Last edited by Kazizilkree

Sunday, August 2, 2020 | History

2 edition of **Best Linear Unbiased Estimation of Missing Observations in an Economic Time Series.** found in the catalog.

Best Linear Unbiased Estimation of Missing Observations in an Economic Time Series.

Princeton University. Econometric Research Program.

- 71 Want to read
- 21 Currently reading

Published
**1975**
by s.n in S.l
.

Written in English

**Edition Notes**

1

Series | Princeton University Econometric Research Program Research Memorandum -- 173 |

Contributions | Chow, G., Lin, A. |

ID Numbers | |
---|---|

Open Library | OL21709839M |

Time series A time series is a series of observations x t, observed over a period of time. Typically the observations can be over an entire interval, randomly sampled on an interval or at xed time points. Di erent types of time sampling require di erent approaches to the data analysis. Previous article in issue: Parameter Estimation for Periodically Stationary Time Series. Next article in issue: The Effect of the Estimation on Goodness-of-Fit Tests in Time Series Models. Next article in issue: The Effect of the Estimation on Goodness-of-Fit Tests in Time Series Models.

Introduction to Time Series Data and Serial Correlation (SW Section ) First, some notation and terminology. Notation for time series data Y t = value of Y in period t. Data set: Y 1,,Y T = T observations on the time series random variable Y We consider only consecutive, evenly-spaced observations (for example, monthly, to , no. In a series of papers W. F. Sheppard (, ) has considered the approximate representation of equidistant, equally weighted, and uncorrelated observations under the following assumptions:– (i) The data being u 1, u 2, , u n, the representation is to be given by linear combinations.

To obtain useful results you can't use nonstationary data with OLS and time series. There are other more advanced methods where nonstationarity is a non issue. With OLS you have to difference real GDP and indices, and also apply log transform in many cases. Introduction to Time Series Using Stata, Revised Edition, by Sean Becketti, is a first-rate, example-based guide to time-series analysis and forecasting using Stata. This is a must-have resource for researchers and students learning to analyze time-series data and for anyone wanting to implement time-series methods in Stata.

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Series used, that (2) the mean of the time series is zero, and that (3) the residual (or the dependent variable itself under the above two assumptions) is serially un-correlated.

Under the assumption that a series has zero mean and is serially un-correlated, the best estimate of a missing observation is clearly zero.

The remaining chapters can be divided into two parts: part 1. consists of Chapters 3 to 10, which focus on adjustments for a single time series; part 2 consists of Chapters 11 to 14, which are devoted to adjustments of systems of time series.

The estimation of dynamic models with missing observations CHOW, G.C. and LIN, A. Best linear unbiased estimation of missing observation in an economic time series.

]ou4nal 01 the Amenlcan. Estimating Missing Observations in Economic Time Series A. Harvey London School of Economics, Houghton Street, London, WC2A 2AE, UK R. Pierse Department of Applied Economics, Cambridge University, Cambridge CB3 9DE, UK March Abstract Two related problems are considered.

The rst concerns the maximum likelihood estimation of theFile Size: KB. Chow, G.C. and An-Loh Lin () "Best linear unbiased estimation of missing observations in an economic time series." J.

of the Amer. Statistical Association 71 (September): Google Scholar | CrossrefCited by: Estimating Missing Observations in Economic Time Series Article (PDF Available) in Journal of the American Statistical Association 79() March with Reads How we measure 'reads'.

Damsleth () proposes to estimate missing observations by finding the optimal linear combi- nation of the forecasts and backforecasts in a time series which can be represented by an ARMA model. A period of missing observations can be both forecasted from previous values and back- Cited by: missing values for ARMA time series models.

[28] discussed different alternatives for the estimation of missing observation in stationary time series for autoregressive moving average models. [29] demonstrated that missing values in time series can be treated as unknown parameters and estimated by maximum likelihood or as random.

Result 3 (Estimation bias in dynamic models) In general, the OLS estimator is not unbiased in a dynamic regression model. As an example, it can be shown that the OLS estimator of the autoregressive coeﬃcient in the AR(1) model (3) is biased towards Size: KB. A time series is a sequence of observations on a variable taken at discrete intervals in time.

Fluctuations in most economic time series tend to persist over time, so elements near is the fundamental innovation or shock to the series at time t—the part that cannot be predicted based on the past history of the series.

Start studying MC Quiz Answers. Learn vocabulary, terms, and more with flashcards, games, and other study tools. allows you to make statements about the economic importance of your estimate d) adds and subtracts from the slope an unbalanced panel contains missing observations for at least one time period or one entity b) you cannot.

11 Autocorrelation In time series data, Y t is typically correlated with Y t j, this is called autocorrelation or serial correlation The jthautocovariance=Cov(Y t; t j) can be estimated by Cov\(Y t;Y t j) = 1 T XT t=j+1 Y t Y j+1;T Y t j Y 1;T j Yj+1;T is the sample average of Y computed over observations t = j + 1;;T Y1;T j is the sample average of Y computed over observations t = 1;;T j.

and each time period. Unbalanced Panel: There are missing data for at least one time period for at least one entity. Cross-Sectional Data: N different entities are observed at the same point in time Time Series Data: 1 entity is observed at T different time periods 16File Size: 1MB.

Then the best linear unbiased estimate of ψ is given by (8) Ψ ̂ = ∑ k c k z ̄ 1 (k) with (9) V(Ψ ̂)= ∑ k (c k 2 /r k (11))σ ∗2, where σ ∗2 =2(1−δ)σ 2. Thus for all contrasts in self-carryover effects to be estimable, it is both necessary and sufficient that r k (11)⩾1, k=1,2,t.

Chow and Lin(), "Best Linear Unbiased Interpolation, Distribution and Extrapolation of Time Series by Related Series", Review of Economics and Statistics, Fernandez(), "A Methodological Note on the Estimation of Time Series", Review of Economics.

Other Estimation Techniques 6. Time Series Models Time Series Models: a Classification An applied economic study usually proceeds in the following way: Statement of theory or hypothesis.

This step requires economic expertise. are said to be best linear unbiased estimators (BLUE). Methods for the estimation of missing values in time series David S.

Fung Edith Cowan University Follow this and additional works at: Part of the Applied Mathematics Commons, and the Longitudinal Data Analysis and Time Series Commons Recommended Citation Fung, D.

Ordinary Least Squares (OLS) is the most common estimation method for linear models—and that’s true for a good reason. As long as your model satisfies the OLS assumptions for linear regression, you can rest easy knowing that you’re getting the best possible estimates.

Time series forecasting is a difficult problem. Unlike classification and regression, time series data also adds a time dimension which imposes an ordering of observations.

This turns rows into a sequence which requires careful and specific handling. In this post, you will discover the top books for time series analysis and forecasting in R.

time series data must be ordered in correct chronological order (earliest to latest), rather than reverse order, or else tests and estimation methods are incorrect.

Consider a hypothetical data set {x1,x2,xp,y}, and a hypothesized linear regres-sion model yi = β0 +β1x1i +β2x2i ++βpxpi + Size: 98KB. ECONOMETRICS BRUCE E. HANSEN ©, University of Wisconsin Department of Economics This Revision: May Comments Welcome 1This manuscript may be printed and reproduced for individual or instructional use, but may not be printed for commercial purposes.As it has been stated before, the condition of (~) − (^) is equivalent to the property that the best linear unbiased estimator of is ^ (best in the sense that it has minimum variance).Autocorrelation is usually found in time-series data.

Economic time series often display a “memory” in that variation around the regression function is not independent sonallyadjustedpriceandquantityseriespublished by government agencies are examples. Time-series data are usually homoscedastic, so σ2 might be σ2 = σ2 ⎡ ⎢File Size: KB.