Monte Carlo comparison of LCCA- and ML-based cointegration tests for panel var process with cross-sectional cointegrating vectors

2019 ◽  
Vol 65 (2) ◽  
pp. 173-182
Author(s):  
Piotr Kębłowski
Keyword(s):  
Cross Sections ◽  
Small Sample ◽  
Small Samples ◽  
Panel Var ◽  
Var Models ◽  
Rank Tests ◽  
Cross Sectional ◽  
Long Run ◽  
Cointegration Tests ◽  
Small Sample Properties

Small-sample properties of bootstrap cointegration rank tests for unrestricted panel VAR process are considered when long-run cross-sectional dependencies occur. It is shown that the bootstrap cointegration rank tests for the panel VAR model based on levels canonical correlation analysis are oversized, whereas the bootstrap cointegration rank tests based on maximum likelihood framework are undersized. Moreover, the former tests are in general outperformed by the latter in terms of performance. The results of the investigation indicate that the ML-based bootstrap cointegration rank tests perform well in small samples for small-sized panel VAR models with a few cross-sections.

Testing for bubbles in housing markets: some evidence for Brazil

2018 ◽  
Vol 11 (5) ◽  
pp. 754-770 ◽  
Author(s):  
Cássio da Nóbrega Besarria ◽  
Nelson Leitão Paes ◽  
Marcelo Eduardo Alves Silva
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Housing Prices ◽  
Market Price ◽  
Housing Markets ◽  
Cointegration Analysis ◽  
Small Samples ◽  
Content Type ◽  
Long Run ◽  
Cointegration Tests

Purpose Housing prices in Brazil have displayed an impressive growth in recent years, raising some concerns about the existence of a bubble in housing markets. In this paper, the authors implement an empirical methodology to identify whether or not there is a bubble in housing markets in Brazil. Design/methodology/approach Based on a theoretical model that establish that, in the absence of a bubble, a long-run equilibrium relationship should be observed between the market price of an asset and its dividends. The authors implement two methodologies. First, the authors assess whether there is a cointegration relationship between housing prices and housing rental prices. Second, the authors test whether the price-to-rent ratio is stationary. Findings The authors’ results show that there is evidence of a bubble in housing prices in Brazil. However, given the short span of the data, the authors perform a Monte Carlo simulation and show that the cointegration tests may be biased in small samples. Therefore, the authors should be caution when assessing the results. Research limitations/implications The results obtained from the cointegration analysis can be biased for small samples. Practical implications The information on the excessive increase of the prices of the properties in relation to their fundamental value can help in the decision-making on investment of the economic agents. Social implications These results corroborate the hypothesis that Brazil has an excessive appreciation in housing prices, and, as Silva and Besarria (2018) have suggested, this behavior explains, in part, the fact that the central bank has taken this issue into account when deciding about the stance of monetary policy of Brazil. Originality/value The originality is linked to the use of the Gregory-Hansen method of cointegration in the identification of bubbles and discussion of the limitations of the research through Monte Carlo simulation.

scholarly journals ASYMPTOTICALLY UNBIASED ESTIMATION OF AUTOCOVARIANCES AND AUTOCORRELATIONS WITH LONG PANEL DATA

2010 ◽  
Vol 26 (5) ◽  
pp. 1263-1304 ◽  
Author(s):  
Ryo Okui
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Unobserved Heterogeneity ◽  
Small Samples ◽  
Unbiased Estimation ◽  
Series Length ◽  
Cross Sectional ◽  
Long Run ◽  
The Individual ◽  
Time Invariant

An important reason for analyzing panel data is to observe the dynamic nature of an economic variable separately from its time-invariant unobserved heterogeneity. This paper examines how to estimate the autocovariances of a variable separately from its time-invariant unobserved heterogeneity. When both cross-sectional and time series sample sizes tend to infinity, we show that the within-group autocovariances are consistent, although they are severely biased when the time series length is short. The biases have the leading term that converges to the long-run variance of the individual dynamics. This paper develops methods to estimate the long-run variance in panel data settings and to alleviate the biases of the within-group autocovariances based on the proposed long-run variance estimators. Monte Carlo simulations reveal that the procedures developed in this paper effectively reduce the biases of the estimators for small samples.

scholarly journals Estimating logit models with small samples

2021 ◽  
pp. 1-16
Author(s):  
Carlisle Rainey ◽  
Kelly McCaskey
Keyword(s):  
Maximum Likelihood ◽  
American Political Science Review ◽  
Substantial Improvement ◽  
Small Sample ◽  
Small Samples ◽  
Noticeable Improvement ◽  
Small Sample Properties ◽  
Substantial Bias ◽  
Ml Estimator ◽  
American Political Science

Abstract In small samples, maximum likelihood (ML) estimates of logit model coefficients have substantial bias away from zero. As a solution, we remind political scientists of Firth's (1993, Biometrika, 80, 27–38) penalized maximum likelihood (PML) estimator. Prior research has described and used PML, especially in the context of separation, but its small sample properties remain under-appreciated. The PML estimator eliminates most of the bias and, perhaps more importantly, greatly reduces the variance of the usual ML estimator. Thus, researchers do not face a bias-variance tradeoff when choosing between the ML and PML estimators—the PML estimator has a smaller bias and a smaller variance. We use Monte Carlo simulations and a re-analysis of George and Epstein (1992, American Political Science Review, 86, 323–337) to show that the PML estimator offers a substantial improvement in small samples (e.g., 50 observations) and noticeable improvement even in larger samples (e.g., 1000 observations).

scholarly journals Nexus Between Carbon Emissions, Energy Consumption, Urbanization and Economic Growth in Asia: Evidence From Common Correlated Effects Mean Group Estimator (CCEMG)

2021 ◽  
Vol 8 ◽  
Author(s):  
Yusuf Babatunde Adeneye ◽  
Amar Hisham Jaaffar ◽  
Chai Aun Ooi ◽  
Say Keat Ooi
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Carbon Emission ◽  
Panel Cointegration ◽  
Energy Prices ◽  
Asian Countries ◽  
Cross Sectional ◽  
Long Run ◽  
Cointegration Tests ◽  
Common Correlated Effects

This study investigates the dynamic relationships between carbon emission, urbanization, energy consumption, and economic growth in a panel of 42 Asian countries for the period 2000–2014 using dynamic common correlated effects panel data modeling. This study employs second generation cross-sectional Pesaran (J. Appl. Econom., 2007, 22(2), 265-312) panel unit root, Westerlund panel cointegration tests (Econom. Stat., 2007, 69(6), 709-748), and Pesaran’s (Econometrica, 2006, 74(4), 967-1012) common correlated effects mean group estimation technique. These approaches allow for cross-sectional dependence, and are robust to the presence of common factors, serial correlation, and slope heterogeneity. The Common Correlated Effect Mean Group test reveals a high average coefficient of 0.602 between carbon emission and energy consumption while low coefficients of 0.114 and 0.184 for the pairs of carbon emission-urbanization and carbon emission-GDP, respectively for the panel as a whole, suggesting a cointegration between carbon emission, urbanization, energy consumption, and economic growth. The results indicate that there is relatively high carbon emission especially for highly populated and geopolitical risk Asian countries in the short run. Findings reveal long run relationships between the variables, which is attributed to the on-going carbon taxation and energy prices. Our results are robust to PMG-ARDL estimator. Overall, these findings cast important implications on renewable energy policy and urban planning insights for the policymakers.

scholarly journals The Relationship between Financial Development and Renewable Energy Consumption in South Asian Countries

2021 ◽  
Author(s):  
Sakib Amin ◽  
Farhan Khan ◽  
Ashfaqur Rahman
Keyword(s):  
Renewable Energy ◽  
Energy Consumption ◽  
Financial Development ◽  
Energy Use ◽  
Green Energy ◽  
Renewable Energy Consumption ◽  
Causality Test ◽  
Cross Sectional ◽  
Long Run ◽  
Cointegration Tests

Abstract We analyse how the financial development and green energy use are linked to the countries of South Asia from 1990 to 2018. Domestic credit to the private sector and renewable energy consumption is being used in this paper as indicators of financial development and the use of renewable energy. On the indication of cross-sectional dependency among the variables of the models, we apply second generation panel unit root tests and cointegration tests to check the stationarity properties and long-run cointegration relation among the variables. We find that variables are stationary at the first difference, and long-run cointegration exists. By applying robust dynamic heterogeneous and cross-section augmented estimators, we find that increase in GDP increases renewable energy consumption by 1.56-0.50%; however reduces by 0.07-0.03% after certain thresholds. Furthermore, increase in financial development, on average, reduces the propensity of renewable energy consumption by 0.15-0.07% in the long-run. On the other hand, the Dumitrescu-Hurlin panel causality test shows a unidirectional relationship from GDP to financial development and financial development to renewable energy consumption but not vice versa. We suggest that the selected countries revisit and restructure the renewable energy policy and emphasise institutional reforms to strengthen renewable energy development in the upcoming years.

INSURANCE AND REAL OUTPUT: THE KEY ROLE OF BANKING ACTIVITIES

2011 ◽  
Vol 17 (2) ◽  
pp. 235-260 ◽  
Author(s):  
Chien-Chiang Lee
Keyword(s):  
Structural Breaks ◽  
Banking Sector ◽  
Panel Cointegration ◽  
Insurance Market ◽  
Real Output ◽  
Cross Sectional ◽  
Positive Effects ◽  
Long Run ◽  
Cointegration Tests ◽  
Negative Effect

This paper applies panel cointegration tests and panel vector error correction models for 17 OECD countries and considers cross-sectional dependence and structural breaks to investigate the interrelationship between an insurance market's development and real output, controlling for banking activities. We first obtain evidence of a fairly strong long-run equilibrium relationship among them. Second, we find that insurance market development has positive effects on real output and that banking activities have an unfavorable, if not negative, effect on real output. In fact, insurance market activity is much more productive than banking sector activity. Finally, there exists bidirectional causality between insurance premiums and economic growth in the long run, suggesting the existence of the feedback hypothesis for the insurance–output nexus.

scholarly journals On the small sample properties of variants of Mardia’s and Srivastava’s kurtosis-based tests for multivariate normality

2012 ◽  
Vol 49 (2) ◽  
pp. 159-175 ◽  
Author(s):  
Zofia Hanusz ◽  
Joanna Tarasińska ◽  
Zbigniew Osypiuk
Keyword(s):  
Small Sample ◽  
Multivariate Normality ◽  
Small Samples ◽  
Standard Normal Distribution ◽  
Test Statistics ◽  
Significance Level ◽  
Nominal Significance ◽  
Standard Normal ◽  
Small Sample Properties ◽  
Mean And Variance

Summary The kurtosis-based tests of Mardia and Srivastava for assessing multivariate normality (MVN) are considered. The asymptotic standard normal distribution of their test statistics, under normality, is often misused for too small samples. The purpose of this paper is to suggest mean-and-variance corrected versions of the Mardia and Srivastava test statistics. Simulation studies evaluating both the true sizes and the powers of original and corrected tests against selected alternatives are presented and compared to the size and the power of the Henze-Zirkler test. The proposed corrected statistics have empirical sizes closer to a nominal significance level than the original ones. It is also shown that the corrected versions of the tests can be more powerful than the original ones.

Long-Term Equilibrium and Short-Term Dynamics between Metropolitan Housing Markets in China

2014 ◽  
Vol 905 ◽  
pp. 343-347
Author(s):  
Gao Lu Zou ◽  
K.W. Chau
Keyword(s):  
House Prices ◽  
Housing Markets ◽  
Small Sample ◽  
Short Term ◽  
Long Run ◽  
Regional Markets ◽  
Cointegration Tests ◽  
Causality Tests ◽  
Short Run

House prices across cities may form long-term relations. Geographic barriers could lead to lack of short-term dynamics. The paper aims to investigate the long-run equilibrium and/or short-run dynamics betweenmetropolitan house pricesin China. The study introduced two cointegration tests and various small-sample corrections. We conductedthe Toda-Yamamoto Granger causality tests. House prices betweencitiesin most regional markets did notshow long-term relations as well as short-term dynamics. Therefore, geographies andtransport costs between cities could reducethe centrifugal forces of city growth. Metropolitan housing markets are typically local.

On Small Sample Properties of R2 in a Linear Regression Model with Multivariate t Errors and Proxy Variables

1993 ◽  
Vol 9 (3) ◽  
pp. 504-515 ◽  
Author(s):  
Kazuhiro Ohtani ◽  
Hikaru Hasegawa
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Important Variable ◽  
Small Sample ◽  
Small Samples ◽  
Coefficient Of Determination ◽  
Proxy Variables ◽  
Small Sample Properties ◽  
Multivariate T

In this paper we consider the small sample properties of the coefficient of determination in a linear regression model with multivariate t errors when proxy variables are used instead of unobservable regressors. The results show that if the unobservable variable is an important variable, the adjusted coefficient of determination can be more unreliable in small samples than the unadjusted coefficient of determination from both viewpoints of the bias and the MSE.

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