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Well, this is my private note and how to run the panel regression in Matlab. I hope this will help me and anyone who is interested in this topic!

Mixed-Effect Modeling using MATLAB

Panel Data refers to observations on a cross-section (of individuals, households, firms, municipalities, states or countries) that are repeated over time (five-year intervals, annual, quarters, weeks, days) Due to the nature of the data we cannot assume that the observations are independently distributed across time.

This example introduces fitting different types of Panel Regression models using Mixed-Effect modelling techniques.

Copyright (c) 2014, MathWorks, Inc.

- Description of the Data
- Load Data
- Preprocess Data
- Balanced and Unbalanced Panel
- Cobb–Douglas production function
- Ordinary Least Squares (OLS) Model
- Fixed-Effects Model
- One-Way Random effects Model (Mixed-Effect Model)
- One-Way Random Effects Model with different predictors
- Compare the two One-Way Random Effects models for improvements
- Two-Way Random effects Model (Mixed-Effect Model)
- Multilevel (Hierarchical) Model: STATE nested in REGION
- Regional random effects for UNEMP and HWY with possible correlation
- Table showing Cobb–Douglas Production Function Estimates for different models
- Bibliography

This panel consists of annual observations of 48 Continental U.S. States, over the period 1970–86 (17 years). Panel data allows you to control for variables you cannot observe. Examples are cultural factors or differences in business practices across companies; or variables that change over time but not across entities (i.e. national policies, federal regulations, international agreements, etc.). That is, it accounts for individual heterogeneity. This data set was provided by Munnell (1990)

**GDP:**Gross State Product by state, Bureau of Economic Analysis**PUB_CAP:**Public capital which includes (HWY, WATER, UTIL)**HWY:**Highways and streets capital stock**WATER:**Water and sewer facilities capital stock**UTIL:**Other public buildings and structures capital stock**PVT_CAP:**Private capital stock based on the Bureau of Economic Analysis national stock estimates**EMP:**Employees on non-agricultural payrolls, Bureau of Labor Statistics**UNEMP:**Unemployment Rate, included capturing business cycle effects, Bureau of Labor Statistics

All dollar figures are millions; the employment figure is in thousands Reference:

**STATE and REGION data**

- GF = Gulf =AL, FL, LA, MS,
- MW = Midwest =IL, IN, KY, Ml, MN, OH, Wl,
- MA = Mid Atlantic =DE, MD, NJ, NY, PA, VA,
- MT = Mountain =CO, ID, MT, ND, SD, WY,
- NE = New England=CT, ME, MA, NH, RI, VT,
- SO = South =GA, NC, SC, TN, WV, R,
- SW = Southwest =AZ, NV, NM, TX, UT,
- CN = Central =AK, IA, KS, MO, NE, OK,
- WC = West Coast =CA, OR, WA.

```
clear, clc, close all
loadPanelData
```

Convert STATE, YR and REGION to categorical

publicdata.STATE = categorical(publicdata.STATE); publicdata.REGION = categorical(publicdata.REGION); publicdata.YR = categorical(publicdata.YR);

For this Panel Regression example, we will fit the famous Cobb–Douglas production function which uses the log transform of the following variables: GDP, PUB_CAP, PC and EMP

logvars = {'PUB_CAP','HWY','WATER','UTIL','PVT_CAP','EMP','GDP'}; publicdata = [publicdata varfun(@log,publicdata(:,logvars))]; publicdata(:,logvars) = []; clear logvars

The Panel Dataset we are working with is a balanced or complete panel. This means that all observations for each state are measured at the same time points (1970 - 1986). If the GDP is unstacked with STATE as the indicator variable and observed over time, notice that there are no missing values.

unstacked = unstack(publicdata,'log_GDP','STATE',... 'GroupingVariable','YR');

If we randomly remove certain observations from the data and unstack GDP we now notice missing information which is not clear when observing the stacked panel.

publicdata_missing_years = publicdata; publicdata_missing_years(randperm(100,20),:) = []; % An alternative way to visualize the same data is to unstack the panel unstacked_missing = unstack(publicdata_missing_years,... 'log_GDP','STATE','GroupingVariable','YR'); unstacked_missing = sortrows(unstacked_missing,'YR','ascend');

Unbalanced or incomplete panels can occur if some states have data going back several more years than others or some states don't have recorded observations for certain years. `fitlme` can fit both types of panels. Because Random Effects are assumed to come from a common distribution, Mixed-Effects models share information between groups. This can improve the precision of predictions for groups that have fewer data points.

Model the production function relationship investigating the productivity of public capital in private production as proposed by Munnell(1990):

\( log(GDP) = \beta_0 + \beta_1 UNEMP + \beta_2 log(PUB\_CAP) + \)

\( \beta_3 log(PVT\_CAP) + \beta_4 log(EMP) + \epsilon \)

An OLS model is also called Pooled OLS Regression since it combines the cross-section and time-series aspects of the data. It is sometimes referred to as a population-averaged model. The assumption here is that all statistical requirements of OLS are met.

lm_ols = fitlm(publicdata,... 'log_GDP ~ 1 + UNEMP + log_PUB_CAP + log_PVT_CAP + log_EMP')

lm_ols = Linear regression model: log_GDP ~ 1 + UNEMP + log_PUB_CAP + log_PVT_CAP + log_EMP Estimated Coefficients: Estimate SE tStat pValue _________ _________ _______ ___________ (Intercept) 1.6433 0.057587 28.536 1.4849e-124 UNEMP -0.006733 0.0014164 -4.7537 2.3632e-06 log_PUB_CAP 0.15501 0.017154 9.0363 1.1674e-18 log_PVT_CAP 0.30919 0.010272 30.1 3.0988e-134 log_EMP 0.59393 0.013747 43.203 1.5408e-212 Number of observations: 816, Error degrees of freedom: 811 Root Mean Squared Error: 0.0881 R-squared: 0.993, Adjusted R-Squared: 0.993 F-statistic vs. constant model: 2.72e+04, p-value = 0

OLS reports that public capital is productive and economically significant in the state's private production (log_PUB_CAP: 0.155) and the p-Values show that the fit is statistically significant. Ignoring that the data is grouped by state. From the model, we can infer that at the state level, that public capital has a significant positive impact on the level of output and does indeed belong in the production function. However, it should be noted that pooled regression may lead to underestimated standard errors and inflated t-statistics. OLS assumes that the observation error is independent across observations. However, when the data is grouped as in this case, within-group observations are correlated with each other.

```
boxplot(lm_ols.Residuals.Raw,publicdata.STATE)
ylabel('OLS Residuals')
```

The boxplot of OLS residuals by STATE shows that the within state residuals are either all positive or all negative and are not centred at 0. This violates the fundamental assumption of OLS and the resulting inference may not be valid.

Fixed-Effect models treat individual effects as fixed but unknown parameters, so as to allow the unobserved individual effects to be correlated with the included variables. Panel Data Fixed-Effect models also refer to Least Squares with Dummy Variables (LSDV) models. All the state-specific information is incorporated in the dummy variables. `fitlm` automatically does this under the hood, and it is ideal for fixed-effect models with a moderate number of cross-sectional units.

lm_fe = fitlm(publicdata,... 'log_GDP ~ 1 + UNEMP + log_PUB_CAP + log_PVT_CAP + log_EMP + STATE') disp(' '), disp(['log_PUB_CAP OLS Estimate: ', num2str(lm_ols.Coefficients{'log_PUB_CAP',1})]) disp(['log_PUB_CAP FE Estimate: ', num2str(lm_fe.Coefficients{'log_PUB_CAP',1})])

lm_fe = Linear regression model: log_GDP ~ 1 + STATE + UNEMP + log_PUB_CAP + log_PVT_CAP + log_EMP Estimated Coefficients: Estimate SE tStat pValue __________ __________ ________ ___________ (Intercept) 2.2016 0.176 12.509 8.5462e-33 STATE_AR 0.061399 0.01672 3.6722 0.00025726 STATE_AZ 0.16647 0.013634 12.21 1.8928e-31 STATE_CA 0.29881 0.036815 8.1164 1.9175e-15 STATE_CO 0.19429 0.013746 14.135 1.8158e-40 STATE_CT 0.26959 0.018807 14.334 1.903e-41 STATE_DE 0.21184 0.022432 9.444 4.2496e-20 STATE_FL 0.13154 0.020489 6.4198 2.3943e-10 STATE_GA 0.056591 0.016073 3.521 0.00045546 STATE_IA 0.12555 0.014002 8.9666 2.3216e-18 STATE_ID 0.1368 0.025164 5.4363 7.3251e-08 STATE_IL 0.1857 0.024067 7.7161 3.7521e-14 STATE_IN 0.057766 0.014086 4.101 4.5557e-05 STATE_KS 0.1371 0.015636 8.7683 1.1666e-17 STATE_KY 0.19767 0.014 14.119 2.1724e-40 STATE_LA 0.31305 0.02213 14.146 1.6006e-40 STATE_MA 0.1606 0.02308 6.9586 7.3977e-12 STATE_MD 0.19866 0.020253 9.8092 1.7882e-21 STATE_ME 0.066799 0.025235 2.647 0.008287 STATE_MI 0.21538 0.02168 9.9344 5.9093e-22 STATE_MN 0.11393 0.016549 6.8845 1.2107e-11 STATE_MO 0.11201 0.015263 7.3384 5.5345e-13 STATE_MS 0.048408 0.014256 3.3955 0.00072041 STATE_MT 0.14654 0.026661 5.4962 5.2903e-08 STATE_NC 0.036009 0.016971 2.1217 0.03418 STATE_ND 0.14228 0.029669 4.7955 1.9508e-06 STATE_NE 0.10966 0.016396 6.6883 4.3605e-11 STATE_NH 0.12253 0.027357 4.479 8.6463e-06 STATE_NJ 0.24125 0.020144 11.977 2.0566e-30 STATE_NM 0.25276 0.022268 11.351 1.0563e-27 STATE_NV 0.14027 0.024615 5.6986 1.7244e-08 STATE_NY 0.27437 0.038809 7.0699 3.5037e-12 STATE_OH 0.12103 0.022762 5.3171 1.3856e-07 STATE_OK 0.21432 0.016437 13.039 3.1226e-35 STATE_OR 0.14929 0.013996 10.666 7.4331e-25 STATE_PA 0.087726 0.024799 3.5375 0.00042837 STATE_RI 0.18673 0.030334 6.1559 1.2049e-09 STATE_SC -0.082223 0.016847 -4.8806 1.2881e-06 STATE_SD 0.088063 0.023997 3.6697 0.00025968 STATE_TN 0.027481 0.015532 1.7693 0.077245 STATE_TX 0.19204 0.025172 7.6292 7.0358e-14 STATE_UT 0.12704 0.017077 7.4394 2.725e-13 STATE_VA 0.17885 0.018246 9.8022 1.9006e-21 STATE_VT 0.13458 0.028763 4.679 3.409e-06 STATE_WA 0.24516 0.020582 11.912 3.9728e-30 STATE_WI 0.12734 0.017036 7.475 2.1186e-13 STATE_WV 0.091533 0.018279 5.0077 6.8458e-07 STATE_WY 0.44694 0.039891 11.204 4.4053e-27 UNEMP -0.0052977 0.00098873 -5.3582 1.1139e-07 log_PUB_CAP -0.02615 0.029002 -0.90166 0.36752 log_PVT_CAP 0.29201 0.02512 11.625 7.0751e-29 log_EMP 0.76816 0.030092 25.527 2.0215e-104 Number of observations: 816, Error degrees of freedom: 764 Root Mean Squared Error: 0.0381 R-squared: 0.999, Adjusted R-Squared: 0.999 F-statistic vs. constant model: 1.14e+04, p-value = 0 log_PUB_CAP OLS Estimate: 0.15501 log_PUB_CAP FE Estimate: -0.02615

Notice that the output contains coefficient estimates for each state. In contrast to OLS, the LSDV model reports that public capital is not as economically significant (log_PUB_CAP: -0.02) However, disadvantages to this approach include loss in degrees of freedom when there are a large number of groups and therefore a large number of estimated fixed parameters. The complexity of this model then increases linearly with the number of STATEs. This can be a problem when dealing with a smaller set of observations.

Group-specific effects can be modelled using random effects. The state-specific observations in this example are assumed drawn at random from a population. `fitlme` uses Maximum Likelihood (ML) or Restricted Maximum Likelihood (REML) estimation to compute the coefficients.

lme_oneway = fitlme(publicdata,... 'log_GDP ~ 1 + log_PUB_CAP + log_PVT_CAP + log_EMP + UNEMP + (1 | STATE)',... 'FitMethod','REML') disp(' ') disp(['log_PUB_CAP OLS Estimate: ', num2str(lm_ols.Coefficients{'log_PUB_CAP',1})]) disp(['log_PUB_CAP FE Estimate: ', num2str(lm_fe.Coefficients{'log_PUB_CAP',1})]) disp(['log_PUB_CAP RE Estimate: ', num2str(lme_oneway.Coefficients{3,2})])

lme_oneway = Linear mixed-effects model fit by REML Model information: Number of observations 816 Fixed effects coefficients 5 Random effects coefficients 48 Covariance parameters 2 Formula: log_GDP ~ 1 + UNEMP + log_PUB_CAP + log_PVT_CAP + log_EMP + (1 | STATE) Model fit statistics: AIC BIC LogLikelihood Deviance -2750.7 -2717.8 1382.4 -2764.7 Fixed effects coefficients (95% CIs): Name Estimate SE tStat DF {'(Intercept)'} 2.1493 0.1357 15.839 811 {'UNEMP' } -0.006116 0.00091025 -6.719 811 {'log_PUB_CAP'} 0.0023163 0.02365 0.097941 811 {'log_PVT_CAP'} 0.30933 0.020082 15.404 811 {'log_EMP' } 0.73241 0.025211 29.051 811 pValue Lower Upper 1.9677e-49 1.883 2.4157 3.4438e-11 -0.0079028 -0.0043293 0.922 -0.044106 0.048739 3.7475e-47 0.26991 0.34875 9.6728e-128 0.68292 0.78189 Random effects covariance parameters (95% CIs): Group: STATE (48 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std'} 0.087071 Lower Upper 0.070498 0.10754 Group: Error Name Estimate Lower Upper {'Res Std'} 0.038157 0.036289 0.04012 log_PUB_CAP OLS Estimate: 0.15501 log_PUB_CAP FE Estimate: -0.02615 log_PUB_CAP RE Estimate: 0.0023163

In contrast to OLS, a mixed-effect model with STATE specific random effect finds that public capital is economically insignificant in the state's private production (log_PUB_CAP: 0.0023).

Mixed-Effects models such as the one above are also known as error component models, random co-efficient regression models, covariance structure models, or multilevel models. The Statistics Toolbox follows the Mixed-Effects terminology, where the regression model consists of two separate parts, fixed effects and random effects. Fixed-effects terms are the conventional linear regression part, and the random effects are associated with group and are assumed to have a prior distribution.

An important aspect of building an accurate predictive model it to choose the right set of predictors. We can attempt to improve the existing random-effects model by including HWY, WATER and UTIL as separate predictors instead of PUB_CAP.

Recall that PUB_CAP = HWY + WATER + UTIL

clc lme_oneway_new = fitlme(publicdata,... ['log_GDP ~ 1 + log_HWY + log_WATER + log_UTIL + log_PVT_CAP +'... 'log_EMP + UNEMP + (1 | STATE)'],'FitMethod','REML')

lme_oneway_new = Linear mixed-effects model fit by REML Model information: Number of observations 816 Fixed effects coefficients 7 Random effects coefficients 48 Covariance parameters 2 Formula: Linear Mixed Formula with 7 predictors. Model fit statistics: AIC BIC LogLikelihood Deviance -2787.7 -2745.5 1402.9 -2805.7 Fixed effects coefficients (95% CIs): Name Estimate SE tStat DF {'(Intercept)'} 2.1782 0.14939 14.581 809 {'UNEMP' } -0.0057842 0.00089788 -6.4421 809 {'log_HWY' } 0.062865 0.022921 2.7427 809 {'log_WATER' } 0.07543 0.014041 5.3722 809 {'log_UTIL' } -0.10099 0.01716 -5.8853 809 {'log_PVT_CAP'} 0.2694 0.020855 12.917 809 {'log_EMP' } 0.7557 0.025775 29.319 809 pValue Lower Upper 6.2342e-43 1.885 2.4715 2.0208e-10 -0.0075466 -0.0040218 0.0062284 0.017874 0.10786 1.0178e-07 0.047869 0.10299 5.8166e-09 -0.13468 -0.06731 7.7082e-35 0.22846 0.31033 2.6003e-129 0.70511 0.8063 Random effects covariance parameters (95% CIs): Group: STATE (48 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std'} 0.089868 Lower Upper 0.07251 0.11138 Group: Error Name Estimate Lower Upper {'Res Std'} 0.036808 0.035003 0.038706

The log_HWY, log_WATER and log_UTIL estimates are statistically significant. log_UTIL appears to have a negative economic impact on the states private capital.

clc compare(lme_oneway, lme_oneway_new)

ans = THEORETICAL LIKELIHOOD RATIO TEST Model DF AIC BIC LogLik LRStat deltaDF lme_oneway 7 -2750.7 -2717.8 1382.4 lme_oneway_new 9 -2787.7 -2745.5 1402.9 41.043 2 pValue 1.2234e-09

p-Value of the likelihood ratio test is close to zero. This indicates that the random effect model is significantly better than the pure fixed-effect model. Since the p-Value of the log_PUB_CAP estimate in the random-effects model is not significant, we can attempt to improve the model.

The two-way random-effects model takes into account the random effect with time as the grouping variable which is individual-invariant. This accounts for any time-specific effect that is not included in the regression. Economically this could account for events that are specific to a year that affect the state private production.

clc lme_twoway = fitlme(publicdata,... ['log_GDP ~ 1 + log_HWY + log_WATER + log_UTIL + log_PVT_CAP +'... 'log_EMP + UNEMP + (1 | STATE) + (1|YR)'],'FitMethod','REML') compare(lme_oneway, lme_twoway)

lme_twoway = Linear mixed-effects model fit by REML Model information: Number of observations 816 Fixed effects coefficients 7 Random effects coefficients 65 Covariance parameters 3 Formula: Linear Mixed Formula with 8 predictors. Model fit statistics: AIC BIC LogLikelihood Deviance -2876.3 -2829.3 1448.1 -2896.3 Fixed effects coefficients (95% CIs): Name Estimate SE tStat DF {'(Intercept)'} 2.4129 0.15442 15.626 809 {'UNEMP' } -0.0042953 0.0010625 -4.0428 809 {'log_HWY' } 0.08226 0.024747 3.324 809 {'log_WATER' } 0.057152 0.013744 4.1583 809 {'log_UTIL' } -0.090081 0.016155 -5.5759 809 {'log_PVT_CAP'} 0.2212 0.022162 9.981 809 {'log_EMP' } 0.7751 0.024684 31.401 809 pValue Lower Upper 2.6764e-48 2.1098 2.716 5.7872e-05 -0.0063809 -0.0022098 0.00092711 0.033684 0.13084 3.5486e-05 0.030174 0.084131 3.3564e-08 -0.12179 -0.05837 3.3338e-22 0.1777 0.2647 3.7471e-142 0.72664 0.82355 Random effects covariance parameters (95% CIs): Group: STATE (48 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std'} 0.093399 Lower Upper 0.075159 0.11606 Group: YR (17 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std'} 0.015926 Lower Upper 0.010685 0.023737 Group: Error Name Estimate Lower Upper {'Res Std'} 0.033774 0.032096 0.035541 ans = THEORETICAL LIKELIHOOD RATIO TEST Model DF AIC BIC LogLik LRStat deltaDF lme_oneway 7 -2750.7 -2717.8 1382.4 lme_twoway 10 -2876.3 -2829.3 1448.1 131.59 3 pValue 0

The likelihood ratio test shows that the two-way random effects model is significantly better than the one-way random-effects model.

So far we built models that accounted for state-specific contextual information by introducing random effects for each state. This model can be extended to introduce random effects for STATE and REGION. In this example, STATE is nested within REGION. In other words, groups of states form a region and no state-level observation is part of more than one region. This can be done by including a random effect for REGION (9 levels) and a random effect for the interaction term STATE.*REGION (48 levels).

clc lme_hierarchical = fitlme(publicdata,... ['log_GDP ~ 1 + log_HWY + log_WATER + log_UTIL + log_PVT_CAP +'... 'log_EMP + UNEMP + (1|REGION) + (1|STATE:REGION) + (1|YR)'],'FitMethod','REML')

lme_hierarchical = Linear mixed-effects model fit by REML Model information: Number of observations 816 Fixed effects coefficients 7 Random effects coefficients 74 Covariance parameters 4 Formula: Linear Mixed Formula with 9 predictors. Model fit statistics: AIC BIC LogLikelihood Deviance -2878.2 -2826.5 1450.1 -2900.2 Fixed effects coefficients (95% CIs): Name Estimate SE tStat DF {'(Intercept)'} 2.3928 0.16409 14.582 809 {'UNEMP' } -0.0043974 0.0010682 -4.1168 809 {'log_HWY' } 0.089372 0.024872 3.5933 809 {'log_WATER' } 0.058102 0.013688 4.2446 809 {'log_UTIL' } -0.089943 0.016032 -5.6101 809 {'log_PVT_CAP'} 0.21538 0.022801 9.4461 809 {'log_EMP' } 0.77702 0.025223 30.806 809 pValue Lower Upper 6.2072e-43 2.0707 2.7149 4.2365e-05 -0.0064941 -0.0023007 0.00034626 0.040551 0.13819 2.4433e-05 0.031233 0.084971 2.7777e-08 -0.12141 -0.058473 3.6701e-20 0.17062 0.26013 1.7222e-138 0.72751 0.82653 Random effects covariance parameters (95% CIs): Group: REGION (9 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std'} 0.047011 Lower Upper 0.020556 0.10751 Group: STATE:REGION (48 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std'} 0.082574 Lower Upper 0.065183 0.10461 Group: YR (17 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std'} 0.016016 Lower Upper 0.010731 0.023903 Group: Error Name Estimate Lower Upper {'Res Std'} 0.033772 0.032093 0.035537

lme_advanced = fitlme(publicdata,... ['log_GDP ~ 1 + log_HWY + log_WATER + log_UTIL + log_PVT_CAP + log_EMP + UNEMP +'... '(1 + log_HWY + UNEMP|REGION) + (1 + log_HWY + UNEMP|STATE:REGION) + (1|YR)'],... 'FitMethod','REML','CovariancePattern',{'Full','Diagonal','Diagonal'}) compare(lme_hierarchical, lme_advanced,'CheckNesting',true)

lme_advanced = Linear mixed-effects model fit by REML Model information: Number of observations 816 Fixed effects coefficients 7 Random effects coefficients 188 Covariance parameters 11 Formula: Linear Mixed Formula with 9 predictors. Model fit statistics: AIC BIC LogLikelihood Deviance -2999.8 -2915.3 1517.9 -3035.8 Fixed effects coefficients (95% CIs): Name Estimate SE tStat DF {'(Intercept)'} 2.5426 0.42123 6.0362 809 {'UNEMP' } -0.0042169 0.0020824 -2.025 809 {'log_HWY' } 0.079243 0.048999 1.6172 809 {'log_WATER' } 0.035572 0.013372 2.6603 809 {'log_UTIL' } -0.05954 0.017 -3.5024 809 {'log_PVT_CAP'} 0.16467 0.022807 7.22 809 {'log_EMP' } 0.83055 0.026705 31.101 809 pValue Lower Upper 2.4023e-09 1.7158 3.3695 0.043197 -0.0083044 -0.00012929 0.10622 -0.016937 0.17542 0.0079629 0.0093247 0.061819 0.00048641 -0.092909 -0.026171 1.2e-12 0.1199 0.20944 2.6314e-140 0.77813 0.88297 Random effects covariance parameters (95% CIs): Group: REGION (9 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std' } 1.0807 {'UNEMP' } {'(Intercept)'} {'corr'} -0.07628 {'log_HWY' } {'(Intercept)'} {'corr'} -0.99583 {'UNEMP' } {'UNEMP' } {'std' } 0.0042097 {'log_HWY' } {'UNEMP' } {'corr'} -0.01499 {'log_HWY' } {'log_HWY' } {'std' } 0.1175 Lower Upper 0.6534 1.7874 -0.11649 -0.035823 -0.99589 -0.99577 0.0020541 0.0086273 -0.055524 0.025593 0.070978 0.1945 Group: STATE:REGION (48 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std'} 0.13535 {'UNEMP' } {'UNEMP' } {'std'} 0.0069407 {'log_HWY' } {'log_HWY' } {'std'} 9.4345e-06 Lower Upper 0.1034 0.17718 0.0051992 0.0092654 NaN NaN Group: YR (17 Levels) Name1 Name2 Type Estimate {'(Intercept)'} {'(Intercept)'} {'std'} 0.01954 Lower Upper 0.013218 0.028887 Group: Error Name Estimate Lower Upper {'Res Std'} 0.028184 0.026673 0.029781 ans = THEORETICAL LIKELIHOOD RATIO TEST Model DF AIC BIC LogLik LRStat deltaDF lme_hierarchical 11 -2878.2 -2826.5 1450.1 lme_advanced 18 -2999.8 -2915.3 1517.9 135.62 7 pValue 0

This example is based on the following literature:

[1] Alicia H. Munnell & Leah M. Cook, 1990. "How does public infrastructure affect regional economic performance?," New England Economic Review, Federal Reserve Bank of Boston, issue Sep, pages 11-33.

[2] Econometric Analysis of Panel Data, 5th Edition, Badi H. Baltagi

Cek juga

10 Aug- 2019, by Editor

What is PhD Journey and what is PhD chapter? Both of these elements is my private note on how I try to explore the social realm through data and research.

Check also

10 Aug- 2015, by Editor

The PhD Journey is my note. About my expedition in how to finish my dissertation. There is a day that it just ends up with frustration, but there is a day that it gave a great result and satisfaction! PhD is like a marathon, you just need to keep running and expect that the kilometres will be getting end soon.

Hi, my name is Dimas, I am a data enthusiast. At this moment I am writing several chapters related to Big Data, Macroprudential effect on the economy, and a couple of economic and IT research. If you are interested in collaborating, please write your mail to [email protected]

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