http://homepage.ntu.edu.tw/~ntuperc/docs/publication/2002_25_Lin.pdf

Unit root tests in panel data: asymptotic and finite-sample properties

Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.

/pdf/econometric-analysis-of-panel-data-46ld72a5p1.pdf

Econometric Analysis of Panel Data

计量经济分析 = Econometric analysis

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

This paper has suggested an open economy forward looking threshold monetary policy rule model for Japan. This model assumes that, in addition to inflation rate and real output deviations, the short term nominal interest rate of the central bank (CB) of Japan responds to nominal (or real) exchange rate deviations from their target levels. This happens when the economy lies in both regimes, the recession and the expansion. This result means that depreciation in Japan's currency will tend to offset decreases in the short term interest rate of the CB due to negative deviations in inflation and real output. A small scale open economy model simulated by the paper shows that the above offsetting effects of exchange rate deviations on interest rates help to reduce the volatility of real exchange rates (the terms of trade) coming from exogenous shocks in domestic real productivity, foreign output and inflation. JEL Classification: E52, C13, C30

An open economy forward looking monetary policy rule model for Japan and its policy implications

This paper suggests an empirically attractive Gaussian dynamic term structure model to retrieve estimates of real interest rates and inflation expectations from the nominal term structure of interest rates which are net of inflation risk premium effects. The paper shows that this model is consistent with the data and that time-variation of inflation risk premium and real interest rates can explain the puzzling behavior of the spread between long and short-term nominal interest rates to forecast changes in inflation rates, especially over short-term horizons. The estimates of inflation risk premium effects retrieved by the model tend to be negative and signi ficant, which implies that investors in the bond market require less compensation for holding nominal bonds compared to inflation-indexed bonds. This is more evident during the recent fi nancial crisis.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2509263_code1992737.pdf?abstractid=2509263&mirid=3

Retrieving Inflation Expectations and Risk Premia Effects from the Term Structure of Interest Rates

In this paper we introduce a new method of pricing an American call option by approximating its early exercise boundary based on Chebyshev polynomial functions. We implement the method to price options under the standard assumptions of the Black-Scholes model, for European options, and under stochastic volatility. For the latter, we provide an integral representation which unbundles the American call option price to Arrow-Debreu security prices. Numerical results indicate that our method is an effective alternative to other exercise boundary approximation methods under both the standard assumptions of the Black-Scholes and stochastic volatility.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID301186_code020224530.pdf?abstractid=301186&mirid=3

Elias Tzavalis

Papers

An open economy forward looking monetary policy rule model for Japan and its policy implications

Retrieving Inflation Expectations and Risk Premia Effects from the Term Structure of Interest Rates

Pricing American Option Based on a Chebyshev Approximation of the Early Exercise Boundary