Difference-in-differences with matching is a widely-used method to measure impact of interventions such policies, programs and treatments. However, this method requires baseline data, i.e., data before interventions, which are not always available for impact evaluation in reality. Instead, paneldata with two time periods are often collected after interventions begin. When there are paneldata without baseline data, one can use parametric fixed-effect regressions. Compared to matching methods, parametric regressions have limitation that they must impose functional assumptions on outcome.
Detecting recent changepoints in time-series can be important for short-term predic- tion, as we can then base predictions just on the data since the changepoint. In many applications we have paneldata, consisting of many related univariate time-series. We present a novel approach to detect sets of most recent changepoints in such paneldata that aims to pool information across time-series, so that we preferentially infer a most recent change at the same time-point in multiple series. Our approach is computa- tionally efficient as it involves analysing each time-series independently to obtain a profile-likelihood like quantity that summarises the evidence for the series having ei- ther no change or a specific value for its most recent changepoint. We then post-process this output from each time-series to obtain a potentially small set of times for the most recent changepoints, and, for each time, the set of series that has their most recent changepoint at that time. We demonstrate the usefulness of this method on two data sets: forecasting events in a telecommunications network and inference about changes in the net asset ratio for a panel of US firms.
First, there are many studies which analyse the exit dynamics from a sample of temporary workers. Specifically, these studies investigate the determinants and the timing of conversions of temporary contracts into permanent contracts. By restricting the analysis to the pool of temporary workers, however, such a research design misses any comparison group. For example, Mertens/McGinnity (2004) find that for Germany about 40% of fixed-term employees have a permanent contract in the following year, of which 70% are retained in their firm. The authors interpret this as support for the view of fixed-term contracts as screening contracts and bridges to permanent work for a substantive share of employees. The notion of temporary contracts as effective routes into permanent employment also applies to other Western and Northern European countries. Remery et al. (2002) report evidence that about one half of all temporary workers transited to permanent employment after two years, about one quarter continued temporary employment, and only about 8–11% became unemployment in the Netherlands during the period of 1986–1996. Using Swiss Household paneldata, Henneberger et al. (2004) show that 37% of temporary workers find a permanent job one year later. Booth et al. (2002) report that 36–38% of fixed-term workers made a transition to a permanent contract in the UK in the period of 1991–1997.
The main objective of this thesis is to examine factors that determine FDI inflows to go or not to go in to Sub-Saharan Africa countries. Forty four Sub-Saharan African countries were sampled over the period of 1990 up to 2013. A balanced paneldata analysis employed and estimated via pool ordinary least square (OLS), random effects (RE) and Fixed effect (FE). Among the three model based on husman test and an F-test fixed effect (FE) was found the appropriate model. To determine factors that affect FDI inflow, I used Trade openness, Natural Resource, Inflation, FDI lag, Return on investment, Corruption, Urban Population, Infrastructure, and Contract Enforcement as explanatory variables. The finding show that ;Trade openness, Natural Resources, FDI_1, Return on Investment (ROI), Urban Population, and Corruption are the most important determinants of FDI inflow at less than 5% level of significance . Whereas Infrastructure and Contract Enforcement, not statistically significance in determine FDI, but their sign of coefficient is as anticipated.
The relationship between HCE and GDP is the subject of a large portion of the literature in health economics. Many early contributions employed cross- sectional data to obtain estimates of this relationship. Without exception, it has been found that most of the observed variation in HCE can be explained by variation in GDP. However, many of these studies have been criticized for the smallness of their data sets and for the assumption that HCE is homoge- nously distributed across countries. More recent research has therefore resorted to paneldata, which offers a number of advantages over pure cross-sectional data. For instance, using multiple years of data increases the sample size while simultaneously allowing researchers to control for a wide range of time invariant country characteristics through the inclusion of country specific constants and trends. In addition, with multiple time series observations for each country, researchers are able to exploit the presence of unit roots and cointegration in HCE and GDP.
Hint: During your Stata sessions, use the help function at the top of the screen as often as you can. The descriptions and instructions there given can be downloaded and printed easily. In this way you can compile your own manual with paneldata routines in paper format.
Ordinary least squares regression ............ LHS=LWAGE Mean = 6.67635 Residuals Sum of squares = 522.20082 Standard error of e = .35447 Fit R-squared = .41121 Model test F[ 8, 4156] (prob) = 362.8(.0000) PanelData Analysis of LWAGE [ONE way] Unconditional ANOVA (No regressors) Source Variation Deg. Free. Mean Square Between 646.25374 594. 1.08797 Residual 240.65119 3570. .06741 Total 886.90494 4164. .21299
This study is concerned with understanding the factors that affect the life expectancy in 136 countries for the period 2002–2010. According to the life expectancy literature, the determinants of life expectancy can be classified into social, economic and environmental factors. In this respect, the paneldata method is employed to compute the relationship between life expectancy and selected economic, social and environmental factors. The results of this study suggest that unemployment and inflation are the main economic factors that influence the life expectancy negatively. But, the gross capital formation and gross national income and affect the life expectancy positively as well. The urbanity seems to be the main socio-environmental cause for mortality. According these results, this study presents a number of recommendations in order to improvement of life expectancy.
Guinea-Bissau became independent in 1973 and since then governments have elaborated different development policies to promote the country's comparative advantages. In the mid-1990s, they promoted the liberalization of the economy and actually the country belongs to several regional economic blocs and it increased its insertion into new extra-continental markets. The purpose of this study is to analyze the determinants of Guinea-Bissau’s exports for the 5 trading partners in the period 1990-2014, using static and paneldata dynamic models based on the traditional specification and extended gravity equation of international trade. The static paneldata methods suggest that exports react positively to the currency depreciation, incomes, population growth, common language, colonial heritage and geographical proximity (border effects), but decrease with the increase of trade cost, which is consistent with the conventional trade literature. The Arellano and Bond (1991) dynamic paneldata model confirms this pattern, also showing a positive correlation between exports and household consumption and investment. These results are important in guiding the country's international trade policies as they suggest the importance of the variables that recur in this standard trade literature.
The basic problem in estimation of model (3.3) is that the ordinary least squares (OLS) and other conventional panel regression techniques such as fixed effects (FE) and random effects (RE) estimators are generally biased and inconsistent. More specifically, due to the correlation between the lagged dependent variable and the error term OLS estimator becomes biased and inconsistent even if the ε are not serially correlated. For the FE estimator, the Within transformation wipes out the individual effects, but the transformed lagged dependent variable will still be correlated with the transformed errors even if the error term is serially uncorrelated. Although, the magnitude of this bias depends on the time dimension (see Nickell (1981) and Alvarez & Arellano (2003) for the nature of this bias), in finite samples this bias is shown to be severe. the same problem occurs with the random effects generalized least squares (GLS) estimator, which uses quasi-demeaning transformation (see Baltagi (1995) Chapter 2) to eliminate the individual effects. As a solution to this problem generalized method of moments (GMM) techniques are widely used in estimation of paneldata model.
Abstract: This paper develops methods of Bayesian inference in a cointegrating paneldata model. This model involves each cross-sectional unit having a vector error correction representation. It is ß exible in the sense that diﬀerent cross-sectional units can have diﬀerent cointegration ranks and cointegration spaces. Furthermore, the parameters which characterize short-run dynamics and deter- ministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coeﬃcients in diﬀerent cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for eﬃcient posterior inference. Our methods are illustrated using real and arti Þ cial data.
The average price paid for 100 gr. of organic chicken varied between 2006 and 2010, whereas the price for conventional chicken has been more stable. It is interesting to notice that in 2006 and 2007 the average price was lower for organic than for conventional chicken. It might be possible that the organic products were bought at a discounted price (this is an assumption, as this is not mentioned in the paneldata) (Figure 3).
Most of the existing work in the literature on nonstationary nonlinear paneldata models requires a large number of time periods- see e.g. Moon and Phillips (2000). One exception is Chen and Khan (2008), who assumed correlated random effects. Here, we look for assump- tions motivated from the previous literature, that aim at relaxing stationarity. The issue is that standard mean and median independence assumptions on the marginals of ǫ’s do not allow us to provide any restrictions on β, i.e., the sharp set is the trivial set- i.e. the original parameter space. The intuition is that the marginal median independence assumption places no restriction on the conditional median of (ǫ i1 − ǫ i2 ). Also, mean independence assumptions
To fill this void, we contribute to the literature by considering a generalized paneldata model of polychotomous switching which also allows for the dependence between unobserved effects and covariates in the model. The model we consider can be thought of as a generalization of a standard switching regression model. We show that Wooldridge’s (1995) estimator can be readily extended to the case of polychotomous and/or sequential selection. For consistency, our method requires strict exogeneity of covariates conditional on unobserved effects. We showcase our model using an empirical illustration in which we estimate scope economies for the publicly owned electric utilities in the U.S. during the period from 2001 to 2003.
In tourism demand modelling, after a short period of application of standard and classic paneldata models (OLS with fixed effects or random effects), the literature had been characterized by a high volume of dynamic panel models. Additionally, from the second half of 2000 onwards, several of the works employ cointegration techniques. The first empirical papers, based on the classic paneldata methods, were published in the beginning of the 2000s. These studies focused generally on the prices elasticities or on the impact of political risk and violence on international tourism arrivals. For example, Ledesma- Rodriguez and Navarro- Ibanez (2001) used annual data from 1979 to 1997 to study factors affecting arrivals to Tenerife from 13 markets. They found arrivals to be elastic with respect to income and inelastic with respect to prices and transport cost in the long run. Espinet et al. (2003) used paneldata with random effects for a hedonic evaluation based on 86,000 prices between 1991 and 1998. The data concerned hotels in the southern Costa Brava region. Their results indicate a real and significant effect from the quality to the price.
Dynamic bunching estimation follows the intuition of the static approach but extends it to dynamic pro- cesses. In light of the concerns about the identifying assumption that the notch only affects the distribution locally, I propose three variants that exploit paneldata in differing ways. I present these methods in order of increasing complexity. The first method is ideally suited to a notch that agents face only once, which avoids concerns about serial dependence and repeated bunching. This method simply involves widening bins to create one unselected “treatment” bin around the notch, and with paneldata it provides estimates of short- and long-run effects and of serial dependence. The second and third methods condition on income in the year prior to the year that an agent approaches the notch, implying that the localness assumption is applied not to the overall income distribution but only to the distribution of one-year growth rates. This identification strategy can be applied even when agents face the same notch repeatedly. I first implement it with bins and OLS, which provides transparent evidence of manipulation, improved heterogeneity analysis, and tests of long-run effects. The OLS implementation is easy to execute and has already been employed, based on an earlier draft of this paper, by St.Clair (2016). The final method uses maximum likelihood esti- mation to implement the same identification strategy. Though more complicated, the MLE implementation retains the flexibility of the binning approach while offering improved precision and the first estimates of extensive-margin responses in the literature. R code is provided with this paper to facilitate adoption.
Abstract -This study explained the construction of paneldata models, compared the estimation performance of the Fixed Effects model and the Random Effects model. Our novelty model focuses on the 10 best travel destinations according to Tripadvisor award 2018. The most appropriate model is the Fixed Effect model with Individual Effect. The positive effect on Gross Domestic Product (GDP) partially arising from Number of International tourist arrivals, International Tourism Receipts, and International Tourism Expenditures and the negative effect on GDP partially arising from Total Employment in the tourism sector. The International Tourist Arrivals, International Tourism Receipts, International Tourism Expenditures, and Total Employment in the tourism sectors have simultaneous significant influence on GDP and adjusted together can explain changes in GDP by 95.054% from France, UK, Italy, Indonesia, Greece, Spain, Czech Rep., Morocco, Turkey, and the USA while the remaining 4.946% is explained by other variables outside the model.
The contribution of this paper is to provide of theoretical results for paneldata model, We consider the random effect paneldata model . Maximum likelihood method is employed to making inferences on the model, and we prove some properties about the likelihood estimators and likelihood ratio test statistics are given here.
the former can be found in any cross-country panel database, the latter is difficult to obtain. To our knowledge the largest paneldata covering both developed and developing countries was constructed by Crego, Larson, Butzer, and Mundlak (2000). Those authors provide a comprehensive study to estimate aggregate capital stocks in a systematic way and they use the perpetual inventory method to obtain fixed capital estimates for 63 countries between 1948 and 1992 resulting in 1323 observations. We follow their methodology and construct our own capital stock estimates using data from gross capital formation, using the perpetual inventory method with a yearly depreciation of 7% and obtain estimates for the period 1973 to 2007. Our final balanced paneldata contains 138 countries and 35 years (1973 to 2007) resulting in 4830 observations. 6 We apply Levin, Lin,