Suchergebnisse

I Methodological Issues -- 1 Some Extensions and Refinements of Gottschalk's Estimation of Production Functions -- 2 Regional Econometric Modeling and the New Jersey State Model -- II External Linkage Issues -- 3 Integrated State-Substate Econometric Modeling: Design and Utilization for Long-Run Economic Analysis -- 4 Linking Regional Economic Models for Policy Analysis -- 5 The Regional Business Cycle: A Theoretical Exposition with Time Series, Cross Sectional, and Predictive Applications -- 6 A Method for Analyzing the Changing Impact of the Business Cycle on Regional Economies -- III Data And Model Evaluation Issues -- 7 A General Framework for Interpolation, Distribution, and Extrapolation of a Time Series by Related Series -- 8 New Techniques for Determining If a Time Series Can Be Seasonally Adjusted Reliably -- 9 Regional Econometric Models: The Forecasting Record.

Introduction -- An overview of the literature -- Models without diffusion -- Technology, diffusion, and growth: a key trinomial -- Models with exogenous diffusion -- Models with endogenous diffusion -- An alternative approach: the advantages of continuous-time quantitative methods -- Econometric estimation -- Towards a dynamic model of growth and technology -- The modified model: endogenising human capital -- Technology diffusion: the SETI model -- Continuous-time econometric models of regional convergence and the role of spatial interactions -- Conclusion

"An introduction to the field of financial econometrics, focusing on providing an introduction for undergraduate and postgraduate students whose math skills may not be at the most advanced level, but who need this material to pursue careers in research and the financial industry"--

Introduction to: econometric modeling of China -- ch. 1. A model study of balance of payments and money supply of China. Introduction. 1. Characteristics of the Chinese balance of payments. 2. Current balance. 3. Capital account. 4. Balance of payments and money supply. 5. Structure and test of the model -- ch. 2. ICSEAD'S econometric model of the Chinese economy. 1. Introduction. 2. Econometric models of the Chinese economy: brief historic review. 3. Outline of the model. 4. Some simulation analyses. 5. Reference -- ch. 3. Outline of the PAIR China-Hong Kong link model. 1. PAIR project. 2. Data problems. 3. Model specification. 4. Estimation results and simulation analysis -- ch. 4. China's econometric model for project PAIR. 1. Introduction. 2. Data. 3. Structure of the model. 4. List of the equations. 5. Explanation of variables. 6. Dynamic simulation test. 7. Policy simulations. 8. Concluding remarks -- ch. 5. A computable general equilibrium model for the Chinese economy. 1. Introduction. 2. Methodology — why a CGE model? 3. The structure of DRCCGE2. 4. Model calibration and the benchmark equilibrium data set. 5. Some major applications of DRCCGE model -- ch. 6. Natural decomposition of total factor productivity growth. 1. Abstract. 2. Growth in total factor productivity. 3. Output elasticities in terms of costs [2]. 4. Decomposition of total factor productivity growth. 5. Decomposition of product output growth -- ch. 7. China's macro econometric annual model. 1. Summary of the model. 2. Model structure. 3. Model application and problems -- ch. 8. A retrospective view of the Asian financial crisis: special reference to exchange rate policy. 1. Introduction. 2. Dollar pegs. 3. How large is the effect of exchange rate policy? 4. Conclusions -- ch. 9. Output and price determination in Chinese macroeconometric models. 1. Introduction. 2. The case of Liang's model. 3. The case of Shen's model. 4. Concluding remarks -- ch. 10. A note on the statistical data of China: population and labor. 1. Introduction. 2. Data of population and labor. 3. Economically active population and employed persons. 4. The number of staff and workers.

The aim of this book is to present the main statistical tools of econometrics. It covers almost all modern econometric methodology and unifies the approach by using a small number of estimation techniques, many from generalized method of moments (GMM) estimation. The work is in four parts: Part I sets forth statistical methods, Part II covers regression models, Part III investigates dynamic models, and Part IV synthesizes a set of problems that are specific models in structural econometrics, namely identification and overidentification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises.

Presents the main statistical tools of econometrics, focusing specifically on modern econometric methodology. The authors unify the approach by using a small number of estimation techniques, mainly generalized method of moments (GMM) estimation and kernel smoothing. The choice of GMM is explained by its relevance in structural econometrics and its preeminent position in econometrics overall. Split into four parts, Part I explains general methods. Part II studies statistical models that are best suited for microeconomic data. Part III deals with dynamic models that are designed for macroeconomic and financial applications. In Part IV the authors synthesize a set of problems that are specific to statistical methods in structural econometrics, namely identification and over-identification, simultaneity, and unobservability. Many theoretical examples illustrate the discussion and can be treated as application exercises. Nobel Laureate James A. Heckman offers a foreword to the work

Econometric Modeling provides a new and stimulating introduction to econometrics, focusing on modeling. The key issue confronting empirical economics is to establish sustainable relationships that are both supported by data and interpretable from economic theory. The unified likelihood-based approach of this book gives students the required statistical foundations of estimation and inference, and leads to a thorough understanding of econometric techniques. David Hendry and Bent Nielsen introduce modeling for a range of situations, including binary data sets, multiple regression, and cointegrated systems. In each setting, a statistical model is constructed to explain the observed variation in the data, with estimation and inference based on the likelihood function. Substantive issues are always addressed, showing how both statistical and economic assumptions can be tested and empirical results interpreted. Important empirical problems such as structural breaks, forecasting, and model selection are covered, and Monte Carlo simulation is explained and applied. Econometric Modeling is a self-contained introduction for advanced undergraduate or graduate students. Throughout, data illustrate and motivate the approach, and are available for computer-based teaching. Technical issues from probability theory and statistical theory are introduced only as needed. Nevertheless, the approach is rigorous, emphasizing the coherent formulation, estimation, and evaluation of econometric models relevant for empirical research

1 Statistical Methods in Economic Education Research -- 2 Building Theoretical Models -- 3 Measuring Intervention, Interaction, and Distribution Effects with Dummy Variables -- 4 Experimental and Nonexperimental Approaches to Statistical Research -- 5 Measurement Instruments -- 6 Simultaneous Equations Estimation -- 7 Applying Two-Stage Least Squares -- 8 The Probit Model -- 9 The Analysis of Qualitative and Limited Responses -- 10 Limited and Discrete Dependent Variable Models -- 11 Simultaneous Nonlinear Learning Models -- 12 Using Bayesian Analysis -- About the Authors.

Intro -- ECONOMETRIC MODELINGOF VALUE-AT-RISK -- Contents -- Preface -- Introduction -- Value at Risk -- 2.1. Value at Risk Criticisms -- Expected Shortfall -- VaR and ES Modeling -- 4.1. Parametric Volatility Forecasting -- 4.1.1. Modeling the Underlying Distribution -- 4.1.2. ARCH Volatility Specifications -- 4.1.3. One-step-ahead VaR and ES Calculation under ParametricVolatility Forecasting -- 4.2. Non-Parametric Risk Management Techniques -- 4.2.1. Historical Simulation -- 4.3. Semi-Parametric Volatility Forecasting -- 4.3.1. Filtered Historical Simulation -- 4.3.2. Extreme Value Theory -- 4.4. Multi-period VaR and ES Forecasts -- 4.5. Realized Volatility Models -- Liquidity AdjustedValue-at-Risk -- 5.1. VaR Adjustments Based on the Bid-Ask Spread -- 5.2. Trading Strategies that Minimize the ExpectedCost and Its Variance -- Backtesting Value-at-Risk -- 6.1. Unconditional Coverage -- 6.2. Conditional Coverage -- 6.3. Generalization of the Conditional Coverage Test -- 6.4. Loss Functions -- Application -- Summary -- References -- Index.

Intro -- Contents -- General Theory -- The Replacement for Hypothesis Testing -- 1 The Nature of Testing -- 2 Predictive Assessment -- 3 Examples -- 3.1 Example 1: Product Placement Recall -- 3.2 Example 2: Professor's Salaries -- 4 The Conclusion Lies in Verification -- References -- On Quantum Probability Calculus for Modeling Economic Decisions -- 1 Introduction -- 2 Some Violations of Expected Utility -- 3 Some Nonadditive Probabilities -- 4 Some Facts from Psychology -- 5 How to Construct a Noncommutative Probability Calculus? -- 6 Towards Quantum Decision and Economic Models -- References -- My Ban on Null Hypothesis Significance Testing and Confidence Intervals -- Abstract -- 1 Confidence Intervals: The Usual Alternative to NHST -- 2 Conclusion -- References -- Kalman Filter and Structural Change Revisited: An Application to Foreign Trade-Economic Growth Nexus -- 1 Introduction -- 2 State Space Model and the Kalman Filter -- 3 Model Specification -- 4 Model Estimation and Results -- 5 Detection of Outliers and Structural Breaks -- 5.1 Model Diagnostics Results -- 6 Conclusions -- References -- Statisticians Should Not Tell Scientists What to Think -- 1 Themes in the Application of Statistics to Science -- 2 Schools of Thought in Probability and Statistics -- 3 Probability as Degree of Belief: Dutch Book Arguments -- 3.1 The House and Your Contract with It -- 3.2 Have You Been Rational? -- 3.3 Dutch Book Treatment of Conditional Probability -- 3.4 The Dutch Book Treatment of Belief Updating -- 3.5 Epistemic Bayesianism -- 4 Probability in the Pragmatic Bayesian School of Statistics -- 5 Models as Hypotheses About Natural Mechanisms -- 5.1 Natural Mechanisms -- 5.2 Probability as a Description of Natural Variability -- 6 Models as Mimics of Natural Mechanisms -- 6.1 At Best, Models only Approximate Natural Mechanisms.

This paper presents the results of econometric modeling of the population size, as well as the natural and migratory population growth in the Rostov region from 2013 to 2022. A linear regression multivariate model was constructed, which most reflects the dependence of the population size and its growth on the selected factors. These factors include the average annual pension size (RUB), the average annual pension size in the Rostov region relative to the average pension size in Moscow (%), labor productivity index, gross regional product per capita (RUB), current expenditures on environmental protection (thousand RUB), average monthly cash income per capita (RUB), and the average price per square meter of total apartment area in the secondary and primary housing markets (RUB). All data used for modelling are annual. It is most likely, that the results of this study may be used by the authorities of the Rostov region in the implementation of social and economic policies.

The article is devoted to the estimation of econometric models of the Latvian economy. The Klein's simplified macroeconomic model of the Latvian economy is discussed. The endogenous variables are consumption, net investment, gross domestic product (excluding net exports and additions to reserves). An exogenous variable is the government spending. The model is just-identified, and Two-stage least squares (2SLS) method provides consistent estimates of the parameters of a structural equation. The modified Keynеsian model was also considered, where the lagged variable - gross domestic product of the previos period is presented. It is proved that the model is over-identified, and the Two-stage least squares (2SLS) method provides estimates of the parameters of a structural equation. We have estimated the models with annual time-series data of the Latvia economy for the years 1995 through 2011 (at basic prices in 2000).

New methodology for econometricians, based on the dual formulation of the theory of production in terms of prices. The objectives of econometric modeling of producer behavior are to determine the nature of substitution among inputs and outputs and of differences in technology, as well as the role of economies of scale in production. Recent advances in methodology, based on the dual formulation of the theory of production in terms of prices, have enabled econometricians to achieve these objectives more effectively. This volume summarizes the economic theory, the econometric methodology, and the empirical findings resulting from the new approach.