Economic Development Effects of Major and Minor League Teams and Stadiums

2020 ◽  
pp. 152700252097584
Author(s):  
Nola Agha ◽  
Daniel Rascher

Professional teams and leagues claim new stadiums lead to economic development. To test this, we utilize data from the Census Bureau on net establishment and employment changes across 871 Metropolitan and Micropolitan Statistical Areas from 2004 to 2012. Difference-in-differences and panel data techniques allow for a cross-sectional and time series comparison for both teams and new stadia in both professional and development leagues. Nearly all results from hundreds of models are insignificantly different from zero. Results from between- and random-effects models suggest that teams move into markets that already have higher employment and establishment growth. (JEL R58, H71, L83, Z28)

2014 ◽  
Vol 3 (1) ◽  
pp. 133-153 ◽  
Author(s):  
Andrew Bell ◽  
Kelvyn Jones

This article challenges Fixed Effects (FE) modeling as the ‘default’ for time-series-cross-sectional and panel data. Understanding different within and between effects is crucial when choosing modeling strategies. The downside of Random Effects (RE) modeling—correlated lower-level covariates and higher-level residuals—is omitted-variable bias, solvable with Mundlak's (1978a) formulation. Consequently, RE can provide everything that FE promises and more, as confirmed by Monte-Carlo simulations, which additionally show problems with Plümper and Troeger's FE Vector Decomposition method when data are unbalanced. As well as incorporating time-invariant variables, RE models are readily extendable, with random coefficients, cross-level interactions and complex variance functions. We argue not simply for technical solutions to endogeneity, but for the substantive importance of context/heterogeneity, modeled using RE. The implications extend beyond political science to all multilevel datasets. However, omitted variables could still bias estimated higher-level variable effects; as with any model, care is required in interpretation.


2017 ◽  
Vol 9 (4) ◽  
pp. 202
Author(s):  
Loice Koskei

Foreign portfolio inflows increase the liquidity and the volume of finance available for financial institutions. At the same time, as foreign portfolio inflows finances in part the capital requirements of local companies, it can also increase the competitiveness of these companies. A huge surge of the inflows can be very inflationary because this forces the Central Bank of Kenya to expand the country’s monetary base by releasing counterpart domestic currency which eventually feeds into the inflationary process. The main aim of this study was to find out the effect of international portfolio equity purchases on security returns of listed financial institutions in Kenya. The study population was 21 financial institutions listed on the Nairobi Securities Exchange. Using purposive sampling technique the study concentrated on 14 financial institutions. The research design of the study was causal as it is concerned more with understanding the connection between cause and effect relationships. The study adopted panel data regression using the Ordinary Least Squares (OLS) method where the data included time series and cross-sectional. A unit root test was carried in this study to examine stationarity of variables because it used panel data which combined both cross-sectional and time series information. Panel estimation results indicated that international portfolio equity purchases have no effect on stock returns of listed financial institutions in Kenya. The study recommended implementation of regulations and policies that would attract foreign portfolio equity inflows in financial institutions.


2017 ◽  
Vol 9 (4) ◽  
pp. 185
Author(s):  
Loice Koskei

Fluctuations of foreign portfolio equity intensify risk and unpredictability in financial institutions leading to high volatility. The main aim of this study was to find out the effect of foreign portfolio equity outflows on stock returns of listed financial institutions in Kenya. The study population was 21 financial institutions listed on the Nairobi Securities Exchange. Using purposive sampling technique the study concentrated on 14 financial institutions. The research design of the study was causal as it is concerned more with understanding the connection between cause and effect relationships. The study adopted panel data regression using the Ordinary Least Squares (OLS) method where the data included time series and cross-sectional. A unit root test was carried in this study to examine stationarity of variables because it used panel data which combined both cross-sectional and time series information. Panel estimation results indicated that foreign portfolio equity outflows have no effect on stock returns of listed financial institutions in Kenya. The study recommended implementation of policies that would curb foreign portfolio outflows in financial institutions in order to minimize reversals of foreign portfolio investments. 


2010 ◽  
Vol 26 (5) ◽  
pp. 1263-1304 ◽  
Author(s):  
Ryo Okui

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.


2016 ◽  
Vol 33 (2) ◽  
pp. 366-412 ◽  
Author(s):  
Lajos Horváth ◽  
Marie Hušková ◽  
Gregory Rice ◽  
Jia Wang

We consider the problem of estimating the common time of a change in the mean parameters of panel data when dependence is allowed between the cross-sectional units in the form of a common factor. A CUSUM type estimator is proposed, and we establish first and second order asymptotics that can be used to derive consistent confidence intervals for the time of change. Our results improve upon existing theory in two primary directions. Firstly, the conditions we impose on the model errors only pertain to the order of their long run moments, and hence our results hold for nearly all stationary time series models of interest, including nonlinear time series like the ARCH and GARCH processes. Secondly, we study how the asymptotic distribution and norming sequences of the estimator depend on the magnitude of the changes in each cross-section and the common factor loadings. The performance of our results in finite samples is demonstrated with a Monte Carlo simulation study, and we consider applications to two real data sets: the exchange rates of 23 currencies with respect to the US dollar, and the GDP per capita in 113 countries.


Author(s):  
Payam Mohammad Aliha ◽  
Tamat Sarmidi ◽  
Fathin Faizah Said

This paper investigates the impact of financial innovations on the demand for money using a dynamic panel data for 10 ASEAN member states from 2004 to 2012 and attempt to forecast the demand for money during 2013 – 2016 to compare between forecasting performance of the fixed effects model with that of random effects model and also to compare the forecasting accuracy of dynamic forecasting and static forecasting obtained from these two models. An autoregressive model by definition is when a value from a time series is regressed on previous values from that same time series. There are two types of forecasting namely dynamic forecast and static forecast. “Dynamic forecast will take previously forecasted values while static forecast will take actual values to make next step forecast. Panel effects models assist in controlling for unobserved heterogeneity when this heterogeneity is constant over time and correlated (fixed effects) or uncorrelated (random effects) with independent variables. Hausman test indicates that the random-effects model is appropriate. We use the conventional money demand that is enriched with the number of automated teller machines (ATM) to proxy for the effect of financial innovations on money demand. By comparing the magnitude of “Root Mean Squared Error” (RMSE) as a benchmark for the two forecasts (0.1164 for dynamic forecast versus 0.0635 for static forecast) we simply find out that static forecast is superior to dynamic forecast meaning that static forecast provides more accurate forecast compared to a dynamic forecast for the fixed-effects model. Therefore, we conclude the static forecast on the basis of the random-effects model provides the most accurate forecasting. The estimation result of the chosen random-effects regression also indicates the estimated coefficient of ATM is not significant meaning that ATM does not impact money demand in ASEAN countries.


Stats ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 185-202
Author(s):  
Bhimasankaram Pochiraju ◽  
Sridhar Seshadri ◽  
Dimitrios Thomakos ◽  
Konstantinos Nikolopoulos

For a symmetric matrix B, we determine the class of Q such that Q t BQ is non-negative definite and apply it to panel data estimation and forecasting: the Hausman test for testing the endogeneity of the random effects in panel data models. We show that the test can be performed if the estimated error variances in the fixed and random effects models satisfy a specific inequality. If it fails, we discuss the restrictions under which the test can be performed. We show that estimators satisfying the inequality exist. Furthermore, we discuss an application to a constrained quadratic minimization problem with an indefinite objective function.


Econometrica ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 171-201 ◽  
Author(s):  
Laura Liu ◽  
Hyungsik Roger Moon ◽  
Frank Schorfheide

This paper considers the problem of forecasting a collection of short time series using cross‐sectional information in panel data. We construct point predictors using Tweedie's formula for the posterior mean of heterogeneous coefficients under a correlated random effects distribution. This formula utilizes cross‐sectional information to transform the unit‐specific (quasi) maximum likelihood estimator into an approximation of the posterior mean under a prior distribution that equals the population distribution of the random coefficients. We show that the risk of a predictor based on a nonparametric kernel estimate of the Tweedie correction is asymptotically equivalent to the risk of a predictor that treats the correlated random effects distribution as known (ratio optimality). Our empirical Bayes predictor performs well compared to various competitors in a Monte Carlo study. In an empirical application, we use the predictor to forecast revenues for a large panel of bank holding companies and compare forecasts that condition on actual and severely adverse macroeconomic conditions.


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