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Annals of the University of Petroşani ∼ Economics ∼ ISSN 1582 – 5949 ANNALS OF THE UNIVERSITY OF PETROŞANI ∼ ECONOMICS ∼ VOL. IV UNIVERSITAS PUBLISHING HOUSE PETROŞANI – ROMANIA 2004 ISSN 1582 – 5949 EDITOR OF PUBLICATION Prof. Eng. Ioan-Lucian BOLUNDUŢ Ph.D. e-mail: [email protected] ADVISORY BOARD Prof. Eng. Ec. Ioan ABRUDAN Ph.D. - Technical University of Cluj-Napoca, Romania; Prof. Eng. Ec. Ionel BARBU Ph.D. - „Aurel Vlaicu” University of Arad, Romania; Prof. Ec. Victoria BĂRBĂCIORU Ph.D. - University of Craiova, Romania; Prof. Ec. Constantin BÂGU Ph.D. - Economic Studies Academy, Bucharest, Romania; Prof. Ec. Sorin BRICIU Ph.D. - „1 Decembrie 1918”University of Alba-Iulia, Romania; Prof. Ec. Anişoara CAPOTĂ Ph.D. - „Transilvania”University of Braşov, Romania; Prof. Ec. Dorin COSMA Ph.D. - West University of Timişoara, Romania; Prof. Ec. Ioan COSMESCU Ph.D. - „Lucian Blaga” University of Sibiu, Romania; Prof. Ec. Horia CRISTEA Ph.D. - West University of Timişoara, Romania; Prof. Ec. Ioan Constantin DIMA Ph.D. - „ARTIFEX” University of Bucharest, Romania; Prof. Ec. Mariana MAN Ph.D. - University of Petroşani, Romania; Assoc. Prof. Mat. Ec. Ilie MITRAN Ph.D. - University of Petroşani, Romania; Prof. Ec. Dumitru OPREAN Ph.D. - „Babeş-Bolyai” University of Cluj-Napoca, Romania; Prof. Oleksandr ROMANOVSKIY Ph.D. - National Technical University of Kharkov, Ukraine; Assoc. Prof. Ec. Aurelia-Felicia STĂNCIOIU Ph.D. - Economic Studies Academy, Bucharest, Romania; Prof. Ec. László TÓTH Ph.D. - University of Miskolc, Hungary. EDITORIAL BOARD Editor-in-chief: Prof. Ec. Mariana MAN Ph.D. - University of Petroşani, Romania Associate Editors: Lecturer Ec. Codruţa DURA Ph.D. - University of Petroşani, Romania Lecturer Ec. Claudia ISAC Ph.D. - University of Petroşani, Romania Assist. Prof. Ec. Emilia MIHĂILĂ - University of Petroşani, Romania Assist. Prof. Ec. Alin MONEA - University of Petroşani, Romania Editor Secretary: Assist. Prof. Ec. Imola DRIGĂ - University of Petroşani, Romania Editorial office address: University of Petroşani, 20 University Street, Petroşani, 332006, Romania, Phone: (40)254/542.994, 542.580, 542.581, 543.382, Fax: (40)254/543.491, 546.238, Telex: 72524 univp, E-mail: [email protected]. Annals of the University of Petroşani, Economics, 4 (2004) 3 Contents Pag Babucea, A.G. Statistical methods for time series and panel data analysis 5 Baron, M.; Dobre-Baron, O. The contribution of the „Romanian Bank” to the 13 „nationalization” of the coal companies in the Jiu Valley Biber, E. Foreign investments in modern economic activities 27 Boncea, A. Elements of market economy build-up strategy 31 Buşe, F. The effect of knowledge on the economic growth 37 Cîrnu, D; Todoruţ, A. Types of strategies in the field of quality 45 Cosmescu, I; Cosmescu, D. Service market – the real form of existence for 51 exchange relations Cucu, I. Considérations sur le contrôle en marketing 55 Dima, I.C. The history of EURO 63 Drigă, I. Means of reducing credit risk 69 Dura, C. Considerations upon sales force management 75 Fleşer, A. Characteristics of eco – systemic management 83 Flităr, M.P. Current trends in social responsibility and ethical standards in 89 services marketing Ghicajanu, M. Strategic planning and managerial control 95 Ghicajanu, M.; Semen, M. Break-even analysis of the enterprise at one 99 product level Irimie, S.; Munteanu, R.; Matei, I. Closure of mines. Problems regarding the 105 environment and the investment efficiency within the context of environment protection and rehabilitation in Jiu Valley Isac, A. Aspects regarding file operating systems used in management 113 Isac, C. Consideration of decisional environment on an international scale 117 Ivănuş, L. Country risk rating – importance and methodology 125 Koronka, A.; Dumbravă, G. Eminescu and protectionism 133 Magda, D. Aspects regarding regional disparity reduction policies 139 Man, M. The structure of expenses and revenues according to International 147 Accounting Standards Mihăilă, E. Some aspects about the administration accountancy’s history 151 Mihăilă, E. Considerations about exporting ware in commission 155 Mitran, I. About some decisional optimum models of the consumer 161 4 Annals of the University of Petroşani, Economics, 4 (2004) Molnar-Siposne, T. Analysis of documents representing environmental 177 awareness in company management Monea, A. Some aspects of inventory held for sale using statements of federal 185 accounting Standard no.3 Munteanu, R. Qualifizierung und vermittlung für eine dynamische arbeitswelt 189 Nedelea, A. The tourism market in Romania 191 Popeangă, V.; Vătuiu, T. The necessity of achieving the internal audit of the 197 public bodies Popescu, M. L’evaluation de l’excedent ou du deficite budgetaire 203 Preda, M. Informatic system of financial management 207 Radu, S. Considerations about the strategic outlook on the quality 223 management of the Romanian high schools Radu, S.; Dolea, G. Considerations for the mining activity analysis through 231 the output theory Răscolean, I. Deontological code of the internal audit 241 Semen, M. Zur frage der möglichkeit der anwendung der 245 produktionsfunktion von gutenberg für das studium von produktionssystemen im bergbau Silivestru, T.; Dobre-Baron, O. Suggestions regarding financial resources 253 solutions to credit Romanian agriculture Silivestru, T.; Dobre-Baron, O. Suggestions regarding the part played by 263 agricultural credit on mortgage in the foundation of viable agricultural exploitations inRromania Slavici, T.; Bivolaru, D. Benutzung der artifiziellen neuronalen netz (ann) im 271 konkurs voraussehen Slavici, T.; Bivolaru, D. Anwendung der artifiziellen neuronale netz (ann) zur 277 aktienmarkt der nikko börsesystem Slusariuc, G. Comparative analyse of the SMEs from Romania and European 283 Union Szasz, M. New tendencies in public administration development 289 Toma, C. Possibilities of improving omniasig activity efficiency 295 Vătuiu, T. Overview of the administration of oracle databases with 297 application in the budgetary section Annals of the University of Petroşani, Economics, 4 (2004), 5-12 5 STATISTICAL METHODS FOR TIME SERIES AND PANEL DATA ANALYSIS ANA-GABRIELA BABUCEA * ABSTRACT: Analysis of time series and panel data is a very large area of methodology. In this brief presentation of these classes of models, I am exchanging depth for breadth in an attempt to point particular types of models that may need to investigate further in doing empirical work. KEY WORDS: time series, panel data, methods, hazard models, Cox regression 1. BASIC PROBLEMS THAT TIME SERIES PRESENT I’ll start with some basic terminology that’s relevant to longitudinal data because the term ‘longitudinal data’ implies that one has panel data, that is, data collected on multiple units across multiple points in time. Time Series typically refers to repeated observations on a single observational unit across time and Multiple Time Series means multiple observational units observed across time. This can also be considered a panel study, although the usage often differs depending on the unit of analysis. Generally, micro data is considered panel data, while macro data is considered time series data. Multivariate Time Series means there are multiple outcomes measured over time. A Trend is a general pattern in time series data over time. There are really very few differences between the approaches that are used to analyze time series or panel data and the basic linear regression model. However, there are four basic problems that such data present: 1) Error correlation within units across time. This problem requires alternative estimation of the linear model, or the development of a different model. 2) Spuriousness due to trending. When attempting to match two time series, it may appear that two aggregate time series with similar trends are causally related, when in fact, they aren’t. * Prof., Ph.D. at the “Constantin Brâncuşi” University of Târgu-Jiu, Romania 6 Babucea, A.G. 3) Few units. Sometimes, time series data are relatively small. When dealing with a single time series, we often have relatively few measurements. This makes it difficult to observe a trend. 4) Censoring. Although our power to examine relationships is enhanced with time series and panel data, such data presents its own problems in terms of ‘missing’ data. 2. TIME SERIES METHODS 2.1. The First Differences Approach As a first effort to eliminate auto-correlated errors, and to produce stationarity in a time series, we can use the first differences approach. In that approach, we * compute y = yt − yt−1 ,∀t . We also difference the covariates. Then, we can be conducted on the new data * (which has n = n − 1). We can repeat the differencing approach if necessary. 2. 2. Autoregressive (AR) and Moving Average (MA) Models Rather than using difference models, we can simply specify structure for the error term. A flexible models’ class that do so are Autoregressive-Moving Average Models (ARMA). Typically, we first construct an autocorrelation plot, which is a plot of the autocorrelation of the data (or errors, if a model was previously specified) at various lags. The function is computed: m∑ (θt − θ )(θ − θ ) AC = t−L (1) L 2 (m − L)∑ ()θt − θ where m is the number of time series data points and L is the number of lags. The shape of this function across L often tells us what type of model we need. For example, if the plot shows an exponential decay of the autocorrelation across lags, an AR(#) model is needed in which # tells us the number of lagged terms to include to reduce the autocorrelation to nonsignificance. An AR(1) model simplifies the error covariance matrix for GLS estimation by decomposing the original OLS error term into two components: εt = ρεt−1 + vt . Here the error at one time point is simply a function of the error at the previous time point plus a random shock at time t(v). Higher order AR models are obtained by allowing the error to be a function of errors at lags >1. Typically, autoregressive models are estimated by incorporating a lagged y variable into the model. So long as the absolute value of the coefficient for the lagged term(s) does not exceed 1, the series can be considered stationary.
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