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Match the following packages with their characteristics:
Menu-driven packages = Do not allow you to compute anything other than what's on the menu Command-driven packages = Typically quite flexible Eviews, RATS and Stata = Used for illustrations in the text MicroFit, PcGive, TSP and SHAZAM = Alternative econometrics programmes
Match the following journals with their content:
Journal of Applied Econometrics = Publish reviews of econometrics software Journal of Economic Surveys = Focus on software reviews
Match the following types of exercises with their purpose:
Technical questions = Check whether the reader has grasped the most important concepts Empirical exercises = Require the reader to use actual data Derivation exercises = Ask for proofs or technical details Proof exercises = Check understanding of key concepts
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Match the following packages with their usage:
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Match the following characteristics with their types of packages:
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Match the following types of software with their characteristics:
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Match the following resources with their content:
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Match the following types of exercises with their requirements:
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Match the following packages with their advantages:
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The ____________ test is used to test for heteroskedasticity.
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When testing for heteroskedasticity, the ____________ test is an alternative to the Breusch-Pagan test.
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The chapter discusses ____________ heteroskedasticity in section 4.3.5.
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Weighted ____________ Squares is a method used to address heteroskedasticity.
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Section 4.4 of the chapter covers ____________ for heteroskedasticity.
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The ______ test is used to test for omitted variables in the normal linear regression model.
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The ______ test is used to test for heteroskedasticity in the normal linear regression model.
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The ______ method of estimation is used to estimate intertemporal asset pricing models.
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Specification ______ are used to test the validity of the model assumptions.
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Quasi-______ likelihood and moment conditions tests are used to test for model assumptions.
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The Fama–MacBeth approach is a method used in ____________ inference.
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Alternative Instrumental Variables Estimators are discussed in section ____________.
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Testing for ____________ and autocorrelation is an important step in regression analysis.
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The ____________ test is used to test for unit roots in panel time series data.
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Panel cointegration tests are used to determine the presence of ____________ relationships between variables.
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What is the primary difference between a spurious regression and a cointegrated regression in the context of nonstationary variables?
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What is the purpose of an error-correction mechanism in a cointegrated system?
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How does a Vector Autoregressive (VAR) model differ from a univariate autoregressive model?
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What is the purpose of testing for cointegration in a Vector Autoregressive (VAR) model?
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What is the advantage of using a multivariate approach, such as a VAR model, over a univariate approach in time series analysis?
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What type of time series models are developed in Chapter 8, and what do they explain?
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What is the purpose of GARCH models in time series analysis?
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What is the primary focus of Chapters 8 and 9 in terms of time series modeling?
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What type of models are developed in Chapter 7, and what type of data do they typically handle?
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What is the significance of the last three decades in terms of time series modeling?
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What is the primary concern in sample selection bias, and how can it be addressed?
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What is the difference between the Tobit model and the Tobit II model, and when would you use each?
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What is the purpose of specification tests in binary choice models, and how do they differ from goodness-of-fit tests?
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What is the difference between a hazard rate and a survival function in duration models, and how are they used?
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What is the purpose of ordering in ordered response models, and how does it differ from binary response models?
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Panel data are only available if we have repeated observations of the same countries.
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Cointegration and error-correction models can be used with panel data.
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Panel data has become less important in economics over recent decades.
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Unit roots and cointegration in a panel data setting is a topic that is not covered in the book.
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Panel data are only used for micro-economic analysis of households and firms.
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The Fixed Effects Model is a type of random effects model in panel data analysis.
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The First-difference Estimator is used to eliminate individual effects in panel data analysis.
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The Random Effects Model is more efficient than the Fixed Effects Model when the individual effects are correlated with the explanatory variables.
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Panel data analysis is only used for time series data.
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The chapter discusses panel data modeling in Chapter 9.
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The Fama–MacBeth approach is a method used in robust inference.
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Panel time series data can be used to test for heteroskedasticity and autocorrelation.
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The fixed effects logit model is a type of binary choice model.
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Dynamic linear models are used to model panel data with exogenous variables.
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The problem of initial conditions is specific to limited dependent variables models.
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What is the primary goal of econometrics in formulating a statistical model?
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What is an important job of an econometrician when evaluating a statistical model?
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What is a crucial consideration when using econometric techniques?
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What is an example of a restriction that economic theory may imply on a statistical model?
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What is the main approach of this book in guiding the reader through econometric techniques?
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Which type of model is ideally suited for analyzing policy changes on an individual level?
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What is the primary purpose of econometric modeling?
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What type of relationships do cross-sectional models describe?
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What type of data is required for analyzing 'what if' questions using cross-sectional relationships?
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What type of models describe differences between different individuals and changes in behavior over time?
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What has led to the increased attention to the modeling of macro-economic relationships and their dynamics?
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What is the primary role of econometrics in empirical work in all fields of economics?
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What is the limitation of introductory econometrics textbooks for applied researchers?
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What is the primary reason for the need for an accessible textbook on econometrics?
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What area of economics has required and stimulated many theoretical developments in econometrics?
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Study Notes
Overview of Econometrics
- Econometrics is at the heart of empirical research in economics and is no longer sufficient to simply run a few regressions and interpret the results.
- The field has evolved significantly since the 1970s, with increased focus on macroeconomic relationships, microeconomic models, and financial markets.
Types of Models in Econometrics
- Time series models: examine relationships between different variables measured at different points in time for a single unit.
- Cross-sectional models: describe relationships between different variables measured at a given point in time for different units (e.g., households or firms).
- Panel data models: simultaneously describe differences between different individuals and differences in behavior of a given individual over time.
Econometrics Textbooks
- Introductory textbooks often provide insufficient coverage for applied researchers.
- Advanced textbooks are often too technical or too detailed for the average economist to grasp the essential ideas.
Purpose of the Book
- To provide an accessible textbook that discusses recent and relatively more advanced developments in econometrics.
Features of the Book
- Exercises at the end of each chapter are intended to check whether the reader has grasped the most important concepts.
- Empirical exercises require the reader to use actual data, available through the book's website.
Software for Econometrics
- Various packages are available, including Eviews, RATS, Stata, MicroFit, PcGive, TSP, and SHAZAM.
- Each package has its particular advantages and disadvantages.
- A trade-off exists between user-friendliness and flexibility.
Contents of the Book
- Page vi: Contents of the book, including chapters on alternative approaches to estimate causal effects, generalized instrumental variables estimator, and maximum likelihood estimation and specification tests.
- Page 5.5: Alternative approaches to estimate causal effects.
- Page 5.6: Generalized instrumental variables estimator.
- Page 5.7: Institutions and economic development.
- Page 5.8: Generalized method of moments.
- Page 5.9: Illustration: estimating intertemporal asset pricing models.
- Page 6: Maximum likelihood estimation and specification tests.
- Page 7: Models with limited dependent variables.
Contents of a Statistics Textbook
- The book covers various topics in statistics, including heteroskedasticity, instrumental variables, maximum likelihood estimation, and models with limited dependent variables.
Heteroskedasticity
- Heteroskedasticity can be tested using various methods, including the Breusch-Pagan test and the White test.
- Multiplicative heteroskedasticity is a specific type of heteroskedasticity.
- Weighted least squares with arbitrary weights can be used to address heteroskedasticity.
Instrumental Variables
- The generalized instrumental variables estimator is a method for estimating causal effects.
- Two-stage least squares is a method for estimating instrumental variables.
- Weak instruments are a problem that can arise in instrumental variables estimation.
- Specification tests are used to test the validity of instrumental variables models.
Maximum Likelihood Estimation
- Maximum likelihood estimation is a method for estimating parameters in statistical models.
- The normal linear regression model is a specific type of model that can be estimated using maximum likelihood.
- Specification tests are used to test the validity of maximum likelihood models.
Models with Limited Dependent Variables
- Binary choice models are a type of model that can be used to analyze limited dependent variables.
- The fixed effects logit model and the random effects probit model are specific types of binary choice models.
- Tobit models are a type of model that can be used to analyze limited dependent variables.
- Panel time series models are used to analyze data with multiple observations over time.
Chapter 9: Multivariate Time Series Models
- Multivariate time series models are typically used in macroeconomics, where multiple economic variables are analyzed
- Chapter 9 covers dynamic models with stationary variables, models with nonstationary variables, spurious regressions, cointegration, and error-correction mechanisms
- Vector Autoregressive (VAR) models are also discussed in this chapter
Dynamic Models with Stationary Variables
- Stationary variables have a constant mean and variance over time
- Dynamic models with stationary variables can be used to analyze the relationship between multiple economic variables
Models with Nonstationary Variables
- Nonstationary variables have a changing mean and/or variance over time
- Examples of nonstationary variables include GDP, inflation rate, and stock prices
- Cointegration is a concept that describes the long-run relationship between multiple nonstationary variables
- Error-correction mechanisms are used to model the short-run dynamics of nonstationary variables
Cointegration
- Cointegration occurs when two or more nonstationary variables have a long-run relationship
- Cointegration implies that the variables move together in the long run, but can deviate from each other in the short run
- Testing for cointegration is an important aspect of multivariate time series analysis
Vector Autoregressive (VAR) Models
- VAR models are a type of multivariate time series model that can be used to analyze the relationships between multiple economic variables
- VAR models can be used to forecast future values of multiple economic variables
- Cointegration can be tested in a VAR model using the Johansen test
Panel Data Modelling
- Panel data consists of repeated observations of the same units (e.g. households, firms, or countries) over time.
- The use of panel data has become increasingly important in many areas of economics due to the availability of micro-economic panels of households and firms, and the ability to pool time series of several countries.
- Panel data allows for a cross-sectional comparison of countries, providing additional information beyond a historical comparison of a country with its own past.
Types of Panel Data Models
- Static Linear Model:
- Fixed Effects Model
- First-difference Estimator
- Random Effects Model
- Dynamic Linear Models:
- Autoregressive Panel Data Model
- Dynamic Models with Exogenous Variables
- Models with Limited Dependent Variables:
- Binary Choice Models
- Fixed Effects Logit Model
- Random Effects Probit Model
- Tobit Models
- Pseudo Panels and Repeated Cross-sections:
- Fixed Effects Model
- Instrumental Variables Interpretation
- Dynamic Models
Important Concepts
- Efficiency of Parameter Estimators
- Identification of Parameters
- Goodness-of-Fit
- Alternative Instrumental Variables Estimators
- Robust Inference
- Testing for Heteroskedasticity and Autocorrelation
- The Fama–MacBeth Approach
- Heterogeneity
- Panel Unit Root Tests
- Panel Cointegration Tests
- Incomplete Panels and Selection Bias
Econometrics
- Econometricians formulate statistical models based on economic theory, confront them with data, and aim to come up with a specification that meets required goals.
- The unknown elements in the specification, parameters, are estimated from a sample of available data.
- Econometricians judge whether the resulting model is 'appropriate', checking assumptions and properties, and intended use, such as prediction or analysing policy changes.
Econometric Techniques and Assumptions
- Numerous econometric techniques can be used, and their validity often depends on underlying assumptions.
- Economic theory implies certain restrictions on the model, such as the efficient market hypothesis, which implies stock market returns are not predictable from their past.
Development of Econometrics
- Since the 1970s, econometric methods have been increasingly employed in micro-economic models describing individual, household or firm behaviour.
- Recent theoretical developments, such as cointegration, have generated increased attention to macro-economic relationships and their dynamics.
- The empirical analysis of financial markets has required and stimulated many theoretical developments in econometrics.
Role of Econometrics in Economics
- Econometrics plays a major role in empirical work in almost all fields of economics, and it is no longer sufficient to be able to run a few regressions and interpret the results.
- There is a need for an accessible textbook that discusses recent and relatively more advanced developments in econometrics.
Types of Relationships in Econometrics
- Cross-sectional relationships describe differences between units (e.g. households or firms) at a given point in time, explaining why they are different or behave differently.
- Cross-sectional relationships can be used to analyse 'what if' questions under particular conditions.
- Panel data, repeated observations over the same units, describe differences between individuals and differences in behaviour over time, suited for analysing policy changes on an individual level.
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Description
Explore the evolution and importance of econometrics in empirical research, including types of models such as time series models. Learn about macroeconomic relationships, microeconomic models, and financial markets.
