Introduction to Estimating Economic Models (Routledge by Atsushi Maki

By Atsushi Maki

The book's entire assurance on the program of econometric how to empirical research of monetary matters is extraordinary. It uncovers the lacking hyperlink among textbooks on monetary thought and econometrics and highlights the strong connection among monetary idea and empirical research completely via examples on rigorous experimental design.  The use of data units for estimation derived with the Monte Carlo procedure is helping facilitate the understanding of the position of speculation checking out utilized to financial types.

Topics lined within the publication are: shopper habit, manufacturer habit, industry equilibrium, macroeconomic versions, qualitative-response types, panel info research and time-series research. Key econometric types are brought, distinctive, envisioned and evaluated. The therapy on methods of estimation in econometrics and the self-discipline of speculation checking out makes it a must have for graduate scholars of economics and econometrics and aids their understanding on the way to estimate financial versions and evaluation the implications by way of coverage implications.

 

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Example text

We consider the models in which the structural parameters are the same but the distribution is different from normal; these are the Cauchy, exponential, and t-distributions. 11. The results confirm that for non-normal distributions, normality is rejected by the Jarque–Bera test. 000, respectively, indicating that the Jarque–Bera test is useful for testing normality. 5 Cross-equation restriction: extension of the model from two to three categories In the previous models, total expenditure is divided into two clusters of items.

The test statistic is written as: JB = n[S2/6 + (K - 3)2/24] (45) where S is skewness and K is kurtosis. When the null hypothesis of normality is correct, the value of JB follows χ2(2). 4 show the results of the normality test. 3 indicates that the normality test is not rejected in three sets of data with no structural change; these three data sets include two separated data sets and a pooled one. But in the case of existing structural change, pooled data does not satisfy the normality test, though each of the other two data sets do.

Here we’ve applied the same procedure we used in the case of the benchmark. After pooling these two data sets, the size of the sample is 200 observations. Now a structural change test is conducted by utilizing the ordinary leastsquares (OLS) estimation method. The random variable has the characteristics of homoskedasticity, of mutual independence from other random variables, and of independence from independent variables and the disturbance term. Therefore, the value estimated by regression analysis is the best linear unbiased estimator (BLUE) according to the Gauss–Markov theorem.

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