## Pooled Time Series Analysis, Issue 70Researchers have often been troubled with relevant data available from both temporal observations at regular intervals (time series) and from observations at single points of time (cross-sections). Pooled Time Series Analysis combines time series and cross-sectional data to provide the researcher with an efficient method of analysis and improved estimates of the population being studied. In addition, with more relevant data available this analysis technique allows the sample size to be increased, which ultimately yields a more effective study. |

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### Contents

Series Editors Introduction | 5 |

The Structural Equation Model 52 | 15 |

The Constant Coefficients Model | 19 |

The LSDV Model | 26 |

The Random Coefficient Model | 32 |

How Good Are These | 62 |

Conclusions on Pooled Time Series | 70 |

77 | |

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actor ARCH model ARMA assume assumption autoregressive process Bartlett test comparable Conflict on Executive constant coefficients model correlation covariance data set dependent variable derived diplomatic friendliness distribution dummy variable dummy variable model Durbin-Watson statistic esti example Executive Adjustments fects Figure 3.t Friendliness on Trade full pool GLS estimator GLS model Goldfeld Goldfeld-Quandt test heteroscedastic errors heteroscedasticity homoscedastic Hsaio included independent intercept international conflict lagged endogenous variable LOGIT LSDV estimates LSDV model matrix nonconstant variance nonlinear null hypothesis OLS and LSDV parameter partial autocorrelation pooled design pooled regression pooled time series Popularity on Prior Prior Approval PROBIT problem random coefficient model regression model Regression of Diplomatic residual variance sample Scatterplot Seemingly Unrelated Regression source of contamination specific stacking standard error stochastic structural equation model Swamy model switching model Table 3.t techniques theoretical tion toregression two-stage estimation unique unit effects unit of analysis vari variance-covariance matrix variation vector

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Page 77 - Methods. 3rd ed. New York: McGraw-Hill. Judge, GG, WE Griffiths, RC Hill, H. Lutkepohl, and TC. Lee. 1985. The Theory and Practice of Econometrics. New York: Wiley.

Page 77 - Combining cross-section data and time series." Cowles Commission Discussion Paper. Statistics No 347. HSAIO, C. (t975l "Some estimation methods for a random coefficient model.

Page 78 - A pooled cross-sectional analysis." Paper presented at the Third Annual Methodology Conference, Harvard University, Cambridge. MA, August 7-t0, t986. MUNDLAK, Y. (t978l "On the pooling of lime scries and cross-section data.