Cluster Analysis — 2

Discovering a simple, robust typology to describe nine planets was straightforward. For a more challenging example, consider the cross-national data in Nations2.dta. These United Nations human-development variables could be applied to develop an empirical typology of nations. . use C:\data\Natlons2.dta, clear Working with the same data in Chapter7, we saw that nonlinear transformations such

Using Factor Scores in Regression by using Stata

Principal components and factor analysis often help to define new composite variables for further analysis. For example, the factor scores calculated by predict could become independent or dependent variables in subsequent regression analyses. To illustrate this process we turn to the survey dataset PNWsurvey211.dta. The 16 variables in this dataset represent a subset from

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Measurement and Structural Equation Models by using Stata

Chapter 8 took a first look at structural equation modeling, beginning with a regression-like example involving relationships among observed variables (Figure 8.15). Structural equation models can also incorporate measurement models, which resemble factor analysis. Measurement models posit one or more unobserved, factor-like latent variables that cause variation in observed variables. Figure 11.10 illustrates using

Smoothing by using Stata

Many time series exhibit high-frequency variations that make it difficult to discern underlying patterns. Smoothing such series breaks the data into two parts, one that varies gradually, and a second “rough” part containing the leftover rapid changes: data = smooth + rough To illustrate smoothing methods, we examine data on daily water consumption for

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Further Time Plot Examples by using Stata

Dataset Greenland temperature.dta contains a famous time series of temperature estimates reconstructed from the GISP2 ice core in central Greenland, covering time from about 50,000 years ago up through 1855 (Alley 2004). In scientific publications on these data, time has been represented by the variable age, in units of thousands of years before present.

Recent Climate Change by using Stata

Shifting scale from thousands of years to only the past thirty, the rest of this chapter looks at how climate has changed recently. Dataset Climate.dta contains three time series estimating monthly global temperatures from 1980 through 2010, along with four possible drivers or causes of temperature. Two of the temperature indexes derive from surface-temperature

Leads, Lags and Differences by using Stata

Time series analysis often involves lagged variables, or values from previous times. Lags can be specified by explicit subscripting. For example, the following command creates variable mei l, equal to the previous month’s Multivariate ENSO Index (mei) value: . generate mei_1 = mei[_n-1] Alternatively, we could accomplish the same thing, using tsset data, with

Correlograms by using Stata

Autocorrelation coefficients estimate the correlation between a variable and itself at particular lags. For example, first-order autocorrelation is the correlation between y t and y t-1 . Second order refers to Cor[ yt,y t-2], and so forth. A correlogram graphs correlation versus lag. Stata’s corrgram command provides simple correlograms and related information. The maximum

ARIMA Models by using Stata

Autoregressive integrated moving average (ARIMA) models can be estimated through the arima command. This command encompasses autoregressive (AR), moving average (MA), or ARIMA models. It also can estimate structural models that include one or more predictor variables and ARIMA disturbances. These are termed ARMAX models, for autoregressive moving average with exogenous variables. The general

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ARMAX Models by using Stata

Earlier in this chapter we saw that an OLS regression of ncdctemp on four lagged predictors gave a good fit to observed temperature values (Figure 12.2), as well as physically plausible parameter estimates. A Durbin-Watson test found significant autocorrelation among the residuals, however, which undermines the OLS t and F tests. ARMAX (autoregressive moving

Regression with Random Intercepts by using Stata

To illustrate xtmixed, we begin with county-level data on votes in the 2004 presidential election (Robinson 2005). In this election, George W. Bush (receiving 50.7% of the popular vote) defeated John Kerry (48.3%) and Ralph Nader (0.4%). One striking aspect of this election was its geographical pattern: Kerry won states on the West coast,

Random Intercepts and Slopes by using Stata

In Figure 13.2 we saw that, overall, the percentage of Bush votes tended to decline as population density increased. Our random-intercept model in the previous section accepted this generalization, while allowing intercepts to vary across regions. But what if the slope of the votes-density relationship also varies across regions? A quick look at scatterplots

Multiple Random Slopes by using Stata

To specify random coefficients on logdens, minority and colled we can simply add these variable names to the random-effects part of an xtmixed command. For later comparison tests, we save the estimation results with name full. Some of the iteration details have been omitted in the following output. . xtmixed bush logdens minority colled

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Nested Levels by using Stata

Mixed-effects models can include more than one nested level. The counties of our voting data, for example, are nested not only within census divisions, but also within states that are nested within census divisions. Might there exist random effects not only at the level of census divisions, but also at the smaller level of

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Repeated Measurements by using Stata

Dataset attract2.dta describes an unusual experiment carried out at a college undergraduate party, where some drinking apparently took place. In this experiment, 51 college students were asked to individually rate the attractiveness, on a scale from 1 to 10, of photographs of men and women unknown to them. The rating exercise was repeated by

Cross-Sectional Time Series by using Stata

This section applies xtmixed to a different kind of multilevel data: cross-sectional time series. Dataset Alaskaregions.dta contains time series of population for each of the 27 boroughs, municipalities or census areas that together make up the state of Alaska. These 27 regions are a fragment from the pan-Arctic human-dimensions database framework described by Hamilton

Mixed-Effects Logit Regression by using Stata

Since 1972, the General Social Survey (Davis et al. 2005) has tracked U.S. public opinion through a series of annual or biannual surveys, and made the data available for teaching and research. Dataset GSS_2010_SwS contains a small subset of variables and observations from the 2010 survey, including background variables along with answers to questions

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What is panel data? and What is Panel Analysis?

What Is Panel Data? In statistics and econometrics, panel data and longitudinal data are both multi-dimensional data involving measurements over time. Panel data is a subset of longitudinal data where observations are for the same subjects each time. Time series and cross-sectional data can be thought of as special cases of panel data that are in one dimension only (one panel member or individual for

Fixed-Effects Regression in Panel Data Analysis using Stata

In panel data, we use fixed-effects model whenever we are only interested in analyzing the impact of variables that vary over time. This model is “designed to study the causes of changes within an entity. A time-invariant characteristic cannot cause such a change, because it is constant for each entity” (Kohler and Kreuter. 2008).

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Random-Effects Regression in Panel Data Analysis using Stata

In panel data, the rationale behind random effects model is that: unlike the fixed-effects model, the variation across entities is assumed to be random and uncorrelated with the independent variables included in the model. So, “…the crucial distinction between fixed and random effects is whether the unobserved individual effect embodies elements that are correlated

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