE METHODS Patrick J. Whitcomb Mark J. Anderson Stat-Ease, Inc. Stat-Ease, Inc. Hennepin Square, Suite 48
More informationBIOS 312: MODERN REGRESSION ANALYSIS
BIOS 312: MODERN REGRESSION ANALYSIS James C (Chris) Slaughter Department of Biostatistics Vanderbilt University School of Medicine james.c.slaughter@vanderbilt.edu biostat.mc.vanderbilt.edu/coursebios312
More information(i) Understanding the basic concepts of signal modeling, correlation, maximum likelihood estimation, least squares and iterative numerical methods
Tools and Applications Chapter Intended Learning Outcomes: (i) Understanding the basic concepts of signal modeling, correlation, maximum likelihood estimation, least squares and iterative numerical methods
More informationPRICES OF THE LIBERTY STANDING QUARTER
This document deals with the prices paid by collectors for quarters in the Liberty standing set, issued between 1916 and 1930. Year / Mint / Type Mintage Value 1916 52,000 14,690 1917 Type 1 8,740,000
More informationONLINE APPENDIX: SUPPLEMENTARY ANALYSES AND ADDITIONAL ESTIMATES FOR. by Martha J. Bailey, Olga Malkova, and Zoë M. McLaren.
ONLINE APPENDIX: SUPPLEMENTARY ANALYSES AND ADDITIONAL ESTIMATES FOR DOES ACCESS TO FAMILY PLANNING INCREASE CHILDREN S OPPORTUNITIES? EVIDENCE FROM THE WAR ON POVERTY AND THE EARLY YEARS OF TITLE X by
More informationDOES INFORMATION AND COMMUNICATION TECHNOLOGY DEVELOPMENT CONTRIBUTES TO ECONOMIC GROWTH?
DOES INFORATION AND COUNICATION TECHNOLOGY DEVELOPENT CONTRIBUTES TO ECONOIC GROWTH? 1 ARYA FARHADI, 2 RAHAH ISAIL 1 Islamic Azad University, obarakeh Branch, Department of Accounting, Isfahan, Iran 2
More informationSignal segmentation and waveform characterization. Biosignal processing, S Autumn 2012
Signal segmentation and waveform characterization Biosignal processing, 5173S Autumn 01 Short-time analysis of signals Signal statistics may vary in time: nonstationary how to compute signal characterizations?
More informationExamining the Link Between U.S. Employment Growth and Tech Investment
Examining the Link Between U.S. Employment Growth and Tech Investment Rajeev Dhawan and Harold Vásquez-Ruíz Economic Forecasting Center J. Mack Robinson College of Business Georgia State University August
More informationExploitation, Exploration and Innovation in a Model of Endogenous Growth with Locally Interacting Agents
DIMETIC Doctoral European Summer School Session 3 October 8th to 19th, 2007 Maastricht, The Netherlands Exploitation, Exploration and Innovation in a Model of Endogenous Growth with Locally Interacting
More informationDepartment of Mechanical and Aerospace Engineering. MAE334 - Introduction to Instrumentation and Computers. Final Examination.
Name: Number: Department of Mechanical and Aerospace Engineering MAE334 - Introduction to Instrumentation and Computers Final Examination December 12, 2002 Closed Book and Notes 1. Be sure to fill in your
More informationThe Effects of Industrial Sector and Location on Venture-Backed United States Companies,
The Effects of Industrial Sector and Location on Venture-Backed United States Companies, 1995-2008 Dr. Yochanan Shachmurove Department of Economics The City College of the City University of New York,
More informationSUPPLEMENT TO THE PAPER TESTING EQUALITY OF SPECTRAL DENSITIES USING RANDOMIZATION TECHNIQUES
SUPPLEMENT TO THE PAPER TESTING EQUALITY OF SPECTRAL DENSITIES USING RANDOMIZATION TECHNIQUES CARSTEN JENTSCH AND MARKUS PAULY Abstract. In this supplementary material we provide additional supporting
More informationHarmonic Analysis. Purpose of Time Series Analysis. What Does Each Harmonic Mean? Part 3: Time Series I
Part 3: Time Series I Harmonic Analysis Spectrum Analysis Autocorrelation Function Degree of Freedom Data Window (Figure from Panofsky and Brier 1968) Significance Tests Harmonic Analysis Harmonic analysis
More informationMacroeconomic Determinants of Technological Progress in Major Eurozone Member Countries
International Journal of Economic Practices and Theories, Vol. 5, No. 5, 015 (October), Special issue on Trends Macroeconomic Determinants of Technological Progress in Major Eurozone Member Countries by
More informationDISCUSSION PAPERS IN ECONOMICS
DISCUSSION PAPERS IN ECONOMICS No. 2018/2 ISSN 1478-9396 TRENDS IN INCOME INEQUALITY YOUSEF MAKHLOUF JANUARY 2018 DISCUSSION PAPERS IN ECONOMICS At Nottingham Business School, our Working Papers Series
More informationROOT MULTIPLE SIGNAL CLASSIFICATION SUPER RESOLUTION TECHNIQUE FOR INDOOR WLAN CHANNEL CHARACTERIZATION. Dr. Galal Nadim
ROOT MULTIPLE SIGNAL CLASSIFICATION SUPER RESOLUTION TECHNIQUE FOR INDOOR WLAN CHANNEL CHARACTERIZATION Dr. Galal Nadim BRIEF DESCRIPTION The root-multiple SIgnal Classification (root- MUSIC) super resolution
More informationInnovation Networks and Foreign Firms in Developing Countries: The Turkish Case
Innovation Networks and Foreign Firms in Developing Countries: The Turkish Case Erol Taymaz & Aykut Lenger Middle East Technical University (METU), Department of Economics, 06531 Ankara Turkey 1. Outline
More informationONLINE APPENDIX FOR UNBUNDLING THE INCUMBENT: EVIDENCE FROM UK BROADBAND
ONLINE APPENDIX FOR UNBUNDLING THE INCUMBENT: EVIDENCE FROM UK BROADBAND Mattia Nardotto University of Cologne Frank Verboven KU Leuven and Telecom ParisTech Tommaso Valletti Imperial College London and
More informationESTIMATING THE LONG RUN RELATIONSHIP BETWEEN INCOME INEQUALITY AND ECONOMIC DEVELOPMENT
ESTIMATING THE LONG RUN RELATIONSHIP BETWEEN INCOME INEQUALITY AND ECONOMIC DEVELOPMENT Tuomas Malinen University of Helsinki Discussion Paper No. 634:2008 ISBN 978-952-10-4823-4, ISSN 0357-3257 August
More informationproc plot; plot Mean_Illness*Dose=Dose; run;
options pageno=min nodate formdlim='-'; Title 'Illness Related to Dose of Therapeutic Drug'; run; data Lotus; input Dose N; Do I=1 to N; Input Illness @@; output; end; cards; 0 20 101 101 101 104 104 105
More informationAppendix 1. SAS Routines to determine MAXIMS curves for milkfish
Appendix 1 SAS Routines to determine MAXIMS curves for milkfish October 1996 data a input X Y cards 6.00 0.685958 6.00 1.355671 6.00 1.545187 6.00 0.448360 6.00 0.723689 7.00 0.790480 7.00 0.817858 7.00
More informationG.J.C.M.P.,Vol.5(3):43-50 (May-June, 2016) ISSN:
,Vol.5(3):43-5 (May-June, 216) ISSN: 2319 7285 Entrepreneurship and Economic Growth in Nigeria: Evidence from Small and Medium Scale Enterprises (SMEs) Financing using Asymmetric Auto- Regressive Distributed
More informationHEALTH CARE EXPENDITURE IN AFRICA AN APPLICATION OF SHRINKAGE METHODS
Vol., No., pp.1, May 1 HEALTH CARE EXPENDITURE IN AFRICA AN APPLICATION OF SHRINKAGE METHODS Emmanuel Thompson Department of Mathematics, Southeast Missouri State University, One University Plaza, Cape
More informationStandard BAL Frequency Response and Frequency Bias Setting
A. Introduction Title: and Frequency Bias Setting Number: BAL-003-1 Purpose: To require sufficient from the Balancing (BA) to maintain Interconnection Frequency within predefined bounds by arresting frequency
More informationE-Training on GDP Rebasing
1 E-Training on GDP Rebasing October, 2018 Session 6: Linking old national accounts series with new base year Economic Statistics and National Accounts Section ACS, ECA Content of the presentation Introduction
More informationState-Space Models with Kalman Filtering for Freeway Traffic Forecasting
State-Space Models with Kalman Filtering for Freeway Traffic Forecasting Brian Portugais Boise State University brianportugais@u.boisestate.edu Mandar Khanal Boise State University mkhanal@boisestate.edu
More informationDepartment of Statistics and Operations Research Undergraduate Programmes
Department of Statistics and Operations Research Undergraduate Programmes OPERATIONS RESEARCH YEAR LEVEL 2 INTRODUCTION TO LINEAR PROGRAMMING SSOA021 Linear Programming Model: Formulation of an LP model;
More informationIES, Faculty of Social Sciences, Charles University in Prague
IMPACT OF INTELLECTUAL PROPERTY RIGHTS AND GOVERNMENTAL POLICY ON INCOME INEQUALITY. Ing. Oksana Melikhova, Ph.D. 1, 1 IES, Faculty of Social Sciences, Charles University in Prague Faculty of Mathematics
More informationSeasonal Adjustment of Weekly Time Series with Application to Unemployment Insurance Claims and Steel Production
Journal of Official Statistics, Vol. 23, No. 2, 2007, pp. 209 221 Seasonal Adjustment of Weekly Time Series with Application to Unemployment Insurance Claims and Steel Production William P. Cleveland 1
More informationAuthor Manuscript Behav Res Methods. Author manuscript; available in PMC 2012 September 01.
NIH Public Access Author Manuscript Published in final edited form as: Behav Res Methods. 2012 September ; 44(3): 806 844. doi:10.3758/s13428-011-0181-x. Four applications of permutation methods to testing
More informationProcedure for ERO Support of Frequency Response and Frequency Bias Setting Standard. Event Selection Process
This procedure outlines the Electric Reliability Organization (ERO) process for supporting the Frequency Response Standard (FRS). A Procedure revision request may be submitted to the ERO for consideration.
More informationGDP as a measure of economic growth
GDP as a measure of economic growth Tera Allas Senior Fellow McKinsey Center for Government May 218 GDP as a measure of economic growth GDP is a useful aggregate indicator, despite its serious drawbacks
More informationCELLULAR NETWORK TRAFFIC PREDICTION USING EXPONENTIAL SMOOTHING METHODS ABSTRACT
How to cite this paper: Tran, Q. T., Li, H., & Trinh, Q. K. (2019). Cellular network traffic prediction using exponential smoothing Methods. Journal of Information and Communication Technology, 18 (1),
More informationFirst-level fmri modeling. UCLA Advanced NeuroImaging Summer School, 2010
First-level fmri modeling UCLA Advanced NeuroImaging Summer School, 2010 Task on Goal in fmri analysis Find voxels with BOLD time series that look like this Delay of BOLD response Voxel with signal Voxel
More informationThe Demand for Money at the Zero Interest Rate Bound
JSPS Grant in Aid for Scientific Research (S) Central Bank Communication Design working paper series No.002 (September 2018) The Demand for Money at the Zero Interest Rate Bound Tsutomu Watanabe Tomoyoshi
More informationDigital Signal Processing
Digital Signal Processing Fourth Edition John G. Proakis Department of Electrical and Computer Engineering Northeastern University Boston, Massachusetts Dimitris G. Manolakis MIT Lincoln Laboratory Lexington,
More informationURL:
Does venture capital really foster innovation? Ana Paula Faria Natália Barbosa 03/ 2013 Does venture capital really foster innovation? Ana Paula Faria Natália Barbosa NIPE * WP 03/ 2013 URL: http://www.eeg.uminho.pt/economia/nipe
More informationDETERMINATES OF CLUSTERING ACROSS AMERICA S NATIONAL PARKS: AN APPLICATION OF THE GINI COEFFICIENT
DETERMINATES OF CLUSTERING ACROSS AMERICA S NATIONAL PARKS: AN APPLICATION OF THE GINI COEFFICIENT R. Geoffrey Lacher Department of Parks, Recreation & Tourism Management Clemson University rlacher@clemson.edu
More informationEXST 7037 Multivariate Analysis Factor Analysis (SASy version) Page 1
EXST 7037 Multivariate Analysis Factor Analysis (SASy version) Page 1 1 *** CH05SD ***; 2 *****************************************************************************; 3 *** The Second International Math
More informationRob Reider Adjunct Professor, NYU-Courant Consultant, Quantopian
INTRODUCTION TO TIME SERIES ANALYSIS IN PYTHON Introducing an AR Model Rob Reider Adjunct Professor, NYU-Courant Consultant, Quantopian Mathematical Decription of AR(1) Model R = μ + ϕ R + ϵ t t 1 t Since
More informationHow can it be right when it feels so wrong? Outliers, diagnostics, non-constant variance
How can it be right when it feels so wrong? Outliers, diagnostics, non-constant variance D. Alex Hughes November 19, 2014 D. Alex Hughes Problems? November 19, 2014 1 / 61 1 Outliers Generally Residual
More informationModule 7-4 N-Area Reliability Program (NARP)
Module 7-4 N-Area Reliability Program (NARP) Chanan Singh Associated Power Analysts College Station, Texas N-Area Reliability Program A Monte Carlo Simulation Program, originally developed for studying
More informationA New Least Mean Squares Adaptive Algorithm over Distributed Networks Based on Incremental Strategy
International Journal of Scientific Research Engineering & echnology (IJSRE), ISSN 78 88 Volume 4, Issue 6, June 15 74 A New Least Mean Squares Adaptive Algorithm over Distributed Networks Based on Incremental
More informationAn Empirical Look at Software Patents (Working Paper )
An Empirical Look at Software Patents (Working Paper 2003-17) http://www.phil.frb.org/econ/homepages/hphunt.html James Bessen Research on Innovation & MIT (visiting) Robert M. Hunt* Federal Reserve Bank
More informationHow do we know macroeconomic time series are stationary?
18 th World IMACS / MODSIM Congress, Cairns, Australia 13-17 July 2009 http://mssanz.org.au/modsim09 How do we know macroeconomic time series are stationary? Kenneth I. Carlaw 1, Steven Kosemplel 2, and
More informationStandard Development Timeline
Standard Development Timeline This section is maintained by the drafting team during the development of the standard and will be removed when the standard is adopted by the NERC Board of Trustees (Board).
More informationAN AUTOREGRESSIVE BASED LFM REVERBERATION SUPPRESSION FOR RADAR AND SONAR APPLICATIONS
AN AUTOREGRESSIVE BASED LFM REVERBERATION SUPPRESSION FOR RADAR AND SONAR APPLICATIONS MrPMohan Krishna 1, AJhansi Lakshmi 2, GAnusha 3, BYamuna 4, ASudha Rani 5 1 Asst Professor, 2,3,4,5 Student, Dept
More informationThe most recent advancement of endogenous growth theory has been the emergence
IMF Staff Papers Vol. 53, No. 2 2006 International Monetary Fund Relating the Knowledge Production Function to Total Factor Productivity: An Endogenous Growth Puzzle YASSER ABDIH AND FREDERICK JOUTZ* The
More informationLife Science Journal 2014;11(5s)
Self Satisfaction of the Entrepreneurs in relation to the CSR Practices across Peshawar KPK Pakistan Dr. Shahid Jan 1, Kashif Amin 2, Dr. Muhammad Tariq 1, Dr. Zahoor Ul Haq 3, Dr. Nazim Ali 4 1 Assistant
More informationPassive fathometer reflector identification with phase shift modeling
1. Introduction Passive fathometer reflector identification with phase shift modeling Zoi-Heleni Michalopoulou Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey
More informationTennessee Senior Bridge Mathematics
A Correlation of to the Mathematics Standards Approved July 30, 2010 Bid Category 13-130-10 A Correlation of, to the Mathematics Standards Mathematics Standards I. Ways of Looking: Revisiting Concepts
More informationPractical Comparison of Results of Statistic Regression Analysis and Neural Network Regression Analysis
Practical Comparison of Results of Statistic Regression Analysis and Neural Network Regression Analysis Marek Vochozka Institute of Technology and Businesses in České Budějovice Abstract There are many
More informationResearch on the Impact of R&D Investment on Firm Performance in China's Internet of Things Industry
Journal of Advanced Management Science Vol. 4, No. 2, March 2016 Research on the Impact of R&D Investment on Firm Performance in China's Internet of Things Industry Jian Xu and Zhenji Jin School of Economics
More informationHOW DOES INCOME DISTRIBUTION AFFECT ECONOMIC GROWTH? EVIDENCE FROM JAPANESE PREFECTURAL DATA
Discussion Paper No. 910 HOW DOES INCOME DISTRIBUTION AFFECT ECONOMIC GROWTH? EVIDENCE FROM JAPANESE PREFECTURAL DATA Masako Oyama July 2014 The Institute of Social and Economic Research Osaka University
More informationAn investigation into the determinants of income inequality and testing the validity of the Kuznets Hypothesis
Mälardalen University Västerås, 2011-06-02 School of Sustainable Development of Society and Technology (HST) Bachelor Thesis in Economics Tutor: Dr. Johan Lindén An investigation into the determinants
More informationModeling Inflation After the Crisis
Modeling Inflation After the Crisis James H. Stock and Mark W. Watson I. Introduction The past five decades have seen tremendous changes in inflation dynamics in the United States. Some of the changes
More informationAnalysis of crucial oil gas and liquid sensor statistics and production forecasting using IIOT and Autoregressive models
Analysis of crucial oil gas and liquid sensor statistics and production forecasting using IIOT and Autoregressive models Anurag Kumar Singh 1, R.K. Pateriya 2 1M. tech Student, Dept. of Computer Science
More informationTable of Contents. Frequently Used Abbreviation... xvii
GPS Satellite Surveying, 2 nd Edition Alfred Leick Department of Surveying Engineering, University of Maine John Wiley & Sons, Inc. 1995 (Navtech order #1028) Table of Contents Preface... xiii Frequently
More informationGlobal Candle Market Research Report 2017
Report Information More information from: https://www.wiseguyreports.com/reports/861304-global-candle-market-research-report-2017 Global Candle Market Research Report 2017 Report / Search Code: WGR861304
More informationPhysics 2310 Lab #6: Multiple Thin Lenses Dr. Michael Pierce (Univ. of Wyoming)
Physics 2310 Lab #6: Multiple Thin Lenses Dr. Michael Pierce (Univ. of Wyoming) Purpose: The purpose of this lab is to investigate the properties of multiple thin lenses. The primary goals are to understand
More informationWhich Second-Generation Endogenous Theory Explains Long-Run Growth of a Developing Economy?
Which Second-Generation Endogenous Theory Explains Long-Run Growth of a Developing Economy? Shishir Saxena 1, Jakob B. Madsen, James Ang Department of Economics, Monash University, Caulfield East, VIC
More informationPhysics 2310 Lab #5: Thin Lenses and Concave Mirrors Dr. Michael Pierce (Univ. of Wyoming)
Physics 2310 Lab #5: Thin Lenses and Concave Mirrors Dr. Michael Pierce (Univ. of Wyoming) Purpose: The purpose of this lab is to introduce students to some of the properties of thin lenses and mirrors.
More informationInterrelAtIon Between Growth AnD InequAlIty
InterrelAtIon Between Growth AnD InequAlIty Jong Woo Kang no. 447 august 215 adb economics working paper series ASIAN DEVELOPMENT BANK ADB Economics Working Paper Series Interrelation between Growth and
More information2011, Stat-Ease, Inc.
Practical Aspects of Algorithmic Design of Physical Experiments from an Engineer s perspective Pat Whitcomb Stat-Ease Ease, Inc. 612.746.2036 fax 612.746.2056 pat@statease.com www.statease.com Statistics
More informationThe Game-Theoretic Approach to Machine Learning and Adaptation
The Game-Theoretic Approach to Machine Learning and Adaptation Nicolò Cesa-Bianchi Università degli Studi di Milano Nicolò Cesa-Bianchi (Univ. di Milano) Game-Theoretic Approach 1 / 25 Machine Learning
More informationLevel I Signal Modeling and Adaptive Spectral Analysis
Level I Signal Modeling and Adaptive Spectral Analysis 1 Learning Objectives Students will learn about autoregressive signal modeling as a means to represent a stochastic signal. This differs from using
More informationCZECH ECONOMY. In 2016 and 1H2017. Section of Industry Economic Analyses Department. Czech Economy
CZECH ECONOMY In 2016 and 1H2017 Gross Domestic Product (constant prices, seasonally adjusted, y-o-y change, in %) 8 6 4 2 0-2 -4-6 -8 I/08 I/09 I/10 I/11 I/12 I/13 I/14 I/15 I/16 I/17 EA19 Germany USA
More informationThe (Un)Reliability of Real-Time Output Gap Estimates with Revised Data
The (Un)Reliability of RealTime Output Gap Estimates with Data Onur Ince * David H. Papell ** September 6, 200 Abstract This paper investigates the differences between realtime and expost output gap estimates
More informationFairfax County: Five Major Forces Shaping Its Economic Evolution
Fairfax County: Five Major Forces Shaping Its Economic Evolution Stephen S. Fuller, Ph.D. The Dwight Schar Faculty Chair and University Professor Director, The Stephen S. Fuller Institute Schar School
More informationCultural and creative industries as a catalyst for growth in BRICS economies
Cultural and creative industries as a catalyst for growth in BRICS economies Ms Nwabisa Kolisi & Prof Ronney Ncwadi Department of Economics, NMMU, Port Elizabeth SA Cultural Observatory National Conférence
More informationConvergence Forward and Backward? 1. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. March Abstract
Convergence Forward and Backward? Quentin Wodon and Shlomo Yitzhaki World Bank and Hebrew University March 005 Abstract This note clarifies the relationship between -convergence and -convergence in a univariate
More informationImplications of the New Growth Theory to Agricultural Trade Research and Trade Policy
i Implications of the New Growth Theory to Agricultural Trade Research and Trade Policy Proceedings of a Conference of the International Agricultural Trade Research Consortium Edited by Terry L. Roe April
More informationSubmitted November 19, 1989 to 2nd Conference Economics and Artificial Intelligence, July 2-6, 1990, Paris
1 Submitted November 19, 1989 to 2nd Conference Economics and Artificial Intelligence, July 2-6, 1990, Paris DISCOVERING AN ECONOMETRIC MODEL BY. GENETIC BREEDING OF A POPULATION OF MATHEMATICAL FUNCTIONS
More informationHow Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory
Prev Sci (2007) 8:206 213 DOI 10.1007/s11121-007-0070-9 How Many Imputations are Really Needed? Some Practical Clarifications of Multiple Imputation Theory John W. Graham & Allison E. Olchowski & Tamika
More informationLawrence A. Soltis. James K. Little
ANGLE TO GRAIN STRENGTH OF DOWEL-TYPE FASTENERS Lawrence A. Soltis Supervisory Research Engineer Forest Products Laboratory,' Forest Service U.S. Department of Agriculture, Madison, WI 53705 Suparman Karnasudirdja
More informationStatistical Process Control and Computer Integrated Manufacturing. The Equipment Controller
Statistical Process Control and Computer Integrated Manufacturing Run to Run Control, Real-Time SPC, Computer Integrated Manufacturing. 1 The Equipment Controller Today, the operation of individual pieces
More informationMissouri Economic Indicator Brief: Manufacturing Industries
Missouri Economic Indicator Brief: Manufacturing Industries Manufacturing is a major component of Missouri s $293.4 billion economy. It represents 13.1 percent ($38.5 billion) of the 2015 Gross State Product
More information