Panel Smooth Transition Regression Models

We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of “extreme regimes”. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms’ investment decisions in the presence of capital market imperfections.financial constraints; heterogenous panel; investment; misspecification test; nonlinear modelling panel data; smooth transition models

Panel Regression with Random Noise

The paper explores the effect of measurement errors on the estimation of a linear panel data model. The conventional fixed effects estimator, which ignores measurement errors, is biased. By correcting for the bias one can construct consistent and asymptotically normal estimators. In addition, we find estimates for the asymptotic variances of these estimators. The paper focuses on multiplicative errors, which are often deliberately added to the data in order to minimize their disclosure risk. They can be analyzed in a similar way as additive errors, but with some important and consequential differences.panel regression, multiplicative measurement errors, bias correction, asymptotic variance, disclosure control

Network and panel quantile effects via distribution regression

This paper provides a method to construct simultaneous con fidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confi dence bands for distribution functions constructed from fixed effects distribution regression estimators. These fi xed effects estimators are bias corrected to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confi dence bands for the quantile functions and effects have correct joint coverage in large samples. An empirical application to gravity models of trade illustrates the applicability of the methods to network data.https://arxiv.org/abs/1803.08154First author draf

Panel Smooth Transition Regression Models

We develop a non-dynamic panel smooth transition regression model with fixed individual effects. The model is useful for describing heterogenous panels, with regression coefficients that vary across individuals and over time. Heterogeneity is allowed for by assuming that these coefficients are continuous functions of an observable variable through a bounded function of this variable and fluctuate between a limited number (often two) of “extreme regimes”. The model can be viewed as a generalization of the threshold panel model of Hansen (1999). We extend the modelling strategy for univariate smooth transition regression models to the panel context. This comprises of model specification based on homogeneity tests, parameter estimation, and diagnostic checking, including tests for parameter constancy and no remaining nonlinearity. The new model is applied to describe firms' investment decisions in the presence of capital market imperfections.financial constraints; heterogeneous panel; invesatment; misspecification test; nonlinear modelling panel data; smooth transition model

Instrumental variables quantile regression for panel data with measurement errors

This paper develops an instrumental variables estimator for quantile regression in panel data with fixed effects. Asymptotic properties of the instrumental variables estimator are studied for large N and T when Na/T ! 0, for some a > 0. Wald and Kolmogorov-Smirnov type tests for general linear restrictions are developed. The estimator is applied to the problem of measurement errors in variables, which induces endogeneity and as a result bias in the model. We derive an approximation to the bias in the quantile regression fixed effects estimator in the presence of measurement error and show its connection to similar effects in standard least squares models. Monte Carlo simulations are conducted to evaluate the finite sample properties of the estimator in terms of bias and root mean squared error. Finally, the methods are applied to a model of firm investment. The results show interesting heterogeneity in the Tobin’s q and cash flow sensitivities of investment. In both cases, the sensitivities are monotonically increasing along the quantiles

PERAN EKONOMI DIGITAL DAN KETENAGAKERJAAN DALAM MENDORONG PERTUMBUHAN EKONOMI: STUDY 5 NEGARA ASEAN

The purpose of this study was to determine the effect of internet network users, e-commerce transaction value, total labor. This study uses a panel data regression approach and uses secondary data from the world bank and statista. This study uses a panel data regression approach to analyze the effect of E-Commerce and labor on the economic growth of 5 ASEAN countries in 2015-2021. The results confirm the hypothesis that the development of network users, the development of e-commerce value, and the number of workers also has a significant effect on economic growth

The Feldstein-Horioka Puzzle: a Panel SmoothTransition Regression Approach

This paper proposes an original framework to determine the relative influence of fivefactors on the Feldstein and Horioka result of OECD countries with a strong saving-investment association. Based on panel threshold regression models, we establishcountry-specific and time-specific saving retention coefficients for 24 OECD coun-tries over the period 1960-2000. These coefficients are assumed to change smoothly,as a function of five threshold variables, considered as the most important in theliterature devoted to the Feldstein and Horioka puzzle. The results show that; de-gree of openness, country size and current account to GDP ratios have the greatestinfluence on the investment-saving relationship.Feldstein Horioka puzzle, Panel Smooth Threshold Regression models,saving-investment association, capital mobility .

Appraising fiscal reaction functions

We estimate fiscal responses for an OECD panel, accounting for cross-country interactions, and also estimate the fiscal responses in a panel VAR. We find that governments have increased primary balances when facing higher government indebtedness, implying a Ricardian fiscal regime, while primary balances have improved to reduce government debt. These results hold for the single regression panel analysis and for the panel VAR.fiscal regimes, Panel VAR, cross-sectional dependence

Prediction Using Panel Data Regression with Spatial Random Effects

This paper considers some of the issues and difficulties relating to the use of spatial paneldata regression in prediction, illustrated by the effects of mass immigration on wages andincome levels in local authority areas of Great Britain. Motivated by contemporary urbaneconomics theory, and using recent advances in spatial econometrics, the panel regression haswages dependent on employment density and the efficiency of the labour force. There aretwo types of spatial interaction, a spatial lag of wages, and an autoregressive process for errorcomponents. The estimates suggest that increased employment densities will increase wagelevels, but wages may fall if migrants are under-qualified. This uncertainty highlights the factthat ex ante forecasting should be used with great caution as a basis for policy decisions.panel data, spatially correlated error components, economic geography, spatialeconometrics

Estimating Cointegrating Relations from a Cross Section

This paper specifies a regression model describing cointegrating relations between variables at the individual level. The models considered allow for homogeneous cointegration and heterogeneous cointegration. In both cases correlation between the regressors and the regression error can occur through aggregate shocks that are common to all cross-section units so the condition about the regressors being independent of the regression error is not imposed. It is shown that the estimator obtained by a cross-section regression performed at any point in time is a consistent estimator of the cointegrating parameters in the homogeneous case and of the cointegrating parameter means in the heterogeneous case. In both cases the limiting distribution of the cross-section estimator is normal.dynamic panel data models; non-stationary panel data; cointegrating relations; cross-section regression