Exploiting cross section variation for unit root inference in dynamic data

by Danny Quah

Publisher: London School of Economics, Financial Markets Group in London

Written in English
Published: Downloads: 724
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Edition Notes

Statementby Danny Quah.
SeriesFinancial markets discussion paper series / London School of Economics, Financial Markets Group -- no.171, Financial markets discussion paper (London School of Economics, Financial Markets Group) -- no.171.
ID Numbers
Open LibraryOL13975252M

Matching Methods for Causal Inference with Time-Series Cross-Sectional Data Kosuke Imaiy In Song Kimz Erik Wangx First Draft: Ap This Draft: January 4, Abstract Matching methods improve the validity of causal inference by reducing model dependence and o ering intuitive diagnostics. While they have become a part of the standard. Dynamic panel data estimators Dynamic panel data estimators In the context of panel data, we usually must deal with unobserved heterogeneity by applying the within (demeaning) transformation, as in one-way fixed effects models, or by taking first differences if the second dimension of the panel is a .   Purpose – There are several studies that investigate evidence for mean reversion in stock prices. However, there is no consensus as to whether stock prices are mean reverting or random walk (unit root) processes. The goal of this paper is to re‐examine mean reversion in stock prices. Design/methodology/approach – The authors use five different panel unit root tests, namely the Im. I Pooled cross sections: Mostly these type of data arise in surveys, where people are asked about e.g. their attitudes to political parties. This survey is repeated, T times, before elections every week. T is usually small. So we have several cross sections, but the persons asked are chosen randomly. Hardly any person of one cross section is.

Inference for Unit Roots in Dynamic Panels with Heteroscedastic and Serially Correlated Errors. by Harris, R. D. F. & Tzavalis, E. Why does book-to-market value of equity forecast cross-section . •Most macroeconomic data for real variables e.g. GDP or Consumption, is quarterly time series data. •The data for monetary variables such as Interest rates is often monthly time series data. 2. Cross sectional data is data associated with the values of many different firms or households that is collected at a single point in time. (i=1 File Size: 2MB.   Mean group tests for stationarity in heterogeneous panels Mean group tests for stationarity in heterogeneous panels Shin, Yongcheol; Snell, Andy Summary This paper proposes a panel‐based mean group test for the null of stationarity against the alternative of unit roots in the presence of both heterogeneity across cross‐section units and serial correlation across . Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes .

  Logit Models from Economics and Other Fields - Ebook written by J. S. Cramer. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Logit Models from Economics and Other Fields. The decibel (symbol: dB) is a relative unit of measurement corresponding to one tenth of is used to express the ratio of one value of a power or field quantity to another, on a logarithmic scale, the logarithmic quantity being called the power level or field level, respectively.. It can be used to express a change in value (e.g., +1 dB or −1 dB) or an absolute value. Regression Analysis with Time Series Data Obviously time series data different from cross section in terms of source of variation in x and y—temporal ordering 2nd difference—NOT randomly sampled in same way as cross sectional— each obs not i.i.d Why? Data over time is a “stochastic process”—we have one realization of. Balestra, P. and M. Nerlove, "Pooling Cross Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas," Econometrica, , pp. Baltagi B., J. Griffin and W. Xiong, " To Pool or Not to Pool: Homogeneous versus Heterogeneous Estimators Applied to Cigarette Demand, " Review of Economics and Statistics.

Exploiting cross section variation for unit root inference in dynamic data by Danny Quah Download PDF EPUB FB2

This paper considers unit root regressions in data having simultaneously extensive cross-section and time-series variation. The standard least squares estimators in such data structures turn out to have an asymptotic distribution that is neither O p (T −1) Dickey-Fuller, nor O p (N 1 2) normal and asymptotically by: This paper considers unit root regressions in data having simultaneously extensive cross-section and time-series variation.

The standard least squares estimators in such data structures turn out to have an asymptotic distribution that is neither O p (T −1) Dickey-Fuller, nor O p (N 1 2) normal and asymptotically unbiased. Instead, the estimator turns out to be consistent and asymptotically normal, Cited by: This paper considers unit root regressions in data having simultaneously extensive cross section and time-eries variation.

The standard least squares estimators in such data structures turn out to have an asymptotic distribution that is neither Dickey-Fuller, nor normal and asymptotically unbiased. Exploiting Cross Section Variation for Unit Root Inference in Dynamic Data Article (PDF Available) in Economics Letters 44() December with 76 Reads How we measure 'reads'.

Quah, Danny () Exploiting cross section variation for unit root inference in dynamic data. Econometrics; EM// (EM//). Suntory and Toyota International Centres for Economics and Related Disciplines, London, UK.

Full text not available from this repository. The standard least squares estimators in such data structures turn out to have an asymptotic distribution that is neither O p (T-1) Dickey-Fuller, nor O p (N-1/2) normal and symptotically unbiased.

instead, the estimator turns out to be consistent and asymptotically normalm, but has a nonvanishing Exploiting cross section variation for unit root inference in dynamic data book in its asymptotic distribution.

Exploiting Cross Section Variation for Unit Root Inference in Dynamic Data. By Danny Quah. Abstract. This paper considers unit root regressions in data having simultaneously extensive cross-section and time-series variation.

The standard least squares estimators in such data structures turn out to have an asymptotic distribution that is Author: Danny Quah. data over the cross-section dimension. Im et al. (), who consider Models (1b) and (1c), do not pool the data, but instead base their test on N separate unit root regressions.

Quah, D. () “Exploiting cross-section variation for unit root inference in dynamic data” Economic Lett Shin, D.W. and Lee, O. () “Test for asymmetry in possibly nonstationary time series data” Journal of Business and Economic Statist “Exploiting cross section variation for unit root inference in dynamic data,” Economics Letters, January “Galton’s fallacy and tests of the convergence hypothesis,” Scandinavian Journal of Economics, December (reprinted in T.

Andersen and File Size: 5MB. Working Paper: Exploiting Cross Section Variation for Unit Root Inference in Dynamic Data () Working Paper: Exploiting Cross Section Variation for Unit Root Inference in Dynamic Data () This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/TextCited by:   Slow moving trends Cross-section dependence Common correlated estimator Bootstrap Panel unit root tests Electronic supplementary material The online version of this article (doi: /s) contains supplementary material, which is available to authorized by: 2.

BibTeX @MISC{ting, author = {Danny Quah and An Esrc and Research Centre and Danny Quah and Danny Quah}, title = {Stockholm. Exploiting Cross Section Variation for Unit Root Inference in Dynamic Data by}, year = {}}.

more likely to satisfy the cross-section independence assumption required for pooling. The third relates to power, and follows from the fact that pooled tests exploit cross-section information and are more powerful than univariate unit root tests.

2This is a static factor model, and is to be distinguished from the dynamic factor model being. Exploiting Cross Section Variation for Unit Root Inference in Dynamic Data by By and Danny QuahAn Esrc, Research Centre and Danny Quah and Danny Quah Abstract.

Quah, D., Exploiting Cross-Section Variations for Unit Root Inference in Dynamic Panels, Economics Lett Table 1. The bias properties of the pooled estimator in the case of normally distributed local-to-unity parameters.

Panel A shows the numerical values for the limit function of the pooled estimator. In this chapter provides some theoretical issues and their application in testing for unit roots in panel data where the time dimension (T), Combination Unit Root Tests for Cross-Sectionally Correlated Quah, D.

Exploiting Cross-Section Variation for Unit Root Inference in Dynamic Data. Economics Letters 9– CrossRef Google Author: Panchanan Das.

Quah, D. () Exploiting Cross Section Variation for Unit root Inference in Dynamic Data. Economics Letters, 44, Pedroni, P.

() Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors, Oxford Bulletin of Economics and Statistics, 61, Misinterpreting the dynamic effects of aggregate demand and supply disturbances Economics Letters,49, (3), View citations (11) Exploiting cross-section variation for unit root inference in dynamic data Economics Letters,44, (), View citations () See also Working Paper ().

Quah, D. () Exploiting cross-section variation for unit root inference in dynamic data. Economics Lett 9 – Quah, D. () Empirics for economic growth and by: A PANIC ATTACK ON UNIT ROOTS AND COINTEGRATION The third relates to power, and follows from the fact that pooled tests exploit cross-section information and are more powerful than univariate unit root tests.

section that inference about unit roots. Quah, D. () Exploiting cross-section variation for unit root inference in dynamic data. Economics Lett 9 – Shiller, R.

& P. Perron () Testing the random walk hypothesis: Power versus frequency of by: W. Ploberger and P. Phillips, Optimal Testing for Unit Roots in Panel Data, D. Quah, Exploiting cross-section variation for unit root inference in dynamic data, Economics Letters, vol. 44, issue. pp.DOI: /(93) We demonstrate that panel unit root tests can have high power when a small fraction of the series are stationary and may lack power when a large fraction is stationary.

The acceptance or rejection of the null is thus not sufficient evidence to conclude that all series have a unit root or that all are stationary. cross-sectional unit.1 Panel data sets are more oriented toward cross-section analyses; they are wide but typically short.

Heterogeneity across units is an integral part—indeed, often the central focus—of the analysis. [See, e.g., Jones and Schurer ().] The analysis of panel or longitudinal data is the subject of one of the most activeFile Size: 1MB.

for spatial unit root tests in SAR models estimated from spatial cross-section data for regular and irregular lattices. We also compute critical SAC values for spatial cointegration tests for cross-section data that happen to be spatially nonstationary.

We show that parameter estimates in spatially cointegrated models are ‘superconsistent’. This paper develops a regression limit theory for nonstationary panel data with large numbers of cross section and time series observations.

The limit theory allows for both sequential limits and joins limits, and the relationship between these multidimensional limits is explored. The panel structures considered allow for no time series cointegration, heterogeneous cointegration, homogeneous.

Linear Regression Limit Theory for Nonstationary Panel Data, Econometrica, Quah, D., Exploiting Cross-Section Variation for Unit Root Inference in Dynamic Data, Economics Letters, Rapach, D.

E., Are Real GDP Levels Nonstationary. Evidence from Panel Data Tests, Seattle University. Unit Roots and Cointegration with Panel Data Quah, D., "Exploiting Cross-Section Variation for Unit Root Inference in Dynamic Data," Economics Letters, 44, Initial Conditions and Moment Restrictions in Dynamic Panel Data Models.

by Blundell, R. & Bond, S. Initial conditions and moment restrictions in dynamic panel data model by R. Blundell & Steven Bond; Taxes and Company Dividends: A Microeconometric Investigation Exploiting Cross-Section Variation in.

Stock characteristics, such as the firm's market capitalization, book-to-market ratio, or lagged return, are related to the stock's expected return, variance, and covariance with other stocks. 1 However, exploiting this fact in portfolio management has been, up to now, extremely difficult. The traditional mean–variance approach of Markowitz requires modeling the expected returns, variances Cited by: Econometrics: Panel Data Methods Jeffrey M.

Wooldridge quantify dynamic linkages, and perform valid inference when data are available on repeated cross sections. For linear models, the basis for many panel have the same time periods available for each cross-sectional unit. In other words, the panel data set is Size: KB.

State and local governments have recently experienced severe budget problems. Many state legislatures and governors have cut spending or raised taxes. Such changes in Cited by: