Principles of econometrics

The conventional linear econometric and statistical models are not effective for forecasting the nonlinear and complex nature of crude oil prices. Computational intelligence techniques and hybrid modelling principles have been proposed to address this issue. Multiple forecasts can be combined using linear or nonlinear methods to create an aggregate forecast.

6p viritesh 02-04-2024 3 1   Download

(BQ) The following will be discussed in this part: Introducing stata, simple linear regression, interval estimation and hypothesis testing, prediction - goodness of fit and modeling issues, multiple linear regression, further inference in the multiple regression model, using indicator variables, heteroskedasticity, regression with time-series data: stationary variables.

332p nanhankhuoctai5 01-06-2020 19 3   Download

(BQ) The following will be discussed in this part: Random regressors and moment based estimation, simultaneous equations models, regression with time-series data: nonstationary variables, vector error correction and vector autoregressive models, time-varying volatility and ARCH models, panel data models, qualitative and limited dependent variable models, review of math essentials.

293p nanhankhuoctai5 01-06-2020 11 3   Download

There is a new method of rendering that was recently developed. It is called radiosity. It does something all the other rendering methods don't do: it figures out the relationship in the scene of all the objects present. For example, in real life, if you take a bright colored ball and put it into a white room, the walls of the room are going to reflect a little bit of color from the ball, making them look a little reddish for example. This is not possible in raytracing, since it does not bounce rays off of matte objects, such as a wall. You can compare the above...

316p hoangphiyeah1tv 24-04-2013 64 5   Download

LINEAR MODELS In this part we begin our econometric analysis of linear models for cross section and panel data. In Chapter 4 we review the single-equation linear model and discuss ordinary least squares estimation. Although this material is, in principle, review, the approach is likely to be different from an introductory linear models course.

34p thegun16 26-11-2010 93 7   Download

3 A continuous dependent variable. In this chapter we review a few principles of econometric modeling, and illustrate these for the case of a continuous dependent variable. We assume basic knowledge of matrix algebra and of basic statistics and mathematics

20p leslienguyen 25-11-2010 61 15   Download

CHAPTER 59 Generalized Method of Moments Estimators. This follows mainly [DM93, Chapter 17]. A good and accessible treatment is [M´9]. The textbook [Hay00] uses GMM as the organizing principle for all 9 estimation methods except maximum likelihood.

16p leslienguyen 28-10-2010 76 3   Download

CHAPTER 53 Errors in Variables. 53.1. The Simplest Errors-in-Variables Model We will explain here the main principles of errors in variables by the example of simple regression, in which y is regressed on one explanatory variable with a constant term.

107p leslienguyen 28-10-2010 69 2   Download

CHAPTER 41 Interval Estimation. We will first show how the least squares principle can be used to construct confidence regions, and then we will derive the properties of these confidence regions. 41.1. A Basic Construction Principle for Confidence Regions The least squares objective function

49p leslienguyen 28-10-2010 46 3   Download

CHAPTER 13 Estimation Principles and Classification of Estimators. 13.1. Asymptotic or Large-Sample Properties of Estimators We will discuss asymptotic properties first, because the idea of estimation is to get more certainty by increasing the sample size. Strictly speaking, asymptotic properties do not refer to individual estimators but to sequences of estimators

70p leslienguyen 28-10-2010 58 3   Download

Chapter 10 The Method of Maximum Likelihood 10.1 Introduction The method of moments is not the only fundamental principle of estimation, even though the estimation methods for regression models discussed up to this point (ordinary, nonlinear, and generalized least squares, instrumental variables

50p summerflora 28-10-2010 79 6   Download